Econstudentlog

Epilepsy Diagnosis & Treatment – 5 New Things Every Physician Should Know

Links to related stuff:
i. Sudden unexpected death in epilepsy (SUDEP).
ii. Status epilepticus.
iii. Epilepsy surgery.
iv. Temporal lobe epilepsy.
v. Lesional epilepsy surgery.
vi. Nonlesional neocortical epilepsy.
vii. A Randomized, Controlled Trial of Surgery for Temporal-Lobe Epilepsy.
viii. Stereoelectroencephalography.
ix. Accuracy of intracranial electrode placement for stereoencephalography: A systematic review and meta-analysis. (The results of the review is not discussed in the lecture, for obvious reasons – lecture is a few years old, this review is brand new – but seemed relevant to me.)
x. MRI-guided laser ablation in epilepsy treatment.
xi. Laser thermal therapy: real-time MRI-guided and computer-controlled procedures for metastatic brain tumors.
xii. Critical review of the responsive neurostimulator system for epilepsy (Again, not covered but relevant).
xiii. A Multicenter, Prospective Pilot Study of Gamma Knife Radiosurgery for Mesial Temporal Lobe Epilepsy: Seizure Response, Adverse Events, and Verbal Memory.
xiv. Gamma Knife radiosurgery for recurrent or residual seizures after anterior temporal lobectomy in mesial temporal lobe epilepsy patients with hippocampal sclerosis: long-term follow-up results of more than 4 years (Not covered but relevant).

July 19, 2017 Posted by | Lectures, Medicine, Neurology, Studies | Leave a comment

Detecting Cosmic Neutrinos with IceCube at the Earth’s South Pole

I thought there were a bit too many questions/interruptions for my taste, mainly because you can’t really hear the questions posed by the members of the audience, but aside from that it’s a decent lecture. I’ve added a few links below which covers some of the topics discussed in the lecture.

Neutrino astronomy.
Antarctic Impulse Transient Antenna (ANITA).
Hydrophone.
Neutral pion decays.
IceCube Neutrino Observatory.
Evidence for High-Energy Extraterrestrial Neutrinos at the IceCube Detector (Science).
Atmospheric and astrophysical neutrinos above 1 TeV interacting in IceCube.
Notes on isotropy.
Measuring the flavor ratio of astrophysical neutrinos.
Blazar.
Supernova 1987A neutrino emissions.

July 18, 2017 Posted by | Astronomy, Lectures, Physics, Studies | Leave a comment

Beyond Significance Testing (III)

There are many ways to misinterpret significance tests, and this book spends quite a bit of time and effort on these kinds of issues. I decided to include in this post quite a few quotes from chapter 4 of the book, which deals with these topics in some detail. I also included some notes on effect sizes.

“[P] < .05 means that the likelihood of the data or results even more extreme given random sampling under the null hypothesis is < .05, assuming that all distributional requirements of the test statistic are satisfied and there are no other sources of error variance. […] the odds-against-chance fallacy […] [is] the false belief that p indicates the probability that a result happened by sampling error; thus, p < .05 says that there is less than a 5% likelihood that a particular finding is due to chance. There is a related misconception i call the filter myth, which says that p values sort results into two categories, those that are a result of “chance” (H0 not rejected) and others that are due to “real” effects (H0 rejected). These beliefs are wrong […] When p is calculated, it is already assumed that H0 is true, so the probability that sampling error is the only explanation is already taken to be 1.00. It is thus illogical to view p as measuring the likelihood of sampling error. […] There is no such thing as a statistical technique that determines the probability that various causal factors, including sampling error, acted on a particular result.

Most psychology students and professors may endorse the local Type I error fallacy [which is] the mistaken belief that p < .05 given α = .05 means that the likelihood that the decision just taken to reject H0 is a type I error is less than 5%. […] p values from statistical tests are conditional probabilities of data, so they do not apply to any specific decision to reject H0. This is because any particular decision to do so is either right or wrong, so no probability is associated with it (other than 0 or 1.0). Only with sufficient replication could one determine whether a decision to reject H0 in a particular study was correct. […] the valid research hypothesis fallacy […] refers to the false belief that the probability that H1 is true is > .95, given p < .05. The complement of p is a probability, but 1 – p is just the probability of getting a result even less extreme under H0 than the one actually found. This fallacy is endorsed by most psychology students and professors”.

“[S]everal different false conclusions may be reached after deciding to reject or fail to reject H0. […] the magnitude fallacy is the false belief that low p values indicate large effects. […] p values are confounded measures of effect size and sample size […]. Thus, effects of trivial magnitude need only a large enough sample to be statistically significant. […] the zero fallacy […] is the mistaken belief that the failure to reject a nil hypothesis means that the population effect size is zero. Maybe it is, but you cannot tell based on a result in one sample, especially if power is low. […] The equivalence fallacy occurs when the failure to reject H0: µ1 = µ2 is interpreted as saying that the populations are equivalent. This is wrong because even if µ1 = µ2, distributions can differ in other ways, such as variability or distribution shape.”

“[T]he reification fallacy is the faulty belief that failure to replicate a result is the failure to make the same decision about H0 across studies […]. In this view, a result is not considered replicated if H0 is rejected in the first study but not in the second study. This sophism ignores sample size, effect size, and power across different studies. […] The sanctification fallacy refers to dichotomous thinking about continuous p values. […] Differences between results that are “significant” versus “not significant” by close margins, such as p = .03 versus p = .07 when α = .05, are themselves often not statistically significant. That is, relatively large changes in p can correspond to small, nonsignificant changes in the underlying variable (Gelman & Stern, 2006). […] Classical parametric statistical tests are not robust against outliers or violations of distributional assumptions, especially in small, unrepresentative samples. But many researchers believe just the opposite, which is the robustness fallacy. […] most researchers do not provide evidence about whether distributional or other assumptions are met”.

“Many [of the above] fallacies involve wishful thinking about things that researchers really want to know. These include the probability that H0 or H1 is true, the likelihood of replication, and the chance that a particular decision to reject H0 is wrong. Alas, statistical tests tell us only the conditional probability of the data. […] But there is [however] a method that can tell us what we want to know. It is not a statistical technique; rather, it is good, old-fashioned replication, which is also the best way to deal with the problem of sampling error. […] Statistical significance provides even in the best case nothing more than low-level support for the existence of an effect, relation, or difference. That best case occurs when researchers estimate a priori power, specify the correct construct definitions and operationalizations, work with random or at least representative samples, analyze highly reliable scores in distributions that respect test assumptions, control other major sources of imprecision besides sampling error, and test plausible null hypotheses. In this idyllic scenario, p values from statistical tests may be reasonably accurate and potentially meaningful, if they are not misinterpreted. […] The capability of significance tests to address the dichotomous question of whether effects, relations, or differences are greater than expected levels of sampling error may be useful in some new research areas. Due to the many limitations of statistical tests, this period of usefulness should be brief. Given evidence that an effect exists, the next steps should involve estimation of its magnitude and evaluation of its substantive significance, both of which are beyond what significance testing can tell us. […] It should be a hallmark of a maturing research area that significance testing is not the primary inference method.”

“[An] effect size [is] a quantitative reflection of the magnitude of some phenomenon used for the sake of addressing a specific research question. In this sense, an effect size is a statistic (in samples) or parameter (in populations) with a purpose, that of quantifying a phenomenon of interest. more specific definitions may depend on study design. […] cause size refers to the independent variable and specifically to the amount of change in it that produces a given effect on the dependent variable. A related idea is that of causal efficacy, or the ratio of effect size to the size of its cause. The greater the causal efficacy, the more that a given change on an independent variable results in proportionally bigger changes on the dependent variable. The idea of cause size is most relevant when the factor is experimental and its levels are quantitative. […] An effect size measure […] is a named expression that maps data, statistics, or parameters onto a quantity that represents the magnitude of the phenomenon of interest. This expression connects dimensions or generalized units that are abstractions of variables of interest with a specific operationalization of those units.”

“A good effect size measure has the [following properties:] […] 1. Its scale (metric) should be appropriate for the research question. […] 2. It should be independent of sample size. […] 3. As a point estimate, an effect size should have good statistical properties; that is, it should be unbiased, consistent […], and efficient […]. 4. The effect size [should be] reported with a confidence interval. […] Not all effect size measures […] have all the properties just listed. But it is possible to report multiple effect sizes that address the same question in order to improve the communication of the results.” 

“Examples of outcomes with meaningful metrics include salaries in dollars and post-treatment survival time in years. means or contrasts for variables with meaningful units are unstandardized effect sizes that can be directly interpreted. […] In medical research, physical measurements with meaningful metrics are often available. […] But in psychological research there are typically no “natural” units for abstract, nonphysical constructs such as intelligence, scholastic achievement, or self-concept. […] Therefore, metrics in psychological research are often arbitrary instead of meaningful. An example is the total score for a set of true-false items. Because responses can be coded with any two different numbers, the total is arbitrary. Standard scores such as percentiles and normal deviates are arbitrary, too […] Standardized effect sizes can be computed for results expressed in arbitrary metrics. Such effect sizes can also be directly compared across studies where outcomes have different scales. this is because standardized effect sizes are based on units that have a common meaning regardless of the original metric.”

“1. It is better to report unstandardized effect sizes for outcomes with meaningful metrics. This is because the original scale is lost when results are standardized. 2. Unstandardized effect sizes are best for comparing results across different samples measured on the same outcomes. […] 3. Standardized effect sizes are better for comparing conceptually similar results based on different units of measure. […] 4. Standardized effect sizes are affected by the corresponding unstandardized effect sizes plus characteristics of the study, including its design […], whether factors are fixed or random, the extent of error variance, and sample base rates. This means that standardized effect sizes are less directly comparable over studies that differ in their designs or samples. […] 5. There is no such thing as T-shirt effect sizes (Lenth, 2006– 2009) that classify standardized effect sizes as “small,” “medium,” or “large” and apply over all research areas. This is because what is considered a large effect in one area may be seen as small or trivial in another. […] 6. There is usually no way to directly translate standardized effect sizes into implications for substantive significance. […] It is standardized effect sizes from sets of related studies that are analyzed in most meta analyses.”

July 16, 2017 Posted by | Books, Psychology, Statistics | Leave a comment

Quotes

i. “Mathematics is a tool which ideally permits mediocre minds to solve complicated problems expeditiously.” (Floyd Alburn Firestone)

ii. “Growing old’s like being increasingly penalized for a crime you haven’t committed.” (Anthony Dymoke Powell)

iii. “To make a discovery is not necessarily the same as to understand a discovery.” (Abraham Pais)

iv. “People usually take for granted that the way things are is the way things must be.” (Poul William Anderson)

v. ” Space isn’t remote at all. It’s only an hour’s drive away if your car could go straight upwards.” (Fred Hoyle)

vi. “One can never pay in gratitude; one can only pay “in kind” somewhere else in life.” (Anne Morrow Lindbergh)

vii. “When a nice quote comes to mind, I always attribute it to Montesquieu, or to La Rochefoucauld. They’ve never complained.” (Indro Montanelli)

viii. “Program testing can be a very effective way to show the presence of bugs, but it is hopelessly inadequate for showing their absence.” (Edsger Wybe Dijkstra)

ix. “History teaches us that men and nations behave wisely once they have exhausted all other alternatives.” (Abba Eban)

x. “Scientific research is not conducted in a social vacuum.” (Robert K. Merton)

xi. “No man knows fully what has shaped his own thinking” (-ll-)

xii. “I write as clearly as I am able to. I sometimes tackle ideas and notions that are relatively complex, and it is very difficult to be sure that I am conveying them in the best way. Anyone who goes beyond cliche phrases and cliche ideas will have this trouble.” (Raphael Aloysius Lafferty)

xiii. “Change should be a friend. It should happen by plan, not by accident.” (Philip B. Crosby)

xiv. “The universe of all things that exist may be understood as a universe of systems where a system is defined as any set of related and interacting elements. This concept is primitive and powerful and has been used increasingly over the last half-century to organize knowledge in virtually all domains of interest to investigators. As human inventions and social interactions grow more complex, general conceptual frameworks that integrate knowledge among different disciplines studying those emerging systems grow more important.” (Gale Alden Swanson & James Grier Miller, Living Systems Theory)

xv. “When I die it’s not me that will be affected. It’s the ones I leave behind.” (Cameron Troy Duncan)

xvi. “I was always deeply uncertain about my own intellectual capacity; I thought I was unintelligent. And it is true that I was, and still am, rather slow. I need time to seize things because I always need to understand them fully. […] At the end of the eleventh grade, I […] came to the conclusion that rapidity doesn’t have a precise relation to intelligence. What is important is to deeply understand things and their relations to each other. This is where intelligence lies. The fact of being quick or slow isn’t really relevant. Naturally, it’s helpful to be quick, like it is to have a good memory. But it’s neither necessary nor sufficient for intellectual success.” (Laurent-Moïse Schwartz)

xvii. “A slowly moving queue does not move uniformly. Rather, waves of motion pass down the queue. The frequency and amplitude of these waves is inversely related to the speed at which the queue is served.” (Anthony Stafford Beer)

xviii. “It is terribly important to appreciate that some things remain obscure to the bitter end.” (-ll-)

xix. “Definitions, like questions and metaphors, are instruments for thinking. Their authority rests entirely on their usefulness, not their correctness. We use definitions in order to delineate problems we wish to investigate, or to further interests we wish to promote. In other words, we invent definitions and discard them as suits our purposes. […] definitions are hypotheses, and […] embedded in them is a particular philosophical, sociological, or epistemological point of view.” (Neil Postman)

xx. “There’s no system foolproof enough to defeat a sufficiently great fool.” (Edward Teller)

July 15, 2017 Posted by | Quotes/aphorisms | Leave a comment

Gravity

“The purpose of this book is to give the reader a very brief introduction to various different aspects of gravity. We start by looking at the way in which the theory of gravity developed historically, before moving on to an outline of how it is understood by scientists today. We will then consider the consequences of gravitational physics on the Earth, in the Solar System, and in the Universe as a whole. The final chapter describes some of the frontiers of current research in theoretical gravitational physics.”

I was not super impressed by this book, mainly because the level of coverage was not quite as high as has been the level of coverage of some of the other physics books in the OUP – A Brief Introduction series. But it’s definitely an okay book about this topic, I was much closer to a three star rating on goodreads than a one star rating, and I did learn some new things from it. I might still change my mind about my two-star rating of the book.

I’ll cover the book the same way I’ve covered some of the other books in the series; I’ll post some quotes with some observations of interest, and then I’ll add some supplementary links towards the end of the post. ‘As usual’ (see e.g. also the introductory remarks to this post) I’ll add links to topics even if I have previously, perhaps on multiple occasions, added the same links when covering other books – the idea behind the links is to remind me – and indicate to you – which kinds of topics are covered in the book.

“[O]ver large distances it is gravity that dominates. This is because gravity is only ever attractive and because it can never be screened. So while most large objects are electrically neutral, they can never be gravitationally neutral. The gravitational force between objects with mass always acts to pull those objects together, and always increases as they become more massive.”

“The challenges involved in testing Newton’s law of gravity in the laboratory arise principally due to the weakness of the gravitational force compared to the other forces of nature. This weakness means that even the smallest residual electric charges on a piece of experimental equipment can totally overwhelm the gravitational force, making it impossible to measure. All experimental equipment therefore needs to be prepared with the greatest of care, and the inevitable electric charges that sneak through have to be screened by introducing metal shields that reduce their influence. This makes the construction of laboratory experiments to test gravity extremely difficult, and explains why we have so far only probed gravity down to scales a little below 1mm (this can be compared to around a billionth of a billionth of a millimetre for the electric force).”

“There are a large number of effects that result from Einstein’s theory. […] [T]he anomalous orbit of the planet Mercury; the bending of starlight around the Sun; the time delay of radio signals as they pass by the Sun; and the behaviour of gyroscopes in orbit around the Earth […] are four of the most prominent relativistic gravitational effects that can be observed in the Solar System.” [As an aside, I only yesterday watched the first ~20 minutes of the first of Nima Arkani-Hamed’s lectures on the topic of ‘Robustness of GR. Attempts to Modify Gravity’, which was recently uploaded on the IAS youtube channel, before I concluded that I was probably not going to be able to follow the lecture – I would have been able to tell Hamed, on account of having read this book, that the name of the ‘American’ astronomer whose name eluded him early on in the lecture (5 minutes in or so) was John Couch Adams (who was in fact British, not American)].

“[T]he overall picture we are left with is very encouraging for Einstein’s theory of gravity. The foundational assumptions of this theory, such as the constancy of mass and the Universality of Free Fall, have been tested to extremely high accuracy. The inverse square law that formed the basis of Newton’s theory, and which is a good first approximation to Einstein’s theory, has been tested from the sub-millimetre scale all the way up to astrophysical scales. […] We […] have very good evidence that Newton’s inverse square law is a good approximation to gravity over a wide range of distance scales. These scales range from a fraction of a millimetre, to hundreds of millions of metres. […] We are also now in possession of a number of accurate experimental results that probe the tiny, subtle effects that result from Einstein’s theory specifically. This data allows us direct experimental insight into the relationship between matter and the curvature of space-time, and all of it is so far in good agreement with Einstein’s predictions.”

“[A]ll of the objects in the Solar System are, relatively speaking, rather slow moving and not very dense. […] If we set our sights a little further though, we can find objects that are much more extreme than anything we have available nearby. […] observations of them have allowed us to explore gravity in ways that are simply impossible in our own Solar System. The extreme nature of these objects amplifies the effects of Einstein’s theory […] Just as the orbit of Mercury precesses around the Sun so too the neutron stars in the Hulse–Taylor binary system precess around each other. To compare with similar effects in our Solar System, the orbit of the Hulse–Taylor pulsar precesses as much in a day as Mercury does in a century.”

“[I]n Einstein’s theory, gravity is due to the curvature of space-time. Massive objects like stars and planets deform the shape of the space-time in which they exist, so that other bodies that move through it appear to have their trajectories bent. It is the mistaken interpretation of the motion of these bodies as occurring in a flat space that leads us to infer that there is a force called gravity. In fact, it is just the curvature of space-time that is at work. […] The relevance of this for gravitational waves is that if a group of massive bodies are in relative motion […], then the curvature of the space-time in which they exist is not usually fixed in time. The curvature of the space-time is set by the massive bodies, so if the bodies are in motion, the curvature of space-time should be expected to be constantly changing. […] in Einstein’s theory, space-time is a dynamical entity. As an example of this, consider the supernovae […] Before their cores collapse, leading to catastrophic explosion, they are relatively stable objects […] After they explode they settle down to a neutron star or a black hole, and once again return to a relatively stable state, with a gravitational field that doesn’t change much with time. During the explosion, however, they eject huge amounts of mass and energy. Their gravitational field changes rapidly throughout this process, and therefore so does the curvature of the space-time around them.

Like any system that is pushed out of equilibrium and made to change rapidly, this causes disturbances in the form of waves. A more down-to-earth example of a wave is what happens when you throw a stone into a previously still pond. The water in the pond was initially in a steady state, but the stone causes a rapid change in the amount of water at one point. The water in the pond tries to return to its tranquil initial state, which results in the propagation of the disturbance, in the form of ripples that move away from the point where the stone landed. Likewise, a loud noise in a previously quiet room originates from a change in air pressure at a point (e.g. a stereo speaker). The disturbance in the air pressure propagates outwards as a pressure wave as the air tries to return to a stable state, and we perceive these pressure waves as sound. So it is with gravity. If the curvature of space-time is pushed out of equilibrium, by the motion of mass or energy, then this disturbance travels outwards as waves. This is exactly what occurs when a star collapses and its outer envelope is ejected by the subsequent explosion. […] The speed with which waves propagate usually depends on the medium through which they travel. […] The medium for gravitational waves is space-time itself, and according to Einstein’s theory, they propagate at exactly the same speed as light. […] [If a gravitational wave passes through a cloud of gas,] the gravitational wave is not a wave in the gas, but rather a propagating disturbance in the space-time in which the gas exists. […] although the atoms in the gas might be closer together (or further apart) than they were before the wave passed through them, it is not because the atoms have moved, but because the amount of space between them has been decreased (or increased) by the wave. The gravitational wave changes the distance between objects by altering how much space there is in between them, not by moving them within a fixed space.”

“If we look at the right galaxies, or collect enough data, […] we can use it to determine the gravitational fields that exist in space. […] we find that there is more gravity than we expected there to be, from the astrophysical bodies that we can see directly. There appears to be a lot of mass, which bends light via its gravitational field, but that does not interact with the light in any other way. […] Moving to even smaller scales, we can look at how individual galaxies behave. It has been known since the 1970s that the rate at which galaxies rotate is too high. What I mean is that if the only source of gravity in a galaxy was the visible matter within it (mostly stars and gas), then any galaxy that rotated as fast as those we see around us would tear itself apart. […] That they do not fly apart, despite their rapid rotation, strongly suggests that the gravitational fields within them are larger than we initially suspected. Again, the logical conclusion is that there appears to be matter in galaxies that we cannot see but which contributes to the gravitational field. […] Many of the different physical processes that occur in the Universe lead to the same surprising conclusion: the gravitational fields we infer, by looking at the Universe around us, require there to be more matter than we can see with our telescopes. Beyond this, in order for the largest structures in the Universe to have evolved into their current state, and in order for the seeds of these structures to look the way they do in the CMB, this new matter cannot be allowed to interact with light at all (or, at most, interact only very weakly). This means that not only do we not see this matter, but that it cannot be seen at all using light, because light is required to pass straight through it. […] The substance that gravitates in this way but cannot be seen is referred to as dark matter. […] There needs to be approximately five times as much dark matter as there is ordinary matter. […] the evidence for the existence of dark matter comes from so many different sources that it is hard to argue with it.”

“[T]here seems to be a type of anti-gravity at work when we look at how the Universe expands. This anti-gravity is required in order to force matter apart, rather than pull it together, so that the expansion of the Universe can accelerate. […] The source of this repulsive gravity is referred to by scientists as dark energy […] our current overall picture of the Universe is as follows: only around 5 per cent of the energy in the Universe is in the form of normal matter; about 25 per cent is thought to be in the form of the gravitationally attractive dark matter; and the remaining 70 per cent is thought to be in the form of the gravitationally repulsive dark energy. These proportions, give or take a few percentage points here and there, seem sufficient to explain all astronomical observations that have been made to date. The total of all three of these types of energy, added together, also seems to be just the right amount to make space flat […] The flat Universe, filled with mostly dark energy and dark matter, is usually referred to as the Concordance Model of the Universe. Among astronomers, it is now the consensus view that this is the model of the Universe that best fits their data.”

 

The universality of free fall.
Galileo’s Leaning Tower of Pisa experiment.
Isaac Newton/Philosophiæ Naturalis Principia Mathematica/Newton’s law of universal gravitation.
Kepler’s laws of planetary motion.
Luminiferous aether.
Special relativity.
Spacetime.
General relativity.
Spacetime curvature.
Pound–Rebka experiment.
Gravitational time dilation.
Gravitational redshift space-probe experiment (Essot & Levine).
Michelson–Morley experiment.
Hughes–Drever experiment.
Tests of special relativity.
Eötvös experiment.
Torsion balance.
Cavendish experiment.
LAGEOS.
Interferometry.
Geodetic precession.
Frame-dragging.
Gravity Probe B.
White dwarf/neutron star/supernova/gravitational collapse/black hole.
Hulse–Taylor binary.
Arecibo Observatory.
PSR J1738+0333.
Gravitational wave.
Square Kilometre Array.
PSR J0337+1715.
LIGO.
Weber bar.
MiniGrail.
Laser Interferometer Space Antenna.
Edwin Hubble/Hubble’s Law.
Physical cosmology.
Alexander Friedmann/Friedmann equations.
Cosmological constant.
Georges Lemaître.
Ralph Asher Alpher/Robert Hermann/CMB/Arno Penzias/Robert Wilson.
Cosmic Background Explorer.
The BOOMERanG experiment.
Millimeter Anisotropy eXperiment IMaging Array.
Wilkinson Microwave Anisotropy Probe.
High-Z Supernova Search Team.
CfA Redshift Survey/CfA2 Great Wall/2dF Galaxy Redshift Survey/Sloan Digital Sky Survey/Sloan Great Wall.
Gravitational lensing.
Inflation (cosmology).
Lambda-CDM model.
BICEP2.
Large Synoptic Survey Telescope.
Grand Unified Theory.
Renormalization (quantum theory).
String theory.
Loop quantum gravity.
Unruh effect.
Hawking radiation.
Anthropic principle.

July 15, 2017 Posted by | Astronomy, Books, cosmology, Physics | Leave a comment

The Personality Puzzle (IV)

Below I have added a few quotes from the last 100 pages of the book. This will be my last post about the book.

“Carol Dweck and her colleagues claim that two […] kinds of goals are […] important […]. One kind she calls judgment goals. Judgment, in this context, refers to seeking to judge or validate an attribute in oneself. For example, you might have the goal of convincing yourself that you are smart, beautiful, or popular. The other kind she calls development goals. A development goal is the desire to actually improve oneself, to become smarter, more beautiful, or more popular. […] From the perspective of Dweck’s theory, these two kinds of goals are important in many areas of life because they produce different reactions to failure, and everybody fails sometimes. A person with a development goal will respond to failure with what Dweck calls a mastery-oriented pattern, in which she tries even harder the next time. […] In contrast, a person with a judgment goal responds to failure with what Dweck calls the helpless pattern: Rather than try harder, this individual simply concludes, “I can’t do it,” and gives up. Of course, that only guarantees more failure in the future. […] Dweck believes [the goals] originate in different kinds of implicit theories about the nature of the world […] Some people hold what Dweck calls entity theories, and believe that personal qualities such as intelligence and ability are unchangeable, leading them to respond helplessly to any indication that they do not have what it takes. Other people hold incremental theories, believing that intelligence and ability can change with time and experience. Their goals, therefore, involve not only proving their competence but increasing it.”

(I should probably add here that any sort of empirical validation of those theories and their consequences are, aside from a brief discussion of the results of a few (likely weak, low-powered) studies, completely absent in the book, but this kind of stuff might even so be worth having in mind, which was why I included this quote in my coverage – US).

“A large amount of research suggests that low self-esteem […] is correlated with outcomes such as dissatisfaction with life, hopelessness, and depression […] as well as loneliness […] Declines in self-esteem also appear to cause outcomes including depression, lower satisfaction with relationships, and lower satisfaction with one’s career […] Your self-esteem tends to suffer when you have failed in the eyes of your social group […] This drop in self-esteem may be a warning about possible rejection or even social ostracism — which, for our distant ancestors, could literally be fatal — and motivate you to restore your reputation. High self-esteem, by contrast, may indicate success and acceptance. Attempts to bolster self-esteem can backfire. […] People who self-enhance — who think they are better than the other people who know them think they are — can run into problems in relations with others, mental health, and adjustment […] Narcissism is associated with high self-esteem that is brittle and unstable because it is unrealistic […], and unstable self-esteem may be worse than low self-esteem […] The bottom line is that promoting psychological health requires something more complex than simply trying to make everybody feel better about themselves […]. The best way to raise self-esteem is through accomplishments that increase it legitimately […]. The most important aspect of your opinion of yourself is not whether it is good or bad, but the degree to which it is accurate.”

“An old theory suggested that if you repeated something over and over in your mind, such rehearsal was sufficient to move the information into long-term memory (LTM), or permanent memory storage. Later research showed that this idea is not quite correct. The best way to get information into LTM, it turns out, is not just to repeat it, but to really think about it (a process called elaboration). The longer and more complex the processing that a piece of information receives, the more likely it is to get transferred into LTM”.

“Concerning mental health, aspects of personality can become so extreme as to cause serious problems. When this happens, psychologists begin to speak of personality disorders […] Personality disorders have five general characteristics. They are (1) unusual and, (2) by definition, tend to cause problems. In addition, most but not quite all personality disorders (3) affect social relations and (4) are stable over time. Finally, (5) in some cases, the person who has a personality disorder may see it not as a disorder at all, but a basic part of who he or she is. […] personality disorders can be ego-syntonic, which means the people who have them do not think anything is wrong. People who suffer from other kinds of mental disorder generally experience their symptoms of confusion, depression, or anxiety as ego-dystonic afflictions of which they would like to be cured. For a surprising number of people with personality disorders, in contrast, their symptoms feel like normal and even valued aspects of who they are. Individuals with the attributes of the antisocial or narcissistic personality disorders, in particular, typically do not think they have a problem.”

[One side-note: It’s important to be aware of the fact that not all people who display unusual behavioral patterns which are causing them problems necessarily suffer from a personality disorder. Other categorization schemes also exist. Autism is for example not categorized as a personality disorder, but is rather considered to be a (neuro)developmental disorder. Funder does not go into this kind of stuff in his book but I thought it might be worth mentioning here – US]

“Some people are more honest than others, but when deceit and manipulation become core aspects of an individual’s way of dealing with the world, he may be diagnosed with antisocial personality disorder. […] People with this disorder are impulsive, and engage in risky behaviors […] They typically are irritable, aggressive, and irresponsible. The damage they do to others bothers them not one whit; they rationalize […] that life is unfair; the world is full of suckers; and if you don’t take what you want whenever you can, then you are a sucker too. […] A wide variety of negative outcomes may accompany this disorder […] Antisocial personality disorder is sometimes confused with the trait of psychopathy […] but it’s importantly different […] Psychopaths are emotionally cold, they disregard social norms, and they are manipulative and often cunning. Most psychopaths meet the criteria for antisocial personality disorder, but the reverse is not true.”

“From day to day with different people, and over time with the same people, most individuals feel and act pretty consistently. […] Predictability makes it possible to deal with others in a reasonable way, and gives each of us a sense of individual identity. But some people are less consistent than others […] borderline personality disorder […] is characterized by unstable and confused behavior, a poor sense of identity, and patterns of self-harm […] Their chaotic thoughts, emotions, and behaviors make persons suffering from this disorder very difficult for others to “read” […] Borderline personality disorder (BPD) entails so many problems for the affected person that nobody doubts that it is, at the very least, on the “borderline” with severe psychopathology.5 Its hallmark is emotional instability. […] All of the personality disorders are rather mixed bags of indicators, and BPD may be the most mixed of all. It is difficult to find a coherent, common thread among its characteristics […] Some psychologists […] have suggested that this [personality disorder] category is too diffuse and should be abandoned.”

“[T]he modern research literature on personality disorders has come close to consensus about one conclusion: There is no sharp dividing line between psychopathology and normal variation (L. A. Clark & Watson, 1999a; Furr & Funder, 1998; Hong & Paunonen, 2011; Krueger & Eaton, 2010; Krueger & Tackett, 2003; B. P. O’Connor, 2002; Trull & Durrett, 2005).”

“Accurate self-knowledge has long been considered a hallmark of mental health […] The process for gaining accurate self-knowledge is outlined by the Realistic Accuracy Model […] according to RAM, one can gain accurate knowledge of anyone’s personality through a four-stage process. First, the person must do something relevant to the trait being judged; second, the information must be available to the judge; third, the judge must detect this information; and fourth, the judge must utilize the information correctly. This model was initially developed to explain the accuracy of judgments of other people. In an important sense, though, you are just one of the people you happen to know, and, to some degree, you come to know yourself the same way you find out about anybody else — by observing what you do and trying to draw appropriate conclusions”.

“[P]ersonality is not just something you have; it is also something you do. The unique aspects of what you do comprise the procedural self, and your knowledge of this self typically takes the form of procedural knowledge. […] The procedural self is made up of the behaviors through which you express who you think you are, generally without knowing you are doing so […]. Like riding a bicycle, the working of the procedural self is automatic and not very accessible to conscious awareness.”

July 14, 2017 Posted by | Books, Psychology | Leave a comment

Probing the Early Universe through Observations of the Cosmic Microwave Background

This lecture/talk is a few years old, but it was only made public on the IAS channel last week (…along with a lot of other lectures – the IAS channel has added a lot of stuff recently, including more than 150 lectures within the last week or so; so if you’re interested you should go have a look).

Below the lecture I have added a few links with stuff (wiki-articles and a few papers) related to the topics covered in the lecture. I didn’t read those links, but I skimmed them (and a few others, which I subsequently decided not to include as their coverage did not overlap sufficiently with the stuff covered in the lecture) and decided to add them in order to remind myself what kind of stuff was included in the lecture/allow others to infer what kind of stuff might be included in the lecture. The links naturally go into a lot more detail than does the lecture, but these are the sort of topics discussed/included.

The lecture is long (90 minutes + a short Q&A), but it was interesting enough for me to watch all of it. The lecturer displays a very high level of speech disfluency throughout the lecture, in the sense that I might not be surprised if I were told that the most commonly word encountered during this lecture was ‘um’ or ‘uh’, rather than more commonly encountered mode words like ‘the’, but you get used to it (at least I managed to sort of ‘tune it out’ after a while). I should caution that there’s a short ‘jump’ very early on in the lecture (at the 2 minute mark or so) where a small amount of frames were apparently dropped, but that should not scare you away from watching the lecture; that frame drop is the only one of its kind during the lecture, aside from a similar brief ‘jump’ around the 1 hour 9 minute mark.

Some links:

Astronomical interferometer.
Polarimetry.
Bolometer.
Fourier transform.
Boomerang : A Balloon-borne Millimeter Wave Telescope and Total Power Receiver for Mapping Anisotropy in the Cosmic Microwave Background.
Observations of the Temperature and Polarization Anisotropies with Boomerang 2003.
THE COBE DIFFUSE INFRARED BACKGROUND EXPERIMENT SEARCH FOR THE COSMIC INFRARED BACKGROUND: I. LIMITS AND DETECTIONS.
Detection of the Power Spectrum of Cosmic Microwave Background Lensing by the Atacama Cosmology Telescope.
Secondary anisotropies of the CMB (review article).
Planck early results. VIII. The all-sky early Sunyaev-Zeldovich cluster sample.
Sunyaev–Zel’dovich effect.
A CMB Polarization Primer.
MEASUREMENT OF COSMIC MICROWAVE BACKGROUND POLARIZATION POWER SPECTRA FROM TWO YEARS OF BICEP DATA.
Spider: a balloon-borne CMB polarimeter for large angular scales.

July 13, 2017 Posted by | Astronomy, cosmology, Lectures, Physics | Leave a comment

A few diabetes papers of interest

i. Long-Acting C-Peptide and Neuropathy in Type 1 Diabetes: A 12-Month Clinical Trial.

“Lack of C-peptide in type 1 diabetes may be an important contributing factor in the development of microvascular complications. Replacement of native C-peptide has been shown to exert a beneficial influence on peripheral nerve function in type 1 diabetes. The aim of this study was to evaluate the efficacy and safety of a long-acting C-peptide in subjects with type 1 diabetes and mild to moderate peripheral neuropathy. […] C-peptide, an integral component of the insulin biosynthesis, is the 31-amino acid peptide that makes up the connecting segment between the parts of the proinsulin molecule that become the A and B chains of insulin. It is split off from proinsulin and secreted together with insulin in equimolar amounts. Much new information on C-peptide physiology has appeared during the past 20 years […] Studies in animal models of diabetes and early clinical trials in patients with type 1 diabetes (T1DM) demonstrate that C-peptide in physiological replacement doses elicits beneficial effects on early stages of diabetes-induced functional and structural abnormalities of the peripheral nerves, the autonomic nervous system, and the kidneys (9). Even though much is still to be learned about C-peptide and its mechanism of action, the available evidence presents the picture of a bioactive peptide with therapeutic potential.”

“This was a multicenter, phase 2b, randomized, double-blind, placebo-controlled, parallel-group study. The study screened 756 subjects and enrolled 250 at 32 clinical sites in the U.S. (n = 23), Canada (n = 2), and Sweden (n = 7). […] A total of 250 patients with type 1 diabetes and peripheral neuropathy received long-acting (pegylated) C-peptide in weekly dosages […] for 52 weeks. […] Once-weekly subcutaneous administration of long-acting C-peptide for 52 weeks did not improve SNCV [sural nerve conduction velocity], other electrophysiological variables, or mTCNS [modified Toronto Clinical Neuropathy Score] but resulted in marked improvement of VPT [vibration perception threshold] compared with placebo. […] During the course of the 12-month study period, there were no significant changes in fasting blood glucose. Levels of HbA1c remained stable and varied within the treatment groups on average less than 0.1% (0.9 mmol/mol) between baseline and 52 weeks. […] There was a gradual lowering of VPT, indicating improvement in subjects receiving PEG–C-peptide […] after 52 weeks, subjects in the low-dose group had lowered their VPT by an average of 31% compared with baseline; the corresponding value for the high-dose group was 19%. […] The difference in VPT response between the dose groups did not attain statistical significance. In contrast to the SNCV results, VPT in the placebo group changed very little from baseline during the study […] The mTCNS, pain, and sexual function scores did not change significantly during the study nor did subgroup analysis involving the subjects most affected at baseline reveal significant differences between subjects treated with PEG–C-peptide or placebo subjects.”

“Evaluation of the safety population showed that PEG–C-peptide was well tolerated and that there was a low and similar incidence of treatment-related adverse events (11.3–16.4%) in all three treatment groups […] A striking finding in the current study is the observation of a progressive improvement in VPT during the 12-month treatment with PEG–C-peptide […], despite nonsignificant changes in SNCV. This finding may reflect differences in the mechanisms of conduction versus transduction of neural impulses. Changes in transduction reflect membrane receptor characteristics limited to the distal extreme of specific subtypes of sensory axons. In the case of vibration, the principal receptor is Pacinian corpuscles in the skin that are innervated by Aβ fibers. Transduction takes place uniquely at the distal extreme of the axon and is largely influenced by the integrity of this limited segment. Studies have documented that the initial effect of toxic neuropathy is a loss of the surface area of the pseudopod extensions of the distal axon within the Pacinian corpuscle and a consequent diminution of transduction (30). In contrast, changes in the speed of conduction are largely a function of factors that influence the elongated tract of the nerve, including the cross-sectional diameter of axons, the degree of myelination, and the integrity of ion clusters at the nodes of Ranvier (31). Thus, it is reasonable that some aspects of distal sensory function may be influenced by a treatment option that has little or no direct effect on nerve conduction velocity. The alternative is the unsupported belief that any intervention in the onset and progression of a sensory neuropathy must alter conduction velocity.

The marked VPT improvement observed in the current study, although associated with nonsignificant changes in SNCV, other electrophysiological variables, or mTCNS, can be interpreted as targeted improvement in a key aspect of sensory function (e.g., the conversion of mechanical energy to neural signals — transduction). […] Because progressive deficits in sensation are often considered the hallmark of diabetic polyneuropathy, the observed effects of C-peptide in the current study are an important finding.”

ii. Hyperbaric Oxygen Therapy Does Not Reduce Indications for Amputation in Patients With Diabetes With Nonhealing Ulcers of the Lower Limb: A Prospective, Double-Blind, Randomized Controlled Clinical Trial.

“Hyperbaric oxygen therapy (HBOT) is used for the treatment of chronic diabetic foot ulcers (DFUs). The controlled evidence for the efficacy of this treatment is limited. The goal of this study was to assess the efficacy of HBOT in reducing the need for major amputation and improving wound healing in patients with diabetes and chronic DFUs.”

“Patients with diabetes and foot lesions (Wagner grade 2–4) of at least 4 weeks’ duration participated in this study. In addition to comprehensive wound care, participants were randomly assigned to receive 30 daily sessions of 90 min of HBOT (breathing oxygen at 244 kPa) or sham (breathing air at 125 kPa). Patients, physicians, and researchers were blinded to group assignment. At 12 weeks postrandomization, the primary outcome was freedom from meeting the criteria for amputation as assessed by a vascular surgeon. Secondary outcomes were measures of wound healing. […] One hundred fifty-seven patients were assessed for eligibility, with 107 randomly assigned and 103 available for end point adjudication. Criteria for major amputation were met in 13 of 54 patients in the sham group and 11 of 49 in the HBOT group (odds ratio 0.91 [95% CI 0.37, 2.28], P = 0.846). Twelve (22%) patients in the sham group and 10 (20%) in the HBOT group were healed (0.90 [0.35, 2.31], P = 0.823).”

CONCLUSIONS HBOT does not offer an additional advantage to comprehensive wound care in reducing the indication for amputation or facilitating wound healing in patients with chronic DFUs.”

iii. Risk Factors Associated With Severe Hypoglycemia in Older Adults With Type 1 Diabetes.

“Older adults with type 1 diabetes (T1D) are a growing but underevaluated population (14). Of particular concern in this age group is severe hypoglycemia, which, in addition to producing altered mental status and sometimes seizures or loss of consciousness, can be associated with cardiac arrhythmias, falls leading to fractures, and in some cases, death (57). In Medicare beneficiaries with diabetes, hospitalizations related to hypoglycemia are now more frequent than those for hyperglycemia and are associated with high 1-year mortality (6). Emergency department visits due to hypoglycemia also are common (5). […] The T1D Exchange clinic registry reported a remarkably high frequency of severe hypoglycemia resulting in seizure or loss of consciousness in older adults with long-standing T1D (9). One or more such events during the prior year was reported by 1 in 5 of 211 participants ≥65 years of age with ≥40 years’ duration of diabetes (9).”

“Despite the high frequency of severe hypoglycemia in older adults with long-standing T1D, little information is available about the factors associated with its occurrence. We conducted a case-control study in adults ≥60 years of age with T1D of ≥20 years’ duration to assess potential contributory factors for the occurrence of severe hypoglycemia, including cognitive and functional measurements, social support, depression, hypoglycemia unawareness, various aspects of diabetes management, residual insulin secretion (as measured by C-peptide levels), frequency of biochemical hypoglycemia, and glycemic control and variability. […] A case-control study was conducted at 18 diabetes centers in the T1D Exchange Clinic Network. […] Case subjects (n = 101) had at least one severe hypoglycemic event in the prior 12 months. Control subjects (n = 100), frequency-matched to case subjects by age, had no severe hypoglycemia in the prior 3 years.”

RESULTS Glycated hemoglobin (mean 7.8% vs. 7.7%) and CGM-measured mean glucose (175 vs. 175 mg/dL) were similar between case and control subjects. More case than control subjects had hypoglycemia unawareness: only 11% of case subjects compared with 43% of control subjects reported always having symptoms associated with low blood glucose levels (P < 0.001). Case subjects had greater glucose variability than control subjects (P = 0.008) and experienced CGM glucose levels <60 mg/dL for ≥20 min on 46% of days compared with 33% of days in control subjects (P = 0.10). […] When defining high glucose variability as a coefficient of variation greater than the study cohort’s 75th percentile (0.481), 38% of case and 12% of control subjects had high glucose variability (P < 0.001).”

CONCLUSIONS In older adults with long-standing type 1 diabetes, greater hypoglycemia unawareness and glucose variability are associated with an increased risk of severe hypoglycemia.”

iv. Type 1 Diabetes and Polycystic Ovary Syndrome: Systematic Review and Meta-analysis.

“Even though PCOS is mainly an androgen excess disorder, insulin resistance and compensatory endogenous hyperinsulinemia, in close association with obesity and abdominal adiposity, are implicated in the pathogenesis of PCOS in many patients (3,4). In agreement, women with PCOS are at high risk for developing type 2 diabetes and gestational diabetes mellitus (3). […] Type 1 diabetes is a disease produced by an autoimmune injury to the endocrine pancreas that results in the abolition of endogenous insulin secretion. We hypothesized 15 years ago that PCOS could be associated with type 1 diabetes (8). The rationale was that women with type 1 diabetes needed supraphysiological doses of subcutaneous insulin to reach insulin concentrations at the portal level capable of suppressing hepatic glucose secretion, thus leading to exogenous systemic hyperinsulinism. Exogenous hyperinsulinism could then contribute to androgen excess in predisposed women, leading to PCOS as happens in insulin-resistance syndromes.

We subsequently published the first report of the association of PCOS with type 1 diabetes consisting of the finding of a threefold increase in the prevalence of this syndrome compared with that of women from the general population […]. Of note, even though this association was confirmed by all of the studies that addressed the issue thereafter (1016), with prevalences of PCOS as high as 40% in some series (10,16), this syndrome is seldom diagnosed and treated in women with type 1 diabetes.

With the aim of increasing awareness of the frequent association of PCOS with type 1 diabetes, we have conducted a systematic review and meta-analysis of the prevalence of PCOS and associated hyperandrogenic traits in adolescent and adult women with type 1 diabetes. […] Nine primary studies involving 475 adolescent or adult women with type 1 diabetes were included. The prevalences of PCOS and associated traits in women with type 1 diabetes were 24% (95% CI 15–34) for PCOS, 25% (95% CI 17–33) for hyperandrogenemia, 25% (95% CI 16–36) for hirsutism, 24% (95% CI 17–32) for menstrual dysfunction, and 33% (95% CI 24–44) for PCOM. These figures are considerably higher than those reported earlier in the general population without diabetes.”

CONCLUSIONS PCOS and its related traits are frequent findings in women with type 1 diabetes. PCOS may contribute to the subfertility of these women by a mechanism that does not directly depend on glycemic/metabolic control among other negative consequences for their health. Hence, screening for PCOS and androgen excess should be included in current guidelines for the management of type 1 diabetes in women.”

v. Impaired Awareness of Hypoglycemia in Adults With Type 1 Diabetes Is Not Associated With Autonomic Dysfunction or Peripheral Neuropathy.

“Impaired awareness of hypoglycemia (IAH), defined as a diminished ability to perceive the onset of hypoglycemia, is associated with an increased risk of severe hypoglycemia in people with insulin-treated diabetes (13). Elucidation of the pathogenesis of IAH may help to minimize the risk of severe hypoglycemia.

The glycemic thresholds for counterregulatory responses, generation of symptoms, and cognitive impairment are reset at lower levels of blood glucose in people who have developed IAH (4). This cerebral adaptation appears to be induced by recurrent exposure to hypoglycemia, and failure of cerebral autonomic mechanisms may be implicated in the pathogenesis (4). Awareness may be improved by avoidance of hypoglycemia (57), but this is very difficult to achieve and does not restore normal awareness of hypoglycemia (NAH) in all people with IAH. Because the prevalence of IAH in adults with type 1 diabetes increases with progressive disease duration (2,8,9), mechanisms that involve diabetic complications have been suggested to underlie the development of IAH.

Because activation of the autonomic nervous system is a fundamental physiological response to hypoglycemia and provokes many of the symptoms of hypoglycemia, autonomic neuropathy was considered to be a cause of IAH for many years (10). […] Studies of people with type 1 diabetes that have examined the glycemic thresholds for symptom generation in those with and without autonomic neuropathy (13,14,16) have [however] found no differences, and autonomic symptom generation was not delayed. […] The aim of the current study was […] to evaluate a putative association between IAH and the presence of autonomic neuropathy using composite Z (cZ) scores based on a battery of contemporary methods, including heart rate variability during paced breathing, the cardiovascular response to tilting and the Valsalva maneuver, and quantitative light reflex measurements by pupillometry.”

“Sixty-six adults with type 1 diabetes were studied, 33 with IAH and 33 with normal awareness of hypoglycemia (NAH), confirmed by formal testing. Participants were matched for age, sex, and diabetes duration. […] The [study showed] no difference in measures of autonomic function between adults with long-standing type 1 diabetes who had IAH, and carefully matched adults with type 1 diabetes with NAH. In addition, no differences between IAH and NAH participants were found with respect to the NCS [nerve conduction studies], thermal thresholds, and clinical pain or neuropathy scores. Neither autonomic dysfunction nor somatic neuropathy was associated with IAH. We consider that this study provides considerable value and novelty in view of the rigorous methodology that has been used. Potential confounding variables have been controlled for by the use of well-matched groups of participants, validated methods for classification of awareness, a large battery of neurophysiological tests, and a novel statistical approach to provide very high sensitivity for the detection of between-group differences.”

vi. Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes.

“Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. […] Our primary message is that diabetes control is all about optimization and balance between two key markers — frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization […] Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components — amplitude and timing.”

“Although reducing hyperglycemia and targeting HbA1c values of 7% or less result in decreased risk of micro- and macrovascular complications (14), the risk for hypoglycemia increases with tightening glycemic control (5,6). […] Thus, patients with diabetes face a lifelong optimization problem: reducing average glycemic levels and postprandial hyperglycemia while simultaneously avoiding hypoglycemia. A strategy for achieving such an optimization can only be successful if it reduces glucose variability (GV). This is because bringing average glycemia down is only possible if GV is constrained — otherwise blood glucose (BG) fluctuations would inevitably enter the range of hypoglycemia (9).”

“In health, glucose metabolism is tightly controlled by a hormonal network including the gut, liver, pancreas, and brain to ensure stable fasting BG levels and transient postprandial glucose fluctuations. In other words, BG fluctuations in type 1 diabetes result from the activity of a complex metabolic system perturbed by behavioral challenges. The frequency and extent of these challenges and the ability of the person’s system to absorb them determine the stability of glycemic control. The degree of system destabilization depends on each individual’s physiological parameters of glucose–insulin kinetics, including glucose appearance from food, insulin secretion, insulin sensitivity, and counterregulatory response.”

“There is strong evidence that feeding behavior is abnormal in both uncontrolled diabetes and hypoglycemia and that feeding signals within the brain and hormones affecting feeding, such as leptin and ghrelin, are implicated in diabetes (1214). Insulin secretion and action vary with the type and duration of diabetes. In type 1 diabetes, insulin secretion is virtually absent, which destroys the natural insulin–glucagon feedback loop and thereby diminishes the dampening effect of glucagon on hypoglycemia. In addition, insulin is typically administered subcutaneously, which adds delays to insulin action and thereby amplifies the amplitude of glucose fluctuations. […] impaired hypoglycemia counterregulation and increased GV in the hypoglycemic range are particularly relevant to type 1 diabetes: It has been shown that glucagon response is impaired (15), and epinephrine response is typically attenuated as well (16). Antecedent hypoglycemia shifts down BG thresholds for autonomic and cognitive responses, thereby further impairing both the hormonal defenses and the detection of hypoglycemia (17). Studies have established relationships between intensive therapy, hypoglycemia unawareness, and impaired counterregulation (16,1820) and concluded that recurrent hypoglycemia spirals into a “vicious cycle” known as hyperglycemia-associated autonomic failure (HAAF) (21). Our studies showed that increased GV and the extent and frequency of low BG are major contributors to hypoglycemia and that such changes are detectable by frequent BG measurement (2225).”

“The traditional statistical calculation of BG includes standard deviation (SD) (27), coefficient of variation (CV), or other metrics, such as the M-value introduced in 1965 (28), the mean amplitude of glucose excursions (MAGE) introduced in 1970 (29), the glycemic lability index (30), or the mean absolute glucose (MAG) change (31,32). […] the low BG index (LBGI), high BG index (HBGI), and average daily risk range (ADRR) […] are [all] based on a transformation of the BG measurement scale […], which aims to correct the substantial asymmetry of the BG measurement scale. Numerically, the hypoglycemic range (BG <70 mg/dL) is much narrower than that in the hyperglycemic range (BG >180 mg/dL) (34). As a result, whereas SD, CV, MAGE, and MAG are inherently biased toward hyperglycemia and have a relatively weak association with hypoglycemia, the LBGI and ADRR account well for the risk of hypoglycemic excursions. […] The analytical form of the scale transformation […] was based on accepted clinical assumptions, not on a particular data set, and was fixed 17 years ago, which made the approach extendable to any data set (34). On the basis of this transformation, we have developed our theory of risk analysis of BG data (35), defining a computational risk space that proved to be very suitable for quantifying the extent and frequency of glucose excursions. The utility of the risk analysis has been repeatedly confirmed (9,25,3638). We first introduced the LBGI and HBGI, which were specifically designed to be sensitive only to the low and high end of the BG scale, respectively, accounting for hypo- and hyperglycemia without overlap (24). Then in 2006, we introduced the ADRR, a measure of GV that is equally sensitive to hypo- and hyperglycemic excursions and is predictive of extreme BG fluctuations (38). Most recently, corrections were introduced that allowed the LBGI and HBGI to be computed from CGM data with results directly comparable to SMBG [self-monitoring of BG] (39).”

“[A]lthough GV has richer information content than just average glucose (HbA1c), its quantitative assessment is not straightforward because glucose fluctuations carry two components: amplitude and timing.

The standard assessment of GV is measuring amplitude. However, when measuring amplitude we should be mindful that deviations toward hypoglycemia are not equal to deviations toward hyperglycemia—a 20 mg/dL decline in BG levels from 70 to 50 mg/dL is clinically more important than a 20 mg/dL raise of BG from 160 to 180 mg/dL. We explained how to fix that with a well-established rescaling of the BG axis introduced more than 15 years ago (34). […] In addition, we should be mindful of the timing of BG fluctuations. There are a number of measures assessing GV amplitude from routine SMBG, but the timing of readings is frequently ignored even if the information is available (42). Yet, contrary to widespread belief, BG fluctuations are a process in time and the speed of transition from one BG state to another is of clinical importance. With the availability of CGM, the assessment of GV timing became not only possible but also required (32). Responding to this necessity, we should keep in mind that the assessment of temporal characteristics of GV benefits from mathematical computations that go beyond basic arithmetic. Thus, some assistance from the theory and practice of time series and dynamical systems analysis would be helpful. Fortunately, these fields are highly developed, theoretically and computationally, and have been used for decades in other areas of science […] The computational methods are standardized and available in a number of software products and should be used for the assessment of GV. […] There is no doubt that the timing of glucose fluctuations is clinically important, but there is a price to pay for its accurate assessment—a bit higher level of mathematical complexity. This, however, should not be a deterrent.”

vii. Predictors of Increased Carotid Intima-Media Thickness in Youth With Type 1 Diabetes: The SEARCH CVD Study.

“Adults with childhood-onset type 1 diabetes are at increased risk for premature cardiovascular disease (CVD) morbidity and mortality compared with the general population (1). The antecedents of CVD begin in childhood (2), and early or preclinical atherosclerosis can be detected as intima-media thickening in the artery wall (3). Carotid intima-media thickness (IMT) is an established marker of atherosclerosis because of its associations with CVD risk factors (4,5) and CVD outcomes, such as myocardial infarction and stroke in adults (6,7).

Prior work […] has shown that youth with type 1 diabetes have higher carotid IMT than control subjects (813). In cross-sectional studies, risk factors associated with higher carotid IMT include younger age at diabetes onset, male sex, adiposity, higher blood pressure (BP) and hemoglobin A1c (HbA1c), and lower vitamin C levels (8,9,11). Only one study has evaluated CVD risk factors longitudinally and the association with carotid IMT progression in youth with type 1 diabetes (14). In a German cohort of 70 youth with type 1 diabetes, Dalla Pozza et al. (14) demonstrated that CVD risk factors, including BMI z score (BMIz), systolic BP, and HbA1c, worsened over time. They also found that baseline HbA1c and baseline and follow-up systolic BP were significant predictors of change in carotid IMT over 4 years.”

“Before the current study, no published reports had assessed the impact of changes in CVD risk factors and carotid IMT in U.S. adolescents with type 1 diabetes. […] Participants in this study were enrolled in SEARCH CVD, an ancillary study to the SEARCH for Diabetes in Youth that was conducted in two of the five SEARCH centers (Colorado and Ohio). […] This report includes 298 youth who completed both baseline and follow-up SEARCH CVD visits […] At the initial visit, youth with type 1 diabetes were a mean age of 13.3 ± 2.9 years (range 7.6–21.3 years) and had an average disease duration of 3.6 ± 3.3 years. […] Follow-up data were obtained at a mean age of 19.2 ± 2.7 years, when the average duration of type 1 diabetes was 10.1 ± 3.9 years. […] In the current study, we show that older age (at baseline) and male sex were significantly associated with follow-up IMT. By using AUC measurements, we also show that a higher BMIz exposure over ∼5 years was significantly associated with IMT at follow-up. From baseline to follow-up, the mean BMI increased from within normal limits (21.1 ± 4.3 kg/m2) to overweight (25.1 ± 4.8 kg/m2), defined as a BMI ≥25 kg/m2 in adults (26,27). This large change in BMI may explain why BMIz was the only modifiable risk factor to be associated with follow-up IMT in the final models. Whether the observed increase in BMIz over time is part of the natural evolution of diabetes, aging in an obesogenic society, or a consequence of intensive insulin therapy is not known.”

“Data from the DCCT/EDIC cohorts have suggested nontraditional risk factors, including acute phase reactants, thrombolytic factors, cytokines/adipokines (34), oxidized LDL, and advanced glycation end products (30) may be important biomarkers of increased CVD risk in adults with type 1 diabetes. However, many of these nontraditional risk factors […] were not found to associate with IMT until 8–12 years after the DCCT ended, at the time when traditional CVD risk factors were also found to predict IMT. Collectively, these findings suggest that many traditional and nontraditional risk factors are not identified as relevant until later in the atherosclerotic process and highlight the critical need to better identify risk factors that may influence carotid IMT early in the course of type 1 diabetes because these may be important modifiable CVD risk factors of focus in the adolescent population. […] Although BMIz was the only identified risk factor to predict follow-up IMT at this age [in our study], it is possible that increases in dyslipidemia, BP, smoking, and HbA1c are related to carotid IMT but only after longer duration of exposure.”

July 13, 2017 Posted by | Cardiology, Diabetes, Medicine, Neurology, Studies | Leave a comment

Words

Almost all of the words included below are words which I encountered while reading the Rex Stout books: Too Many Cooks, Some Buried Caesar, Over My Dead Body, Where There’s A Will, Black Orchids, Not Quite Dead Enough, The Silent Speaker, and Too Many Women.

Consilience. Plerophory. Livery. Fleshpot. Electioneer. Estop. Gibbosity. Piroshki. Clodhopper. Phlebotomy. Concordat. Clutch. Katydid. Tarpon. Bower. Scoot. Suds. Rotunda. Gab. Floriculture.

Scowl. Commandeer. Apodictically. Blotch. Bauble. Thurl. Wilt. Huff. Clodhopper. Consignee. Épée. Imprecation. Intransigent. Couturier. Quittance. Dingus. MetonymyChintzy. Skittish. Natty.

Intrigante. Curlicue. Bedraggled. Rotogravure. Legatee. Caper. Phiz. Derrick. Labellum. Mumblety-peg. Flump. Kerplunk. Portage. Pettish. Darb. Partridge. Cheviot. Jaunty. Accouterment. Obreptitious.

Receptacle. Impetuous. Springe. Toting. Blowsy. Flam. Linnet. Carton. Bollix. Awning. Chiffonier. Sniggle. Toggle. Craw. Simp. Titter. Wren. Endive. Assiduity. Pudgy.

July 12, 2017 Posted by | Books, language | Leave a comment

Beyond Significance Testing (II)

I have added some more quotes and observations from the book below.

“The least squares estimators M and s2 are not robust against the effects of extreme scores. […] Conventional methods to construct confidence intervals rely on sample standard deviations to estimate standard errors. These methods also rely on critical values in central test distributions, such as t and z, that assume normality or homoscedasticity […] Such distributional assumptions are not always plausible. […] One option to deal with outliers is to apply transformations, which convert original scores with a mathematical operation to new ones that may be more normally distributed. The effect of applying a monotonic transformation is to compress one part of the distribution more than another, thereby changing its shape but not the rank order of the scores. […] It can be difficult to find a transformation that works in a particular data set. Some distributions can be so severely nonnormal that basically no transformation will work. […] An alternative that also deals with departures from distributional assumptions is robust estimation. Robust (resistant) estimators are generally less affected than least squares estimators by outliers or nonnormality.”

“An estimator’s quantitative robustness can be described by its finite-sample breakdown point (BP), or the smallest proportion of scores that when made arbitrarily very large or small renders the statistic meaningless. The lower the value of BP, the less robust the estimator. For both M and s2, BP = 0, the lowest possible value. This is because the value of either statistic can be distorted by a single outlier, and the ratio 1/N approaches zero as sample size increases. In contrast, BP = .50 for the median because its value is not distorted by arbitrarily extreme scores unless they make up at least half the sample. But the median is not an optimal estimator because its value is determined by a single score, the one at the 50th percentile. In this sense, all the other scores are discarded by the median. A compromise between the sample mean and the median is the trimmed mean. A trimmed mean Mtr is calculated by (a) ordering the scores from lowest to highest, (b) deleting the same proportion of the most extreme scores from each tail of the distribution, and then (c) calculating the average of the scores that remain. […] A common practice is to trim 20% of the scores from each tail of the distribution when calculating trimmed estimators. This proportion tends to maintain the robustness of trimmed means while minimizing their standard errors when sampling from symmetrical distributions […] For 20% trimmed means, BP = .20, which says they are robust against arbitrarily extreme scores unless such outliers make up at least 20% of the sample.”

The standard H0 is both a point hypothesis and a nil hypothesis. A point hypothesis specifies the numerical value of a parameter or the difference between two or more parameters, and a nil hypothesis states that this value is zero. The latter is usually a prediction that an effect, difference, or association is zero. […] Nil hypotheses as default explanations may be fine in new research areas when it is unknown whether effects exist at all. But they are less suitable in established areas when it is known that some effect is probably not zero. […] Nil hypotheses are tested much more often than non-nil hypotheses even when the former are implausible. […] If a nil hypothesis is implausible, estimated probabilities of data will be too low. This means that risk for Type I error is basically zero and a Type II error is the only possible kind when H0 is known in advance to be false.”

“Too many researchers treat the conventional levels of α, either .05 or .01, as golden rules. If other levels of α are specifed, they tend to be even lower […]. Sanctification of .05 as the highest “acceptable” level is problematic. […] Instead of blindly accepting either .05 or .01, one does better to […] [s]pecify a level of α that reflects the desired relative seriousness (DRS) of Type I error versus Type II error. […] researchers should not rely on a mechanical ritual (i.e., automatically specify .05 or .01) to control risk for Type I error that ignores the consequences of Type II error.”

“Although p and α are derived in the same theoretical sampling distribution, p does not estimate the conditional probability of a Type I error […]. This is because p is based on a range of results under H0, but α has nothing to do with actual results and is supposed to be specified before any data are collected. Confusion between p and α is widespread […] To differentiate the two, Gigerenzer (1993) referred to p as the exact level of significance. If p = .032 and α = .05, H0 is rejected at the .05 level, but .032 is not the long-run probability of Type I error, which is .05 for this example. The exact level of significance is the conditional probability of the data (or any result even more extreme) assuming H0 is true, given all other assumptions about sampling, distributions, and scores. […] Because p values are estimated assuming that H0 is true, they do not somehow measure the likelihood that H0 is correct. […] The false belief that p is the probability that H0 is true, or the inverse probability error […] is widespread.”

“Probabilities from significance tests say little about effect size. This is because essentially any test statistic (TS) can be expressed as the product TS = ES × f(N) […] where ES is an effect size and f(N) is a function of sample size. This equation explains how it is possible that (a) trivial effects can be statistically significant in large samples or (b) large effects may not be statistically significant in small samples. So p is a confounded measure of effect size and sample size.”

“Power is the probability of getting statistical significance over many random replications when H1 is true. it varies directly with sample size and the magnitude of the population effect size. […] This combination leads to the greatest power: a large population effect size, a large sample, a higher level of α […], a within-subjects design, a parametric test rather than a nonparametric test (e.g., t instead of Mann–Whitney), and very reliable scores. […] Power .80 is generally desirable, but an even higher standard may be need if consequences of Type II error are severe. […] Reviews from the 1970s and 1980s indicated that the typical power of behavioral science research is only about .50 […] and there is little evidence that power is any higher in more recent studies […] Ellis (2010) estimated that < 10% of studies have samples sufficiently large to detect smaller population effect sizes. Increasing sample size would address low power, but the number of additional cases necessary to reach even nominal power when studying smaller effects may be so great as to be practically impossible […] Too few researchers, generally < 20% (Osborne, 2008), bother to report prospective power despite admonitions to do so […] The concept of power does not stand without significance testing. as statistical tests play a smaller role in the analysis, the relevance of power also declines. If significance tests are not used, power is irrelevant. Cumming (2012) described an alternative called precision for research planning, where the researcher specifies a target margin of error for estimating the parameter of interest. […] The advantage over power analysis is that researchers must consider both effect size and precision in study planning.”

“Classical nonparametric tests are alternatives to the parametric t and F tests for means (e.g., the Mann–Whitney test is the nonparametric analogue to the t test). Nonparametric tests generally work by converting the original scores to ranks. They also make fewer assumptions about the distributions of those ranks than do parametric tests applied to the original scores. Nonparametric tests date to the 1950s–1960s, and they share some limitations. One is that they are not generally robust against heteroscedasticity, and another is that their application is typically limited to single-factor designs […] Modern robust tests are an alternative. They are generally more flexible than nonparametric tests and can be applied in designs with multiple factors. […] At the end of the day, robust statistical tests are subject to many of the same limitations as other statistical tests. For example, they assume random sampling albeit from population distributions that may be nonnormal or heteroscedastic; they also assume that sampling error is the only source of error variance. Alternative tests, such as the Welch–James and Yuen–Welch versions of a robust t test, do not always yield the same p value for the same data, and it is not always clear which alternative is best (Wilcox, 2003).”

July 11, 2017 Posted by | Books, Psychology, Statistics | Leave a comment

The Personality Puzzle (III)

I have added some more quotes and observations from the book below.

“Across many, many traits, the average correlation across MZ twins is about .60, and across DZ twins it is about .40, when adjusted for age and gender […] This means that, according to twin studies, the average heritability of many traits is about .40, which is interpreted to mean that 40 percent of phenotypic (behavioral) variance is accounted for by genetic variance. The heritabilities of the Big Five traits are a bit higher; according to one comprehensive summary they range from .42, for agreeableness, to .57, for openness (Bouchard, 2004). […] behavioral genetic analyses and the statistics they produce refer to groups or populations, not individuals. […] when research concludes that a personality trait is, say, 50 percent heritable, this does not mean that half of the extent to which an individual expresses that trait is determined genetically. Instead, it means that 50 percent of the degree to which the trait varies across the population can be attributed to genetic variation. […] Because heritability is the proportion of variation due to genetic influences, if there is no variation, then the heritability must approach zero. […] Heritability statistics are not the nature-nurture ratio; a biologically determined trait can have a zero heritability.”

The environment can […] affect heritability […]. For example, when every child receives adequate nutrition, variance in height is genetically controlled. […] But in an environment where some are well fed while others go hungry, variance in height will fall more under the control of the environment. Well-fed children will grow near the maximum of their genetic potential while poorly fed children will grow closer to their genetic minimum, and the height of the parents will not matter so much; the heritability coeffcient for height will be much closer to 0. […] A trait that is adaptive in one situation may be harmful in another […] the same environments that promote good outcomes for some people can promote bad outcomes for others, and vice versa […] More generally, the same circumstances might be experienced as stressful, enjoyable, or boring, depending on the genetic predispositions of the individuals involved; these variations in experience can lead to very different behaviors and, over time, to the development of different personality traits.”

Mihalyi Csikszentmihalyi [argued] that the best way a person can spend time is in autotelic activities, those that are enjoyable for their own sake. The subjective experience of an autotelic activity — the enjoyment itself — is what Csikszentmihalyi calls flow.
Flow is not the same thing as joy, happiness, or other, more familiar terms for subjective well-being. Rather, the experience of flow is characterized by tremendous concentration, total lack of distractibility, and thoughts concerning only the activity at hand. […] Losing track of time is one sign of experiencing flow. According to Csikszentmihalyi, flow arises when the challenges an activity presents are well matched with your skills. If an activity is too diffcult or too confusing, you will experience anxiety, worry, and frustration. If the activity is too easy, you will experience boredom and (again) anxiety. But when skills and challenges are balanced, you experience flow. […] Csikszentmihalyi thinks that the secret for enhancing your quality of life is to spend as much time in flow as possible. Achieving flow entails becoming good at something you find worthwhile and enjoyable. […] Even in the best of circumstances [however], flow seems to describe a rather solitary kind of happiness. […] The drawback with flow is that somebody experiencing it can be difficult to interact with”. [I really did not like most of the stuff included in the part of the book from which this quote is taken, but I did find Csikszentmihalyi’s flow concept quite interesting.]

“About 80 percent of the participants in psychological research come from countries that are Western, Educated, Industrialized, Rich, and Democratic — ”WEIRD” in other words — although only 12 percent of the world’s population live there (Henrich et al., 2010).”

“If an animal or a person performs a behavior, and the behavior is followed by a good result — a reinforcement — the behavior becomes more likely. If the behavior is followed by a punishment, it becomes less likely. […] the results of operant conditioning are not necessarily logical. It can increase the frequency of any behavior, regardless of its real connection with the consequences that follow.”

“A punishment is an aversive consequence that follows an act in order to stop it and prevent its repetition. […] Many people believe the only way to stop or prevent somebody from doing something is punishment. […] You can [however] use reward for this purpose too. All you have to do is find a response that is incompatible with the one you are trying to get rid of, and reward that incompatible response instead. Reward a child for reading instead of punishing him for watching television. […] punishment works well when it is done right. The only problem is, it is almost never done right. […] One way to see how punishment works, or fails to work, is to examine the rules for applying it correctly. The classic behaviorist analysis says that five principles are most important […] 1. Availability of Alternatives: An alternative response to the behavior that is being punished must be available. This alternative response must not be punished and should be rewarded. […] 2. Behavioral and Situational Specificity: Be clear about exactly what behavior you are punishing and the circumstances under which it will and will not be punished. […] 3. Timing and Consistency: To be effective, a punishment needs to be applied immediately after the behavior you wish to prevent, every time that behavior occurs. Otherwise, the person (or animal) being punished may not understand which behavior is forbidden. […] 4. Conditioning Secondary Punishing Stimuli: One can lessen the actual use of punishment by conditioning secondary stimuli to it [such as e.g.  verbal warnings] […] 5. Avoiding Mixed Messages: […] Sometimes, after punishing a child, the parent feels so guilty that she picks the child up for a cuddle. This is a mistake. The child might start to misbehave just to get the cuddle that follows the punishment. Punish if you must punish, but do not mix your message. A variant on this problem occurs when the child learns to play one parent against the other. For example, after the father punishes the child, the child goes to the mother for sympathy, or vice versa. This can produce the same counterproductive result.”

Punishment will backfire unless all of the guidelines [above] are followed. Usually, they are not. A punisher has to be extremely careful, for several reasons. […] The first and perhaps most important danger of punishment is that it creates emotion. […] powerful emotions are not conducive to clear thinking. […] Punishment [also] tends to vary with the punisher’s mood, which is one reason it is rarely applied consistently. […] Punishment [furthermore] [m]otivates [c]oncealment: The prospective punishee has good reasons to conceal behavior that might be punished. […] Rewards have the reverse effect. When workers anticipate rewards for good work instead of punishment for bad work, they are naturally motivated to bring to the boss’s attention everything they are doing, in case it merits reward.”

Gordon Allport observed years ago [that] [“]For some the world is a hostile place where men are evil and dangerous; for others it is a stage for fun and frolic. It may appear as a place to do one’s duty grimly; or a pasture for cultivating friendship and love.[“] […] people with different traits see the world differently. This perception affects how they react to the events in their lives which, in turn, affects what they do. […] People [also] differ in the emotions they experience, the emotions they want to experience, how strongly they experience emotions, how frequently their emotions change, and how well they understand and control their emotions.”

July 9, 2017 Posted by | Books, Genetics, Psychology | Leave a comment

Beyond Significance Testing (I)

“This book introduces readers to the principles and practice of statistics reform in the behavioral sciences. it (a) reviews the now even larger literature about shortcomings of significance testing; (b) explains why these criticisms have sufficient merit to justify major changes in the ways researchers analyze their data and report the results; (c) helps readers acquire new skills concerning interval estimation and effect size estimation; and (d) reviews alternative ways to test hypotheses, including Bayesian estimation. […] I assume that the reader has had undergraduate courses in statistics that covered at least the basics of regression and factorial analysis of variance. […] This book is suitable as a textbook for an introductory course in behavioral science statistics at the graduate level.”

I’m currently reading this book. I have so far read 8 out of the 10 chapters included, and I’m currently sort of hovering between a 3 and 4 star goodreads rating; some parts of the book are really great, but there are also a few aspects I don’t like. Some parts of the coverage are rather technical and I’m still debating to which extent I should cover the technical stuff in detail later here on the blog; there are quite a few equations included in the book and I find it annoying to cover math using the wordpress format of this blog. For now I’ll start out with a reasonably non-technical post with some quotes and key ideas from the first parts of the book.

“In studies of intervention outcomes, a statistically significant difference between treated and untreated cases […] has nothing to do with whether treatment leads to any tangible benefits in the real world. In the context of diagnostic criteria, clinical significance concerns whether treated cases can no longer be distinguished from control cases not meeting the same criteria. For example, does treatment typically prompt a return to normal levels of functioning? A treatment effect can be statistically significant yet trivial in terms of its clinical significance, and clinically meaningful results are not always statistically significant. Accordingly, the proper response to claims of statistical significance in any context should be “so what?” — or, more pointedly, “who cares?” — without more information.”

“There are free computer tools for estimating power, but most researchers — probably at least 80% (e.g., Ellis, 2010) — ignore the power of their analyses. […] Ignoring power is regrettable because the median power of published nonexperimental studies is only about .50 (e.g., Maxwell, 2004). This implies a 50% chance of correctly rejecting the null hypothesis based on the data. In this case the researcher may as well not collect any data but instead just toss a coin to decide whether or not to reject the null hypothesis. […] A consequence of low power is that the research literature is often difficult to interpret. Specifically, if there is a real effect but power is only .50, about half the studies will yield statistically significant results and the rest will yield no statistically significant findings. If all these studies were somehow published, the number of positive and negative results would be roughly equal. In an old-fashioned, narrative review, the research literature would appear to be ambiguous, given this balance. It may be concluded that “more research is needed,” but any new results will just reinforce the original ambiguity, if power remains low.”

“Statistical tests of a treatment effect that is actually clinically significant may fail to reject the null hypothesis of no difference when power is low. If the researcher in this case ignored whether the observed effect size is clinically significant, a potentially beneficial treatment may be overlooked. This is exactly what was found by Freiman, Chalmers, Smith, and Kuebler (1978), who reviewed 71 randomized clinical trials of mainly heart- and cancer-related treatments with “negative” results (i.e., not statistically significant). They found that if the authors of 50 of the 71 trials had considered the power of their tests along with the observed effect sizes, those authors should have concluded just the opposite, or that the treatments resulted in clinically meaningful improvements.”

“Even if researchers avoided the kinds of mistakes just described, there are grounds to suspect that p values from statistical tests are simply incorrect in most studies: 1. They (p values) are estimated in theoretical sampling distributions that assume random sampling from known populations. Very few samples in behavioral research are random samples. Instead, most are convenience samples collected under conditions that have little resemblance to true random sampling. […] 2. Results of more quantitative reviews suggest that, due to assumptions violations, there are few actual data sets in which significance testing gives accurate results […] 3. Probabilities from statistical tests (p values) generally assume that all other sources of error besides sampling error are nil. This includes measurement error […] Other sources of error arise from failure to control for extraneous sources of variance or from flawed operational definitions of hypothetical constructs. It is absurd to assume in most studies that there is no error variance besides sampling error. Instead it is more practical to expect that sampling error makes up the small part of all possible kinds of error when the number of cases is reasonably large (Ziliak & mcCloskey, 2008).”

“The p values from statistical tests do not tell researchers what they want to know, which often concerns whether the data support a particular hypothesis. This is because p values merely estimate the conditional probability of the data under a statistical hypothesis — the null hypothesis — that in most studies is an implausible, straw man argument. In fact, p values do not directly “test” any hypothesis at all, but they are often misinterpreted as though they describe hypotheses instead of data. Although p values ultimately provide a yes-or-no answer (i.e., reject or fail to reject the null hypothesis), the question — p < a?, where a is the criterion level of statistical significance, usually .05 or .01 — is typically uninteresting. The yes-or-no answer to this question says nothing about scientific relevance, clinical significance, or effect size. […] determining clinical significance is not just a matter of statistics; it also requires strong knowledge about the subject matter.”

“[M]any null hypotheses have little if any scientific value. For example, Anderson et al. (2000) reviewed null hypotheses tested in several hundred empirical studies published from 1978 to 1998 in two environmental sciences journals. They found many implausible null hypotheses that specified things such as equal survival probabilities for juvenile and adult members of a species or that growth rates did not differ across species, among other assumptions known to be false before collecting data. I am unaware of a similar survey of null hypotheses in the behavioral sciences, but I would be surprised if the results would be very different.”

“Hoekstra, Finch, Kiers, and Johnson (2006) examined a total of 266 articles published in Psychonomic Bulletin & Review during 2002–2004. Results of significance tests were reported in about 97% of the articles, but confidence intervals were reported in only about 6%. Sadly, p values were misinterpreted in about 60% of surveyed articles. Fidler, Burgman, Cumming, Buttrose, and Thomason (2006) sampled 200 articles published in two different biology journals. Results of significance testing were reported in 92% of articles published during 2001–2002, but this rate dropped to 78% in 2005. There were also corresponding increases in the reporting of confidence intervals, but power was estimated in only 8% and p values were misinterpreted in 63%. […] Sun, Pan, and Wang (2010) reviewed a total of 1,243 works published in 14 different psychology and education journals during 2005–2007. The percentage of articles reporting effect sizes was 49%, and 57% of these authors interpreted their effect sizes.”

“It is a myth that the larger the sample, the more closely it approximates a normal distribution. This idea probably stems from a misunderstanding of the central limit theorem, which applies to certain group statistics such as means. […] This theorem justifies approximating distributions of random means with normal curves, but it does not apply to distributions of scores in individual samples. […] larger samples do not generally have more normal distributions than smaller samples. If the population distribution is, say, positively skewed, this shape will tend to show up in the distributions of random samples that are either smaller or larger.”

“A standard error is the standard deviation in a sampling distribution, the probability distribution of a statistic across all random samples drawn from the same population(s) and with each sample based on the same number of cases. It estimates the amount of sampling error in standard deviation units. The square of a standard error is the error variance. […] Variability of the sampling distributions […] decreases as the sample size increases. […] The standard error sM, which estimates variability of the group statistic M, is often confused with the standard deviation s, which measures variability at the case level. This confusion is a source of misinterpretation of both statistical tests and confidence intervals […] Note that the standard error sM itself has a standard error (as do standard errors for all other kinds of statistics). This is because the value of sM varies over random samples. This explains why one should not overinterpret a confidence interval or p value from a significance test based on a single sample.”

“Standard errors estimate sampling error under random sampling. What they measure when sampling is not random may not be clear. […] Standard errors also ignore […] other sources of error [:] 1. Measurement error [which] refers to the difference between an observed score X and the true score on the underlying construct. […] Measurement error reduces absolute effect sizes and the power of statistical tests. […] 2. Construct definition error [which] involves problems with how hypothetical constructs are defined or operationalized. […] 3. Specification error [which] refers to the omission from a regression equation of at least one predictor that covaries with the measured (included) predictors. […] 4. Treatment implementation error occurs when an intervention does not follow prescribed procedures. […] Gosset used the term real error to refer all types of error besides sampling error […]. In reasonably large samples, the impact of real error may be greater than that of sampling error.”

“The technique of bootstrapping […] is a computer-based method of resampling that recombines the cases in a data set in different ways to estimate statistical precision, with fewer assumptions than traditional methods about population distributions. Perhaps the best known form is nonparametric bootstrapping, which generally makes no assumptions other than that the distribution in the sample reflects the basic shape of that in the population. It treats your data file as a pseudo-population in that cases are randomly selected with replacement to generate other data sets, usually of the same size as the original. […] The technique of nonparametric bootstrapping seems well suited for interval estimation when the researcher is either unwilling or unable to make a lot of assumptions about population distributions. […] potential limitations of nonparametric bootstrapping: 1. Nonparametric bootstrapping simulates random sampling, but true random sampling is rarely used in practice. […] 2. […] If the shape of the sample distribution is very different compared with that in the population, results of nonparametric bootstrapping may have poor external validity. 3. The “population” from which bootstrapped samples are drawn is merely the original data file. If this data set is small or the observations are not independent, resampling from it will not somehow fix these problems. In fact, resampling can magnify the effects of unusual features in a small data set […] 4. Results of bootstrap analyses are probably quite biased in small samples, but this is true of many traditional methods, too. […] [In] parametric bootstrapping […] the researcher specifies the numerical and distributional properties of a theoretical probability density function, and then the computer randomly samples from that distribution. When repeated many times by the computer, values of statistics in these synthesized samples vary randomly about the parameters specified by the researcher, which simulates sampling error.”

July 9, 2017 Posted by | Books, Psychology, Statistics | Leave a comment

A few SSC comments

I recently left a few comments in an open thread on SSC, and I figured it might make sense to crosspost some of the comments made there here on the blog. I haven’t posted all my contributions to the debate here, rather I’ve just quoted some specific comments and observations which might be of interest. I’ve also added some additional remarks and comments which relate to the topics discussed. Here’s the main link (scroll down to get to my comments).

“One thing worth keeping in mind when evaluating pre-modern medicine characterizations of diabetes and the natural history of diabetes is incidentally that especially to the extent that one is interested in type 1 survivorship bias is a major problem lurking in the background. Prognostic estimates of untreated type 1 based on historical accounts of how long people could live with the disease before insulin are not in my opinion likely to be all that reliable, because the type of patients that would be recognized as (type 1) diabetics back then would tend to mainly be people who had the milder forms, because they were the only ones who lived long enough to reach a ‘doctor’; and the longer they lived, and the milder the sub-type, the more likely they were to be studied/’diagnosed’. I was a 2-year old boy who got unwell on a Tueday and was hospitalized three days later. Avicenna would have been unlikely to have encountered me, I’d have died before he saw me. (Similar lines of reasoning might lead to an argument that the incidence of diseases like type 1 diabetes may also today be underdiagnosed in developing countries with poorly developed health care systems.)”

Douglas Knight mentioned during our exchange that medical men of the far past might have been more likely to attend to patients with acute illnesses than patients with chronic conditions, making them more likely to attend to such cases than would otherwise be the case, a point I didn’t discuss in any detail during the exchange. I did however think it important to note here that information exchange was significantly slower, and transportation costs were much higher, in the past than they are today. This should make such a bias less relevant, all else equal. Avicenna and his colleagues couldn’t take a taxi, or learn by phone that X is sick. He might have preferentially attended to the acute cases he learned about, but given high transportation costs and inefficient communication channels he might often never arrive in time, or at all. A particular problem here is that there are no good data on the unobserved cases, because the only cases we know about today are the ones people like him have told us about.

Some more comments:

“One thing I was considering adding to my remarks about survivorship bias is that it is not in my opinion unlikely that what you might term the nature of the disease has changed over the centuries; indeed it might still be changing today. Globally the incidence of type 1 has been increasing for decades and nobody seems to know why, though there’s consensus about an environmental trigger playing a major role. Maybe incidence is not the only thing that’s changed, maybe e.g. the time course of the ‘average case’ has also changed? Maybe due to secondary factors; better nutritional status now equals slower progression of beta cell failure than was the case in the past? Or perhaps the other way around: Less exposure to bacterial agents the immune system throughout evolutionary time has been used to having to deal with today means that the autoimmune process is accelerated today, compared to in the far past where standards of hygiene were different. Who knows? […] Maybe survivorship bias wasn’t that big of a deal, but I think one should be very cautious about which assumptions one might implicitly be making along the way when addressing questions of this sort of nature. Some relevant questions will definitely be unknowable due to lack of good data which we will never be able to obtain.”

I should perhaps interpose here that even if survivorship bias ‘wasn’t that big of a deal’, it’s still sort of a big problem in the analytical setting because it seems perfectly plausible to me to be making the assumption that it might even so have been a big deal. These kinds of problems magnify our error bars and reduce confidence in our conclusions, regardless of the extent to which they actually played a role. When you know the exact sign and magnitude of a given moderating effect you can try to correct for it, but this is very difficult to do when a large range of moderator effect sizes might be considered plausible. It might also here be worth mentioning explicitly that biases such as the survivorship bias mentioned can of course impact a lot of things besides just the prognostic estimates; for example if a lot of cases never come to the attention of the medical people because these people were unavailable (due to distance, cost, lack of information, etc.) to the people who were sick, incidence and prevalence will also implicitly be underestimated. And so on. Back to the comments:

“Once you had me thinking that it might have been harder [for people in the past] to distinguish [between type 1 and type 2 diabetes] than […] it is today, I started wondering about this, and the comments below relate to this topic. An idea that came to mind in relation to the type 1/type 2 distinction and the ability of people in the past to make this distinction: I’ve worked on various identification problems present in the diabetes context before, and I know that people even today make misdiagnoses and e.g. categorize type 1 diabetics as type 2. I asked a diabetes nurse working in the local endocrinology unit about this at one point, and she told me they had actually had a patient not long before then who had been admitted a short while after having been diagnosed with type 2. Turned out he was type 1, so the treatment failed. Misdiagnoses happen for multiple reasons, one is that obese people also sometimes develop type 1, and if it’s an acute onset setting the weight loss is not likely to be very significant. Patient history should in such a case provide the doctor with the necessary clues, but if the guy making the diagnosis is a stressed out GP who’s currently treating a lot of obese patients for type 2, mistakes happen. ‘Pre-scientific method’ this sort of individual would have been inconvenient to encounter, because a ‘counter-example’ like that supposedly demonstrating that the obese/thin(/young/old, acute/protracted…) distinction was ‘invalid’ might have held a lot more weight than it hopefully would today in the age of statistical analysis. A similar problem would be some of the end-stage individuals: A type 1 pre-insulin would be unlikely to live long enough to develop long term complications of the disease, but would instead die of DKA. The problem is that some untreated type 2 patients also die of DKA, though the degree of ketosis varies in type 2 patients. DKA in type 2 could e.g. be triggered by a superimposed cardiovascular event or an infection, increasing metabolic demands to an extent that can no longer be met by the organism, and so might well present just as acutely as it would in a classic acute-onset type 1 case. Assume the opposite bias you mention is playing a role; the ‘doctor’ in the past is more likely to see the patients in such a life-threatening setting than in the earlier stages. He observes a 55 year old fat guy dying in a very similar manner to the way a 12 year old girl died a few months back – very characteristic symptoms, breath smells fruity, Kussmaul respiration, polyuria and polydipsia…). What does he conclude? Are these different diseases?”

Making the doctor’s decision problem even harder is of course the fact that type 2 diabetes even today often goes undiagnosed until complications arise. Some type 2 patients get their diagnosis only after they had their first heart attack as a result of their illness. So the hypothetical obese middle-aged guy presenting with DKA might not have been known by anyone to be ‘a potentially different kind of diabetic’.

‘The Nybbler’ asked this question in the thread: “Wouldn’t reduced selection pressure be a major reason for increase of Type I diabetes? Used to be if you had it, chance of surviving to reproduce was close to nil.”

I’ll mention here that I’ve encountered this kind of theorizing before, but that I’ve never really addressed it – especially the second part – explicitly, though I’ve sometimes felt like doing that. I figured this post might be a decent place to at least scratch the surface. The idea that there are more type 1 diabetics now than there used to be because type 1 diabetics used to die of their disease and now they don’t (…and so now they are able to transmit their faulty genes to subsequent generations, leading to more diabetic individuals over time) sounds sort of reasonable if you don’t know very much about diabetes, but it sounds less reasonable to people who do. Genes matter, and changed selection pressures have probably played a role, but I find it hard to believe this particular mechanism is a major factor. I have included both my of my replies to ‘Nybbler’ below:

First comment:

“I’m not a geneticist and this is sort-of-kind-of near the boundary area of where I feel comfortable providing answers (given that others may be more qualified to evaluate questions like this than I am). However a few observations which might be relevant are the following:

i) Although I’ll later go on to say that vertical transmission is low, I first have to point out that some people who developed type 1 diabetes in the past did in fact have offspring, though there’s no doubt about the condition being fitness-reducing to a very large degree. The median age of diagnosis of type 1 is somewhere in the teenage years (…today. Was it the same way 1000 years ago, or has the age profile changed over time? This again relates to questions asked elsewhere in this discussion…), but people above the age of 30 get type 1 too.

ii) Although type 1 display some level of familia[l] clustering, most cases of type 1 are not the result of diabetics having had children who then proceed to inherit their parents’ disease. To the extent that reduced selection is a driver of increased incidence, the main cause would be broad selection effects pertaining to immune system functioning in general in the total population at risk (i.e. children in general, including many children with what might be termed suboptimal immune system functioning, being more likely to survive and later develop type 1 diabetes), not effects derived from vertical transmission of the disease (from parent to child). Roughly 90% of newly diagnosed type 1 diabetics in population studies have a negative family history of the disease, and on average only 2% of the children of type 1 diabetic mothers, and 5% of the children of type 1 diabetic fathers, go on to develop type 1 diabetes themselves.

iii) Historically vertical transmission has even in modern times been low. On top of the quite low transmission rates mentioned above, until well into the 80es or 90es many type 1 diabetic females were explicitly advised by their medical care providers not to have children, not because of the genetic risk of disease transmission but because pregnancy outcomes were likely to be poor; and many of those who disregarded the advice gave birth to offspring who were at a severe fitness disadvantage from the start. Poorly controlled diabetes during pregnancy leads to a very high risk of birth defects and/or miscarriage, and may pose health risks to the mother as well through e.g. an increased risk of preeclampsia (relevant link). It is only very recently that we’ve developed the knowledge and medical technology required to make pregnancy a reasonably safe option for female diabetics. You still had some diabetic females who gave birth before developing diabetes, like in the far past, and the situation was different for males, but either way I feel reasonably confident claiming that if you look for genetic causes of increasing incidence, vertical transmission should not be the main factor to consider.

iv) You need to be careful when evaluating questions like these to keep a distinction between questions relating to drivers of incidence and questions relating to drivers of prevalence at the back of your mind. These two sets of questions are not equivalent.

v) If people are interested to know more about the potential causes of increased incidence of type 1 diabetes, here’s a relevant review paper.”

I followed up with a second comment a while later, because I figured a few points of interest might not have been sufficiently well addressed in my first comment:

“@Nybbler:

A few additional remarks.

i) “Temporal trends in chronic disease incidence rates are almost certainly environmentally induced. If one observes a 50% increase in the incidence of a disorder over 20 yr, it is most likely the result of changes in the environment because the gene pool cannot change that rapidly. Type 1 diabetes is a very dynamic disease. […] results clearly demonstrate that the incidence of type 1 diabetes is rising, bringing with it a large public health problem. Moreover, these findings indicate that something in our environment is changing to trigger a disease response. […] With the exception of a possible role for viruses and infant nutrition, the specific environmental determinants that initiate or precipitate the onset of type 1 diabetes remain unclear.” (Type 1 Diabetes, Etiology and Treatment. Just to make it perfectly clear that although genes matter, environmental factors are the most likely causes of the rising levels of incidence we’ve seen in recent times.)

ii. Just as you need to always keep incidence and prevalence in mind when analyzing these things (for example low prevalence does not mean incidence is necessarily low, or was low in the past; low prevalence could also be a result of a combination of high incidence and high case mortality. I know from experience that even diabetes researchers tend to sometimes overlook stuff like this), you also need to keep the distinction between genotype and phenotype in mind. Given the increased importance of one or more environmental triggers in modern times, penetrance is likely to have changed over time. This means for example that ‘a diabetic genotype’ may have been less fitness reducing in the past than it is today, even if the associated ‘diabetic phenotype’ may on the other hand have been much more fitness reducing than it is now; people who developed diabetes died, but many of the people who might in the current environment be considered high-risk cases may not have been high risk in the far past, because the environmental trigger causing disease was absent, or rarely encountered. Assessing genetic risk for diabetes is complicated, and there’s no general formula for calculating this risk either in the type 1 or type 2 case; monogenic forms of diabetes do exist, but they account for a very small proportion of cases (1-5% of diabetes in young individuals) – most cases are polygenic and display variable levels of penetrance. Note incidentally that a story of environmental factors becoming more important over time is actually implicitly also, to the extent that diabetes is/has been fitness-reducing, a story of selection pressures against diabetic genotypes potentially increasing over time, rather than the opposite (which seems to be the default assumption when only taking into account stuff like the increased survival rates of type 1 diabetics over time). This stuff is complicated.”

I wasn’t completely happy with my second comment (I wrote it relatively fast and didn’t have time to go over it in detail after I’d written it), so I figured it might make sense to add a few details here. One key idea here is of course that you need to distinguish between people who are ‘vulnerable’ to developing type 1 diabetes, and people who actually do develop the disease. If fewer people who today would be considered ‘vulnerable’ developed the disease in the past than is the case now, selection against the ‘vulnerable’ genotype would – all else equal – have been lower throughout evolutionary time than it is today.

All else is not equal because of insulin treatment. But a second key point is that when you’re interested in fitness effects, mortality is not the only variable of interest; many diabetic women who were alive because of insulin during the 20th century but who were also being discouraged from having children may well have left no offspring. Males who committed suicide or died from kidney failure in their twenties likely also didn’t leave many offspring. Another point related to the mortality variable is that although diabetes mortality might in the past have been approximated reasonably well by a simple binary outcome variable/process (no diabetes = alive, diabetes = dead), type 1 diabetes has had large effects on mortality rates also throughout the chunk of history during which insulin has been a treatment option; mortality rates 3 or 4 times higher than those of non-diabetics are common in population studies, and such mortality rates add up over time even if base rates are low, especially in a fitness context, as they for most type 1 diabetics are at play throughout the entire fertile period of the life history. Type 2 diabetes is diagnosed mainly in middle-aged individuals, many of whom have already completed their reproductive cycle, but type 1 diabetes is very different in that respect. Of course there are multiple indirect effects at play as well here, e.g. those of mate choice; which is the more attractive potential partner, the individual with diabetes or the one without? What if the diabetic also happens to be blind?

A few other quotes from the comments:

“The majority of patients on insulin in the US are type 2 diabetics, and it is simply wrong that type 2 diabetics are not responsive to insulin treatment. They were likely found to be unresponsive in early trials because of errors of dosage, as they require higher levels of the drug to obtain the same effect as will young patients diagnosed with type 1 (the primary group on insulin in the 30es). However, insulin treatment is not the first-line option in the type 2 context because the condition can usually be treated with insulin-sensitizing agents for a while, until they fail (those drugs will on average fail in something like ~50% of subjects within five years of diagnosis, which is the reason – combined with the much (order(/s, depending on where you are) of magnitude) higher prevalence of type 2 – why a majority of patients on insulin have type 2), and these tend to a) be more acceptable to the patients (a pill vs an injection) and b) have fewer/less severe side effects on average. One reason which also played a major role in delaying the necessary use of insulin to treat type 2 diabetes which could not be adequately controlled via other means was incidentally the fact that insulin ca[u]ses weight gain, and the obesity-type 2 link was well known.”

“Type 1 is autoimmune, and most cases of type 2 are not, but some forms of type 2 seem to have an autoimmune component as well (“the overall autoantibody frequency in type 2 patients varies between 6% and 10%” – source) (these patients, who can be identified through genetic markers, will on average proceed to insulin dependence because of treatment failure in the context of insulin sensitizing-agents much sooner than is usually the case in patients with type 2). In general type 1 is caused by autoimmune beta cell destruction and type 2 mainly by insulin resistance, but combinations of the two are also possible […], and patients with type 1 can develop insulin resistance just as patients with type 2 can lose beta cells via multiple pathways. The major point here being that the sharp diagnostic distinction between type 1 and type 2 is a major simplification of what’s really going on, and it’s hiding a lot of heterogeneity in both samples. Some patients with type 1 will develop diabetes acutely or subacutely, within days or hours, whereas others will have elevated blood glucose levels for months before medical attention is received and a diagnosis is made (you can tell whether or not blood glucose has been elevated pre-diagnosis by looking at one of the key diagnostic variables, Hba1c, which is a measure of the average blood glucose over the entire lifetime of a red blood cell (~3-4 months) – in some newly diagnosed type 1s, this variable is elevated, in others it is not. Some type 1 patients will develop other autoimmune conditions later on, whereas others will not, and some will be more likely to develop complications than others who have the same level of glycemic control.

Type 1 and type 2 diabetes are quite different conditions, but in terms of many aspects of the diseases there are significant degrees of overlap (for example they develop many of the same complications, for similar (pathophysiological) reasons), yet they are both called diabetes. You don’t want to treat a type 2 diabetic with insulin if he can be treated with metformin, and treating a type 1 with metformin will not help – so different treatments are required.”

“In terms of whether it’s ideal to have one autistic diagnostic group or two (…or three, or…) [this question was a starting point for the debate from which I quote, but I decided not to go much into this topic here], I maintain that to a significant extent the answer to that question relates to what the diagnosis is supposed to accomplish. If it makes sense for researchers to be able to distinguish, which it probably does, but it is not necessary for support organizers/providers to know the subtype in order to provide aid, then you might end up with one ‘official’ category and two (or more) ‘research categories’. I would be fine with that (but again I don’t find this discussion interesting). Again a parallel might be made to diabetes research: Endocrinologists are well aware that there’s a huge amount of variation in both the type 1 and type 2 samples, to the extent that it’s sort of silly to even categorize these illnesses using the same name, but they do it anyway for reasons which are sort of obvious. If you’re type 1 diabetic and you have an HLA mutation which made you vulnerable to diabetes and you developed diabetes at the age of 5, well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. If on the other hand you’re an adult guy who due to a very different genetic vulnerability developed type 1 diabetes at the age of 30 (and later on Graves’ disease at the age of 40, due to the same mutation), well, we’ll start you on insulin, try to help you achieve good metabolic control, and screen you regularly for complications. The only thing type 1 diabetics have in common is the fact that their beta cells die due to some autoimmune processes. But it could easily be conceived of instead as literally hundreds of different diseases. Currently the distinctions between the different disease-relevant pathophysiological processes don’t matter very much in the treatment context, but they might do that at some point in the future, and if that happens the differences will start to become more important. People might at that point start to talk about type 1a diabetes, which might be the sort you can delay or stop with gene therapy, and type 1b which you can’t delay or stop (…yet). Lumping ‘different’ groups together into one diagnostic category is bad if it makes you overlook variation which is important, and this may be a problem in the autism context today, but regardless of the sizes of the diagnostic groups you’ll usually still end up with lots of residual (‘unexplained’) variation.”

I can’t recall to which extent I’ve discussed this last topic – the extent to which type 1 diabetes is best modeled as one illness or many – but it’s an important topic to keep at the back of your mind when you’re reading the diabetes literature. I’m assuming that in some contexts the subgroup heterogeneities, e.g. in terms of treatment response, will be much more important than in other contexts, so you probably need specific subject matter knowledge to make any sort of informed decision about to which extent potential unobserved heterogeneities may be important in a specific setting, but even if you don’t have that ‘a healthy skepticism’, derived from keeping the potential for these factors to play a role in mind, is likely to be more useful than the alternative. In that context I think the (poor, but understandable) standard practice of lumping together type 1 and type 2 diabetics in studies may lead many people familiar with the differences between the two conditions to think along the lines that as long as you know the type, you’re good to go – ‘at least this study only looked at type 1 individuals, not like those crappy studies which do not distinguish between type 1 and type 2, so I can definitely trust these results to apply to the subgroup of type 1 diabetics in which I’m interested’ – and I think this tendency, to the extent that it exists, is unfortunate.

July 8, 2017 Posted by | autism, Diabetes, Epidemiology, Genetics, Medicine, Psychology | Leave a comment

Words

Many of the words below, though far from all of them, are words which I’ve encountered while reading Rex Stout‘s Nero Wolfe novels. I’ve read roughly 20 of Stout’s books over the last month or so and I like them a lot.

Scofflaw. Vulnific. Brisance. Delitescent. Scrunch. Tosspot. Flaneur. Crenellation. Autotelic. Decoupage. Gulosity. Bray. Modish. Cloddish. Vermiculate. Logy. Instar. Amatory. Coddle. Rayon.

Impedimenta. Mosey. Mucilage. Lulu. Contrariety. Loam. Lath. Sumac. Excelsior. Crotalid. Tonneau. Rotogravure. Dicker. Quixotism. Twill. Sill. Rumpus. Avoirdupois. Tarragon. Flummery.

Extempore. Rodomontade. Piddling. Dainties. Dingy. Aplomb. Gullery. Mash note. Carom. Flue. Traipsing. Contumacy. Hoosegow. Modicum. Snooty. Phiz. Acarpous. Gob. Scraggly. Spiff.

Frazzle. Burlap. Ruction. Apodictic. Clepe. Craichy. Fricandeau. Rut. Scuff. Querulous. Escutcheon. Dolichocephaly. Pestiferous. Caravansary. Coquin. Klieg. Gump. Herringbone. Ebullience. Confraternity.

 

July 5, 2017 Posted by | language | Leave a comment

The Personality Puzzle (II)

I have added some more quotes and observations from the book below. Some of the stuff covered in this post is very closely related to material I’ve previously covered on the blog, e.g. here and here, but I didn’t mind reviewing this stuff here. If you’re already familiar with Funder’s RAM model of personality judgment you can probably skip the last half of the post without missing out on anything.

“[T]he trait approach [of personality psychology] focuses exclusively on individual differences. It does not attempt to measure how dominant, sociable, or nervous anybody is in an absolute sense; there is no zero point on any dominance scale or on any measure of any other trait. Instead, the trait approach seeks to measure the degree to which a person might be more or less dominant, sociable, or nervous than someone else. (Technically, therefore, trait measurements are made on ordinal rather than ratio scales.) […] Research shows that the stability of the differences between people increases with age […] According to one major summary of the literature, the correlation coefficient reflecting consistency of individual differences in personality is .31 across childhood, .54 during the college years, and .74 between the ages of 50 and 70 […] The main reason personality becomes more stable during the transition from child to adult to senior citizen seems to be that one’s environment also gets more stable with age […] According to one major review, longitudinal data show that, on average, people tend to become more socially dominant, agreeable, conscientious, and emotionally stable (lower on neuroticism) over time […] [However] people differ from each other in the degree to which they have developed a consistent personality […] Several studies suggest that the consistency of personality is associated with maturity and general mental health […] More-consistent people appear to be less neurotic, more controlled, more mature, and more positive in their relations with others (Donnellan, Conger, & Burzette, 2007; Roberts, Caspi, & Mofftt, 2001; Sherman, Nave, & Funder, 2010).”

“Despite the evidence for the malleability of personality […], it would be a mistake to conclude that change is easy. […] most people like their personalities pretty much the way they are, and do not see any reason for drastic change […] Acting in a way contrary to one’s traits takes effort and can be exhausting […] Second, people have a tendency to blame negative experiences and failures on external forces rather than recognizing the role of their own personality. […] Third, people generally like their lives to be consistent and predictable […] Change requires learning new skills, going new places, meeting new people, and acting in unaccustomed ways. That can make it uncomfortable. […] personality change has both a downside and an upside. […] people tend to like others who are “judgeable,” who are easy to understand, predict, and relate to. But when they don’t know what to expect or how to predict what a person will do, they are more likely to avoid that person. […] Moreover, if one’s personality is constantly changing, then it will be difficult to choose consistent goals that can be pursued over the long term.”

“There is no doubt that people change their behavior from one situation to the next. This obvious fact has sometimes led to the misunderstanding that personality consistency somehow means “acting the same way all the time.” But that’s not what it means at all. […] It is individual differences in behavior that are maintained across situations, not how much a behavior is performed. […] as the effect of the situation gets stronger, the effect of the person tends to get weaker, and vice versa. […] any fair reading of the research literature make one thing abundantly clear: When it comes to personality, one size does not fit all. People really do act differently from each other. Even when they are all in the same situation, some individuals will be more sociable, nervous, talkative, or active than others. And when the situation changes, those differences will still be there […] the evidence is overwhelming that people are psychologically different from one another, that personality traits exist, that people’s impressions of each other’s personalities are based on reality more than cognitive error, and that personality traits affect important life outcomes […] it is […] important to put the relative role of personality traits and situations into perspective. Situational variables are relevant to how people will act under specific circumstances. Personality traits are better for describing how people act in general […] A sad legacy of the person-situation debate is that many psychologists became used to thinking of the person and the situation as opposing forces […] It is much more accurate to see persons and situations as constantly interacting to produce behavior together. […] Persons and situations interact in three major ways […] First, the effect of a personality variable may depend on the situation, or vice versa. […] Certain types of people go to or find themselves in different types of situations. This is the second kind of person-situation interaction. […] The third kind of interaction stems from the way people change situations by virtue of what they do in them”.

“Shy people are often lonely and may deeply wish to have friends and normal social interactions, but are so fearful of the process of social involvement that they become isolated. In some cases, they won’t ask for help when they need it, even when someone who could easily solve their problem is nearby […]. Because shy people spend a lot of time by themselves, they deny themselves the opportunity to develop normal social skills. When they do venture out, they are so out of practice they may not know how to act. […] A particular problem for shy people is that, typically, others do not perceive them as shy. Instead, to most observers, they seem cold and aloof. […] shy people generally are not cold and aloof, or at least they do not mean to be. But that is frequently how they are perceived. That perception, in turn, affects the lives of shy people in important negative ways and is part of a cycle that perpetuates shyness […] the judgments of others are an important part of the social world and can have a significant effect on personality and life. […] Judgments of others can also affect you through “self-fulfilling prophecies,” more technically known as expectancy effects.1 These effects can affect both intellectual performance and social behavior.”

“Because people constantly make personality judgments, and because these judgments are consequential, it would seem important to know when and to what degree these judgments are accurate. […] [One relevant] method is called convergent validation. […] Convergent validation is achieved by assembling diverse pieces of information […] that “converge” on a common conclusion […] The more items of diverse information that converge, the more confident the conclusion […] For personality judgments, the two primary converging criteria are interjudge agreement and behavioral prediction. […] psychological research can evaluate personality judgments by asking two questions […] (1) Do the judgments agree with one another? (2) Can they predict behavior? To the degree the answers are Yes, the judgments are probably accurate.”

“In general, judges [of personality] will reach more accurate conclusions if the behaviors they observe are closely related to the traits they are judging. […] A moderator of accuracy […] is a variable that changes the correlation between a judgment and its criterion. Research on accuracy has focused primarily on four potential moderators: properties (1) of the judge, (2) of the target (the person who is judged), (3) of the trait that is judged, and (4) of the information on which the judgment is based. […] Do people know whether they are good judges of personality? The answer appears to be both no and yes […]. No, because people who describe themselves as good judges, in general, are no better than those who rate themselves as poorer in judgmental ability. But the answer is yes, in another sense. When asked which among several acquaintances they can judge most accurately, most people are mostly correct. In other words, we can tell the difference between people who we can and cannot judge accurately. […] Does making an extra effort to be accurate help? Research results so far are mixed.”

“When it comes to accurate judgment, who is being judged might be even more important than who is doing the judging. […] People differ quite a lot in how accurately they can be judged. […] “Judgable” people are those about whom others reach agreement most easily, because they are the ones whose behavior is most predictable from judgments of their personalities […] The behavior of judgable people is organized coherently; even acquaintances who know them in separate settings describe essentially the same person. Furthermore, the behavior of such people is consistent; what they do in the future can be predicted from what they have done in the past. […] Theorists have long postulated that it is psychologically healthy to conceal as little as possible from those around you […]. If you exhibit a psychological façade that produces large discrepancies between the person “inside” and the person you display “outside,” you may feel isolated from the people around you, which can lead to unhappiness, hostility, and depression. Acting in a way that is contrary to your real personality takes effort, and can be psychologically tiring […]. Evidence even suggests that concealing your emotions may be harmful to physical health“.

“All traits are not created equal — some are much easier to judge accurately than others. For example, more easily observed traits, such as “talkativeness,” “sociability,” and other traits related to extraversion, are judged with much higher levels of interjudge agreement than are less visible traits, such as cognitive and ruminative styles and habits […] To find out about less visible, more internal traits like beliefs or tendencies to worry, self-reports […] are more informative […] [M]ore information is usually better, especially when judging certain traits. […] Quantity is not the only important variable concerning information. […] it can be far more informative to observe a person in a weak situation, in which different people do different things, than in a strong situation, in which social norms restrict what people do […] The best situation for judging someone’s personality is one that brings out the trait you want to judge. To evaluate a person’s approach toward his work, the best thing to do is to observe him working. To evaluate a person’s sociability, observations at a party would be more informative […] The accurate judgment of personality, then, depends on both the quantity and the quality of the information on which it is based. More information is generally better, but it is just as important for the information to be relevant to the traits that one is trying to judge.”

“In order to get from an attribute of an individual’s personality to an accurate judgment of that trait, four things must happen […]. First, the person being judged must do something relevant; that is, informative about the trait to be judged. Second, this information must be available to a judge. Third, this judge must detect this information. Fourth and fnally, the judge must utilize this information correctly. […] If the process fails at any step — the person in question never does something relevant, or does it out of sight of the judge, or the judge doesn’t notice, or the judge makes an incorrect interpretation — accurate personality judgment will fail. […] Traditionally, efforts to improve accuracy have focused on attempts to get judges to think better, to use good logic and avoid inferential errors. These efforts are worthwhile, but they address only one stage — utilization — out of the four stages of accurate personality judgment. Improvement could be sought at the other stages as well […] Becoming a better judge of personality […] involves much more than “thinking better.” You should also try to create an interpersonal environment where other people can be themselves and where they feel free to let you know what is really going on.”

July 5, 2017 Posted by | Books, Psychology | Leave a comment

Melanoma therapeutic strategies that select against resistance

A short lecture, but interesting:

If you’re not an oncologist, these two links in particular might be helpful to have a look at before you start out: BRAF (gene) & Myc. A very substantial proportion of the talk is devoted to math and stats methodology (which some people will find interesting and others …will not).

July 3, 2017 Posted by | Biology, Cancer/oncology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

The Antarctic

“A very poor book with poor coverage, mostly about politics and history (and a long collection of names of treaties and organizations). I would definitely not have finished it if it were much longer than it is.”

That was what I wrote about the book in my goodreads review. I was strongly debating whether or not to blog it at all, but I decided in the end to just settle for some very lazy coverage of the book, only consisting of links to content covered in the book. I only cover the book here to at least have some chance of remembering which kinds of things were covered in the book later on.

If you’re interested enough in the Antarctic to read a book about it, read Scott’s Last Expedition instead of this one (here’s my goodreads review of Scott).

Links:

Antarctica (featured).
Antarctic Convergence.
Antarctic Circle.
Southern Ocean.
Antarctic Circumpolar Current.
West Antarctic Ice Sheet.
East Antarctic Ice Sheet.
McMurdo Dry Valleys.
Notothenioidei.
Patagonian toothfish.
Antarctic krill.
Fabian Gottlieb von Bellingshausen.
Edward Bransfield.
James Clark Ross.
United States Exploring Expedition.
Heroic Age of Antarctic Exploration (featured).
Nimrod Expedition (featured).
Roald Amundsen.
Wilhelm Filchner.
Japanese Antarctic Expedition.
Terra Nova Expedition (featured).
Lincoln Ellsworth.
British Graham Land expedition.
German Antarctic Expedition (1938–1939).
Operation Highjump.
Operation Windmill.
Operation Deep Freeze.
Commonwealth Trans-Antarctic Expedition.
Caroline Mikkelsen.
International Association of Antarctica Tour Operators.
Territorial claims in Antarctica.
International Geophysical Year.
Antarctic Treaty System.
Operation Tabarin.
Scientific Committee on Antarctic Research.
United Nations Convention on the Law of the Sea.
Convention on the Continental Shelf.
Council of Managers of National Antarctic Programs.
British Antarctic Survey.
International Polar Year.
Antarctic ozone hole.
Gamburtsev Mountain Range.
Pine Island Glacier (‘good article’).
Census of Antarctic Marine Life.
Lake Ellsworth Consortium.
Antarctic fur seal.
Southern elephant seal.
Grytviken (whaling-related).
International Convention for the Regulation of Whaling.
International Whaling Commission.
Ocean Drilling Program.
Convention on the Regulation of Antarctic Mineral Resource Activities.
Agreement on the Conservation of Albatrosses and Petrels.

July 3, 2017 Posted by | Biology, Books, Geography, Geology, History, Wikipedia | Leave a comment

Stars

“Every atom of our bodies has been part of a star, and every informed person should know something of how the stars evolve.”

I gave the book three stars on goodreads. At times it’s a bit too popular-science-y for me, and I think the level of coverage is a little bit lower than that of some of the other physics books in the ‘A Very Brief Introduction‘ series by Oxford University Press, but on the other hand it did teach me some new things and explained some other things I knew about but did not fully understand before and I’m well aware that it can be really hard to strike the right balance when writing books like these. I don’t like it when authors employ analogies instead of equations to explain stuff, but on the other hand I’ve seen some of the relevant equations before, e.g. in the context of IAS lectures, so I was okay with skipping some of the math because I know how the math here can really blow up in your face fast – and it’s not like this book has no math or equations, but I think it’s the kind of math most people should be able to deal with. It’s a decent introduction to the topic, and I must admit I have yet really to be significantly disappointed in a book from the physics part of this OUP series – they’re good books, readable and interesting.

Below I have added some quotes and observations from the book, as well as some relevant links to material or people covered in the book. Some of the links below I have also added previously when covering other books in the physics series, but I do not really care about that as I try to cover each book separately; the two main ideas behind adding links of this kind are: 1) to remind me which topics (…which I was unable to cover in detail in the post using quotes, because there’s too much stuff to cover in the book for that to make sense…) were covered in the book, and: 2) to give people who might be interested in reading the book an idea of which topics are covered therein; if I neglected to add relevant links simply because such topics were also covered in other books I’ve covered here, the link collection would not accomplish what I’d like it to accomplish. The link collection was gathered while I was reading the book (I was bookmarking relevant wiki articles along the way while reading the book), whereas the quotes included in the post were only added to the post after I had finished adding the links from the link collection; I am well aware that some topics covered in the quotes of the book are also covered in the link collection, but I didn’t care enough about this ‘double coverage of topics’ to remove those links that refer to material also covered in my quotes in this post from the link collection.

I think the part of the book coverage related to finding good quotes to include in this post was harder than it has been in the context of some of the other physics books I’ve covered recently, because the author goes into quite some detail explaining some specific dynamics of star evolution which are not easy to boil down to a short quote which is still meaningful to people who do not know the context. The fact that he does go into those details was of course part of the reason why I liked the book.

“[W]e cannot consider heat energy in isolation from the other large energy store that the Sun has – gravity. Clearly, gravity is an energy source, since if it were not for the resistance of gas pressure, it would make all the Sun’s gas move inwards at high speed. So heat and gravity are both potential sources of energy, and must be related by the need to keep the Sun in equilibrium. As the Sun tries to cool down, energy must be swapped between these two forms to keep the Sun in balance […] the heat energy inside the Sun is not enough to spread all of its contents out over space and destroy it as an identifiable object. The Sun is gravitationally bound – its heat energy is significant, but cannot supply enough energy to loosen gravity’s grip, and unbind the Sun. This means that when pressure balances gravity for any system (as in the Sun), the total heat energy T is always slightly less than that needed (V) to disperse it. In fact, it turns out to be exactly half of what would be needed for this dispersal, so that 2T + V = 0, or V = −2 T. The quantities T and V have opposite signs, because energy has to be supplied to overcome gravity, that is, you have to use T to try to cancel some of V. […] you need to supply energy to a star in order to overcome its gravity and disperse all of its gas to infinity. In line with this, the star’s total energy (thermal plus gravitational) is E = T + V = −T, that is, the total energy is minus its thermal energy, and so is itself negative. That is, a star is a gravitationally bound object. Whenever the system changes slowly enough that pressure always balances gravity, these two energies always have to be in this 1:2 ratio. […] This reasoning shows that cooling, shrinking, and heating up all go together, that is, as the Sun tries to cool down, its interior heats up. […] Because E = –T, when the star loses energy (by radiating), making its total energy E more negative, the thermal energy T gets more positive, that is, losing energy makes the star heat up. […] This result, that stars heat up when they try to cool, is central to understanding why stars evolve.”

“[T]he whole of chemistry is simply the science of electromagnetic interaction of atoms with each other. Specifically, chemistry is what happens when electrons stick atoms together to make molecules. The electrons doing the sticking are the outer ones, those furthest from the nucleus. The physical rules governing the arrangement of electrons around the nucleus mean that atoms divide into families characterized by their outer electron configurations. Since the outer electrons specify the chemical properties of the elements, these families have similar chemistry. This is the origin of the periodic table of the elements. In this sense, chemistry is just a specialized branch of physics. […] atoms can combine, or react, in many different ways. A chemical reaction means that the electrons sticking atoms together are rearranging themselves. When this happens, electromagnetic energy may be released, […] or an energy supply may be needed […] Just as we measured gravitational binding energy as the amount of energy needed to disperse a body against the force of its own gravity, molecules have electromagnetic binding energies measured by the energies of the orbiting electrons holding them together. […] changes of electronic binding only produce chemical energy yields, which are far too small to power stars. […] Converting hydrogen into helium is about 15 million times more effective than burning oil. This is because strong nuclear forces are so much more powerful than electromagnetic forces.”

“[T]here are two chains of reactions which can convert hydrogen to helium. The rate at which they occur is in both cases quite sensitive to the gas density, varying as its square, but extremely sensitive to the gas temperature […] If the temperature is below a certain threshold value, the total energy output from hydrogen burning is completely negligible. If the temperature rises only slightly above this threshold, the energy output becomes enormous. It becomes so enormous that the effect of all this energy hitting the gas in the star’s centre is life-threatening to it. […] energy is related to mass. So being hit by energy is like being hit by mass: luminous energy exerts a pressure. For a luminosity above a certain limiting value related to the star’s mass, the pressure will blow it apart. […] The central temperature of the Sun, and stars like it, must be almost precisely at the threshold value. It is this temperature sensitivity which fixes the Sun’s central temperature at the value of ten million degrees […] All stars burning hydrogen in their centres must have temperatures close to this value. […] central temperature [is] roughly proportional to the ratio of mass to radius [and this means that] the radius of a hydrogen-burning star is approximately proportional to its mass […] You might wonder how the star ‘knows’ that its radius is supposed to have this value. This is simple: if the radius is too large, the star’s central temperature is too low to produce any nuclear luminosity at all. […] the star will shrink in an attempt to provide the luminosity from its gravitational binding energy. But this shrinking is just what it needs to adjust the temperature in its centre to the right value to start hydrogen burning and produce exactly the right luminosity. Similarly, if the star’s radius is slightly too small, its nuclear luminosity will grow very rapidly. This increases the radiation pressure, and forces the star to expand, again back to the right radius and so the right luminosity. These simple arguments show that the star’s structure is self-adjusting, and therefore extremely stable […] The basis of this stability is the sensitivity of the nuclear luminosity to temperature and so radius, which controls it like a thermostat.”

“Hydrogen burning produces a dense and growing ball of helium at the star’s centre. […] the star has a weight problem to solve – the helium ball feels its own weight, and that of all the rest of the star as well. A similar effect led to the ignition of hydrogen in the first place […] we can see what happens as the core mass grows. Let’s imagine that the core mass has doubled. Then the core radius also doubles, and its volume grows by a factor 2 × 2 × 2 = 8. This is a bigger factor than the mass growth, so the density is 2/(2 × 2 × 2) = 1/4 of its original value. We end with the surprising result that as the helium core mass grows in time, its central number density drops. […] Because pressure is proportional to density, the central pressure of the core drops also […] Since the density of the hydrogen envelope does not change over time, […] the helium core becomes less and less able to cope with its weight problem as its mass increases. […] The end result is that once the helium core contains more than about 10% of the star’s mass, its pressure is too low to support the weight of the star, and things have to change drastically. […] massive stars have much shorter main-sequence lifetimes, decreasing like the inverse square of their masses […] A star near the minimum main-sequence mass of one-tenth of the Sun’s has an unimaginably long lifetime of almost 1013 years, nearly a thousand times the Sun’s. All low-mass stars are still in the first flush of youth. This is the fundamental fact of stellar life: massive stars have short lives, and low-mass stars live almost forever – certainly far longer than the current age of the Universe.”

“We have met all three […] timescales [see links below – US] for the Sun. The nuclear time is ten billion years, the thermal timescale is thirty million years, and the dynamical one […] just half an hour. […] Each timescale says how long the star takes to react to changes of the given type. The dynamical time tells us that if we mess up the hydrostatic balance between pressure and weight, the star will react by moving its mass around for a few dynamical times (in the Sun’s case, a few hours) and then settle down to a new state in which pressure and weight are in balance. And because this time is so short compared with the thermal time, the stellar material will not have lost or gained any significant amount of heat, but simply carried this around […] although the star quickly finds a new hydrostatic equilibrium, this will not correspond to thermal equilibrium, where heat moves smoothly outwards through the star at precisely the rate determined by the nuclear reactions deep in the centre. Instead, some bits of the star will be too cool to pass all this heat on outwards, and some will be too hot to absorb much of it. Over a thermal timescale (a few tens of millions of years in the Sun), the cool parts will absorb the extra heat they need from the stellar radiation field, and the hot parts rid themselves of the excess they have, until we again reach a new state of thermal equilibrium. Finally, the nuclear timescale tells us the time over which the star synthesizes new chemical elements, radiating the released energy into space.”

“[S]tars can end their lives in just one of three possible ways: white dwarf, neutron star, or black hole.”

“Stars live a long time, but must eventually die. Their stores of nuclear energy are finite, so they cannot shine forever. […] they are forced onwards through a succession of evolutionary states because the virial theorem connects gravity with thermodynamics and prevents them from cooling down. So main-sequence dwarfs inexorably become red giants, and then supergiants. What breaks this chain? Its crucial link is that the pressure supporting a star depends on how hot it is. This link would snap if the star was instead held up by a pressure which did not care about its heat content. Finally freed from the demand to stay hot to support itself, a star like this would slowly cool down and die. This would be an endpoint for stellar evolution. […] Electron degeneracy pressure does not depend on temperature, only density. […] one possible endpoint of stellar evolution arises when a star is so compressed that electron degeneracy is its main form of pressure. […] [Once] the star is a supergiant […] a lot of its mass is in a hugely extended envelope, several hundred times the Sun’s radius. Because of this vast size, the gravity tying the envelope to the core is very weak. […] Even quite small outward forces can easily overcome this feeble pull and liberate mass from the envelope, so a lot of the star’s mass is blown out into space. Eventually, almost the entire remaining envelope is ejected as a roughly spherical cloud of gas. The core quickly exhausts the thin shell of nuclear-burning material on its surface. Now gravity makes the core contract in on itself and become denser, increasing the electron degeneracy pressure further. The core ends as an extremely compact star, with a radius similar to the Earth’s, but a mass similar to the Sun, supported by this pressure. This is a white dwarf. […] Even though its surface is at least initially hot, its small surface means that it is faint. […] White dwarfs cannot start nuclear reactions, so eventually they must cool down and become dark, cold, dead objects. But before this happens, they still glow from the heat energy left over from their earlier evolution, slowly getting fainter. Astronomers observe many white dwarfs in the sky, suggesting that this is how a large fraction of all stars end their lives. […] Stars with an initial mass more than about seven times the Sun’s cannot end as white dwarfs.”

“In many ways, a neutron star is a vastly more compact version of a white dwarf, with the fundamental difference that its pressure arises from degenerate neutrons, not degenerate electrons. One can show that the ratio of the two stellar radii, with white dwarfs about one thousand times bigger than the 10 kilometres of a neutron star, is actually just the ratio of neutron to electron mass.”

“Most massive stars are not isolated, but part of a binary system […]. If one is a normal star, and the other a neutron star, and the binary is not very wide, there are ways for gas to fall from the normal star on to the neutron star. […] Accretion on to very compact objects like neutron stars almost always occurs through a disc, since the gas that falls in always has some rotation. […] a star’s luminosity cannot be bigger than the Eddington limit. At this limit, the pressure of the radiation balances the star’s gravity at its surface, so any more luminosity blows matter off the star. The same sort of limit must apply to accretion: if this tries to make too high a luminosity, radiation pressure will tend to blow away the rest of the gas that is trying to fall in, and so reduce the luminosity until it is below the limit. […] a neutron star is only 10 kilometres in radius, compared with the 700,000 kilometres of the Sun. This can only happen if this very small surface gets very hot. The surface of a healthily accreting neutron star reaches about 10 million degrees, compared with the 6,000 or so of the Sun. […] The radiation from such intensely hot surfaces comes out at much shorter wavelengths than the visible emission from the Sun – the surfaces of a neutron star and its accretion disc emit photons that are much more energetic than those of visible light. Accreting neutron stars and black holes make X-rays.”

“[S]tar formation […] is harder to understand than any other part of stellar evolution. So we use our knowledge of the later stages of stellar evolution to help us understand star formation. Working backwards in this way is a very common procedure in astronomy […] We know much less about how stars form than we do about any later part of their evolution. […] The cyclic nature of star formation, with stars being born from matter chemically enriched by earlier generations, and expelling still more processed material into space as they die, defines a cosmic epoch – the epoch of stars. The end of this epoch will arrive only when the stars have turned all the normal matter of the Universe into iron, and left it locked in dead remnants such as black holes.”

Stellar evolution.
Gustav Kirchhoff.
Robert Bunsen.
Joseph von Fraunhofer.
Spectrograph.
Absorption spectroscopy.
Emission spectrum.
Doppler effect.
Parallax.
Stellar luminosity.
Cecilia Payne-Gaposchkin.
Ejnar Hertzsprung/Henry Norris Russell/Hertzsprung–Russell diagram.
Red giant.
White dwarf (featured article).
Main sequence (featured article).
Gravity/Electrostatics/Strong nuclear force.
Pressure/Boyle’s law/Charles’s law.
Hermann von Helmholtz.
William Thomson (Kelvin).
Gravitational binding energy.
Thermal energy/Gravitational energy.
Virial theorem.
Kelvin-Helmholtz time scale.
Chemical energy/Bond-dissociation energy.
Nuclear binding energy.
Nuclear fusion.
Heisenberg’s uncertainty principle.
Quantum tunnelling.
Pauli exclusion principle.
Eddington limit.
Convection.
Electron degeneracy pressure.
Nuclear timescale.
Number density.
Dynamical timescale/free-fall time.
Hydrostatic equilibrium/Thermal equilibrium.
Core collapse.
Hertzsprung gap.
Supergiant star.
Chandrasekhar limit.
Core-collapse supernova (‘good article’).
Crab Nebula.
Stellar nucleosynthesis.
Neutron star.
Schwarzschild radius.
Black hole (‘good article’).
Roy Kerr.
Pulsar.
Jocelyn Bell.
Anthony Hewish.
Accretion/Accretion disk.
X-ray binary.
Binary star evolution.
SS 433.
Gamma ray burst.
Hubble’s law/Hubble time.
Cosmic distance ladder/Standard candle/Cepheid variable.
Star formation.
Pillars of Creation.
Jeans instability.
Initial mass function.

July 2, 2017 Posted by | Astronomy, Books, Chemistry, Physics | Leave a comment

A few diabetes papers of interest

i. An Inverse Relationship Between Age of Type 2 Diabetes Onset and Complication Risk and Mortality: The Impact of Youth-Onset Type 2 Diabetes.

“This study compared the prevalence of complications in 354 patients with T2DM diagnosed between 15 and 30 years of age (T2DM15–30) with that in a duration-matched cohort of 1,062 patients diagnosed between 40 and 50 years (T2DM40–50). It also examined standardized mortality ratios (SMRs) according to diabetes age of onset in 15,238 patients covering a wider age-of-onset range.”

“After matching for duration, despite their younger age, T2DM15–30 had more severe albuminuria (P = 0.004) and neuropathy scores (P = 0.003). T2DM15–30 were as commonly affected by metabolic syndrome factors as T2DM40–50 but less frequently treated for hypertension and dyslipidemia (P < 0.0001). An inverse relationship between age of diabetes onset and SMR was seen, which was the highest for T2DM15–30 (3.4 [95% CI 2.7–4.2]). SMR plots adjusting for duration show that for those with T2DM15–30, SMR is the highest at any chronological age, with a peak SMR of more than 6 in early midlife. In contrast, mortality for older-onset groups approximates that of the background population.”

“Young people with type 2 diabetes are likely to be obese, with a clustering of unfavorable cardiometabolic risk factors all present at a very early age (3,4). In adolescents with type 2 diabetes, a 10–30% prevalence of hypertension and an 18–54% prevalence of dyslipidemia have been found, much greater than would be expected in a population of comparable age (4).”

CONCLUSIONS The negative effect of diabetes on morbidity and mortality is greatest for those diagnosed at a young age compared with T2DM of usual onset.”

It’s important to keep base rates in mind when interpreting the reported SMRs, but either way this is interesting.

ii. Effects of Sleep Deprivation on Hypoglycemia-Induced Cognitive Impairment and Recovery in Adults With Type 1 Diabetes.

OBJECTIVE To ascertain whether hypoglycemia in association with sleep deprivation causes greater cognitive dysfunction than hypoglycemia alone and protracts cognitive recovery after normoglycemia is restored.”

CONCLUSIONS Hypoglycemia per se produced a significant decrement in cognitive function; coexisting sleep deprivation did not have an additive effect. However, after restoration of normoglycemia, preceding sleep deprivation was associated with persistence of hypoglycemic symptoms and greater and more prolonged cognitive dysfunction during the recovery period. […] In the current study of young adults with type 1 diabetes, the impairment of cognitive function that was associated with hypoglycemia was not exacerbated by sleep deprivation. […] One possible explanation is that hypoglycemia per se exerts a ceiling effect on the degree of cognitive dysfunction as is possible to demonstrate with conventional tests.”

iii. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up.

“The DCCT randomly assigned 1,441 patients with type 1 diabetes to intensive versus conventional therapy for a mean of 6.5 years, after which 93% were subsequently monitored during the observational Epidemiology of Diabetes Interventions and Complications (EDIC) study. Cardiovascular disease (nonfatal myocardial infarction and stroke, cardiovascular death, confirmed angina, congestive heart failure, and coronary artery revascularization) was adjudicated using standardized measures.”

“During 30 years of follow-up in DCCT and EDIC, 149 cardiovascular disease events occurred in 82 former intensive treatment group subjects versus 217 events in 102 former conventional treatment group subjects. Intensive therapy reduced the incidence of any cardiovascular disease by 30% (95% CI 7, 48; P = 0.016), and the incidence of major cardiovascular events (nonfatal myocardial infarction, stroke, or cardiovascular death) by 32% (95% CI −3, 56; P = 0.07). The lower HbA1c levels during the DCCT/EDIC statistically account for all of the observed treatment effect on cardiovascular disease risk.”

CONCLUSIONS Intensive diabetes therapy during the DCCT (6.5 years) has long-term beneficial effects on the incidence of cardiovascular disease in type 1 diabetes that persist for up to 30 years.”

I was of course immediately thinking that perhaps they had not considered if this might just be the result of the Hba1c differences achieved during the trial being maintained long-term (during follow-up), and so what they were doing was not as much measuring the effect of the ‘metabolic memory’ component as they were just measuring standard population outcome differences resulting from long-term Hba1c differences. But they (of course) had thought about that, and that’s not what’s going on here, which is what makes it particularly interesting:

“Mean HbA1c during the average 6.5 years of DCCT intensive therapy was ∼2% (20 mmol/mol) lower than that during conventional therapy (7.2 vs. 9.1% [55.6 vs. 75.9 mmol/mol], P < 0.001). Subsequently during EDIC, HbA1c differences between the treatment groups dissipated. At year 11 of EDIC follow-up and most recently at 19–20 years of EDIC follow-up, there was only a trivial difference between the original intensive and conventional treatment groups in the mean level of HbA1c

They do admittedly find a statistically significant difference between the Hba1cs of the two groups when you look at (weighted) Hba1cs long-term, but that difference is certainly nowhere near large enough to explain the clinical differences in outcomes you observe. Another argument in favour of the view that what’s driving these differences is metabolic memory is the observation that the difference in outcomes between the treatment and control groups are smaller now than they were ten years ago (my default would probably be to if anything expect the outcomes of the two groups to converge long-term if the samples were properly randomized to start with, but this is not the only plausible model and it sort of depends on how you model the risk function, as they also talk about in the paper):

“[T]he risk reduction of any CVD with intensive therapy through 2013 is now less than that reported previously through 2004 (30% [P = 0.016] vs. 47% [P = 0.005]), and likewise, the risk reduction per 10% lower mean HbA1c through 2013 was also somewhat lower than previously reported but still highly statistically significant (17% [P = 0.0001] vs. 20% [P = 0.001]).”

iv. Commonly Measured Clinical Variables Are Not Associated With Burden of Complications in Long-standing Type 1 Diabetes: Results From the Canadian Study of Longevity in Diabetes.

“The Canadian Study of Longevity in Diabetes actively recruited 325 individuals who had T1D for 50 or more years (5). Subjects completed a questionnaire, and recent laboratory tests and eye reports were provided by primary care physicians and eye specialists, respectively. […] The 325 participants were 65.5 ± 8.5 years old with diagnosis at age 10 years (interquartile range [IQR] 6.0, 16) and duration of 54.9 ± 6.4 years.”

“In univariable analyses, the following were significantly associated with a greater burden of complications: presence of hypertension, statin, aspirin and ACE inhibitor or ARB use, higher Problem Areas in Diabetes (PAID) and Geriatric Depression Scale (GDS) scores, and higher levels of triglycerides and HbA1c. The following were significantly associated with a lower burden of complications: current physical activity, higher quality of life, and higher HDL cholesterol.”

“In the multivariable analysis, a higher PAID score was associated with a greater burden of complications (risk ratio [RR] 1.15 [95% CI 1.06–1.25] for each 10-point-higher score). Aspirin and statin use were also associated with a greater burden of complications (RR 1.24 [95% CI 1.01–1.52] and RR 1.34 [95% CI 1.05–1.70], respectively) (Table 1), whereas HbA1c was not.”

“Our findings indicate that in individuals with long-standing T1D, burden of complications is largely not associated with historical characteristics or simple objective measurements, as associations with statistical significance likely reflect reverse causality. Notably, HbA1c was not associated with burden of complications […]. This further confirms that other unmeasured variables such as genetic, metabolic, or physiologic characteristics may best identify mechanisms and biomarkers of complications in long-standing T1D.”

v. Cardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study.

“Controlling cardiovascular disease (CVD) risk factors in diabetes mellitus (DM) reduces the number of CVD events, but the effects of multifactorial risk factor control are not well quantified. We examined whether being at targets for blood pressure (BP), LDL cholesterol (LDL-C), and glycated hemoglobin (HbA1c) together are associated with lower risks for CVD events in U.S. adults with DM. […] We studied 2,018 adults, 28–86 years of age with DM but without known CVD, from the Atherosclerosis Risk in Communities (ARIC) study, Multi-Ethnic Study of Atherosclerosis (MESA), and Jackson Heart Study (JHS). Cox regression examined coronary heart disease (CHD) and CVD events over a mean 11-year follow-up in those individuals at BP, LDL-C, and HbA1c target levels, and by the number of controlled risk factors.”

“Of 2,018 DM subjects (43% male, 55% African American), 41.8%, 32.1%, and 41.9% were at target levels for BP, LDL-C, and HbA1c, respectively; 41.1%, 26.5%, and 7.2% were at target levels for any one, two, or all three factors, respectively. Being at BP, LDL-C, or HbA1c target levels related to 17%, 33%, and 37% lower CVD risks and 17%, 41%, and 36% lower CHD risks, respectively (P < 0.05 to P < 0.0001, except for BP in CHD risk); those subjects with one, two, or all three risk factors at target levels (vs. none) had incrementally lower adjusted risks of CVD events of 36%, 52%, and 62%, respectively, and incrementally lower adjusted risks of CHD events of 41%, 56%, and 60%, respectively (P < 0.001 to P < 0.0001). Propensity score adjustment showed similar findings.”

“In our pooled analysis of subjects with DM in three large-scale U.S. prospective studies, the more factors among HbA1c, BP, and LDL-C that were at goal levels, the lower are the observed CHD and CVD risks (∼60% lower when all three factors were at goal levels compared with none). However, fewer than one-tenth of our subjects were at goal levels for all three factors. These findings underscore the value of achieving target or lower levels of these modifiable risk factors, especially in combination, among persons with DM for the future prevention of CHD and CVD events.”

In some studies you see very low proportions of patients reaching target variables because the targets are stupid (to be perfectly frank about it). The HbA1c target applied in this study was a level <53.0 mmol/mol (7%), which is definitely not crazy if the majority of the individuals included were type 2, which they almost certainly were. You can argue about the BP goal, but it’s obvious here that the authors are perfectly aware of the contentiousness of this variable.

It’s incidentally noteworthy – and the authors do take note of it, of course – that one of the primary results of this study (~60% lower risk when all risk factors reach the target goal), which includes a large proportion of African Americans in the study sample, is almost identical to the results of the Danish Steno-2 clinical trial, which included only Danish white patients (and the results of which I have discussed here on the blog before). In the Steno study, the result was “a 57% reduction in CVD death and a 59% reduction in CVD events.”

vi. Illness Identity in Adolescents and Emerging Adults With Type 1 Diabetes: Introducing the Illness Identity Questionnaire.

“The current study examined the utility of a new self-report questionnaire, the Illness Identity Questionnaire (IIQ), which assesses the concept of illness identity, or the degree to which type 1 diabetes is integrated into one’s identity. Four illness identity dimensions (engulfment, rejection, acceptance, and enrichment) were validated in adolescents and emerging adults with type 1 diabetes. Associations with psychological and diabetes-specific functioning were assessed.”

“A sample of 575 adolescents and emerging adults (14–25 years of age) with type 1 diabetes completed questionnaires on illness identity, psychological functioning, diabetes-related problems, and treatment adherence. Physicians were contacted to collect HbA1c values from patients’ medical records. Confirmatory factor analysis (CFA) was conducted to validate the IIQ. Path analysis with structural equation modeling was used to examine associations between illness identity and psychological and diabetes-specific functioning.”

“The first two identity dimensions, engulfment and rejection, capture a lack of illness integration, or the degree to which having diabetes is not well integrated as part of one’s sense of self. Engulfment refers to the degree to which diabetes dominates a person’s identity. Individuals completely define themselves in terms of their diabetes, which invades all domains of life (9). Rejection refers to the degree to which diabetes is rejected as part of one’s identity and is viewed as a threat or as unacceptable to the self. […] Acceptance refers to the degree to which individuals accept diabetes as a part of their identity, besides other social roles and identity assets. […] Enrichment refers to the degree to which having diabetes results in positive life changes, benefits one’s identity, and enables one to grow as a person (12). […] These changes can manifest themselves in different ways, including an increased appreciation for life, a change of life priorities, and a more positive view of the self (14).”

“Previous quantitative research assessing similar constructs has suggested that the degree to which individuals integrate their illness into their identity may affect psychological and diabetes-specific functioning in patients. Diabetes intruding upon all domains of life (similar to engulfment) [has been] related to more depressive symptoms and more diabetes-related problems […] In contrast, acceptance has been related to fewer depressive symptoms and diabetes-related problems and to better glycemic control (6,15). Similarly, benefit finding has been related to fewer depressive symptoms and better treatment adherence (16). […] The current study introduces the IIQ in individuals with type 1 diabetes as a way to assess all four illness identity dimensions.”

“The Cronbach α was 0.90 for engulfment, 0.84 for rejection, 0.85 for acceptance, and 0.90 for enrichment. […] CFA indicated that the IIQ has a clear factor structure, meaningfully differentiating four illness identity dimensions. Rejection was related to worse treatment adherence and higher HbA1c values. Engulfment was related to less adaptive psychological functioning and more diabetes-related problems. Acceptance was related to more adaptive psychological functioning, fewer diabetes-related problems, and better treatment adherence. Enrichment was related to more adaptive psychological functioning. […] the concept of illness identity may help to clarify why certain adolescents and emerging adults with diabetes show difficulties in daily functioning, whereas others succeed in managing developmental and diabetes-specific challenges.”

June 30, 2017 Posted by | Cardiology, Diabetes, Medicine, Psychology, Studies | Leave a comment

The Personality Puzzle (I)

I don’t really like this book, which is a personality psychology introductory textbook by David Funder. I’ve read the first 400 pages (out of 700), but I’m still debating whether or not to finish it, it just isn’t very good; the level of coverage is low, it’s very fluffy and the signal-to-noise ratio is nowhere near where I’d like it to be when I’m reading academic texts. Some parts of it frankly reads like popular science. However despite not feeling that the book is all that great I can’t justify not blogging it; stuff I don’t blog I tend to forget, and if I’m reading a mediocre textbook anyway I should at least try to pick out some of the decent stuff in there which keeps me reading and try to make it easier for me to recall that stuff later. Some parts of- and arguments/observations included in the book are in my opinion just plain silly or stupid, but I won’t go into these things in this post because I don’t really see what would be the point of doing that.

The main reason why I decided to give the book a go was that I liked Funder’s book Personality Judgment, which I read a few years ago and which deals with some topics also covered superficially in this text – it’s a much better book, in my opinion, at least as far as I can remember (…I have actually been starting to wonder if it was really all that great, if it was written by the same guy who wrote this book…), if you’re interested in these matters. If you’re interested in a more ‘pure’ personality psychology text, a significantly better alternative is Leary et al.‘s Handbook of Individual Differences in Social Behavior. Because of the multi-author format it also includes some very poor chapters, but those tend to be somewhat easy to identify and skip to get to the good stuff if you’re so inclined, and the general coverage is at a much higher level than that of this book.

Below I have added some quotes and observations from the first 150 pages of the book.

“A theory that accounts for certain things extremely well will probably not explain everything else so well. And a theory that tries to explain almost everything […] would probably not provide the best explanation for any one thing. […] different [personality psychology] basic approaches address different sets of questions […] each basic approach usually just ignores the topics it is not good at explaining.”

Personality psychology tends to emphasize how individuals are different from one another. […] Other areas of psychology, by contrast, are more likely to treat people as if they were the same or nearly the same. Not only do the experimental subfields of psychology, such as cognitive and social psychology, tend to ignore how people are different from each other, but also the statistical analyses central to their research literally put individual differences into their “error” terms […] Although the emphasis of personality psychology often entails categorizing and labeling people, it also leads the field to be extraordinarily sensitive — more than any other area of psychology — to the fact that people really are different.”

“If you want to “look at” personality, what do you look at, exactly? Four different things. First, and perhaps most obviously, you can have the person describe herself. Personality psychologists often do exactly this. Second, you can ask people who know the person to describe her. Third, you can check on how the person is faring in life. And finally, you can observe what the person does and try to measure her behavior as directly and objectively as possible. These four types of clues can be called S [self-judgments], I [informants], L [life], and B [behavior] data […] The point of the four-way classification […] is not to place every kind of data neatly into one and only one category. Rather, the point is to illustrate the types of data that are relevant to personality and to show how they all have both advantages and disadvantages.”

“For cost-effectiveness, S data simply cannot be beat. […] According to one analysis, 70 percent of the articles in an important personality journal were based on self-report (Vazire, 2006).”

“I data are judgments by knowledgeable “informants” about general attributes of the individual’s personality. […] Usually, close acquaintanceship paired with common sense is enough to allow people to make judgments of each other’s attributes with impressive accuracy […]. Indeed, they may be more accurate than self-judgments, especially when the judgments concern traits that are extremely desirable or extremely undesirable […]. Only when the judgments are of a technical nature (e.g., the diagnosis of a mental disorder) does psychological education become relevant. Even then, acquaintances without professional training are typically well aware when someone has psychological problems […] psychologists often base their conclusions on contrived tests of one kind or another, or on observations in carefully constructed and controlled environments. Because I data derive from behaviors informants have seen in daily social interactions, they enjoy an extra chance of being relevant to aspects of personality that affect important life outcomes. […] I data reflect the opinions of people who interact with the person every day; they are the person’s reputation. […] personality judgments can [however] be [both] unfair as well as mistaken […] The most common problem that arises from letting people choose their own informants — the usual practice in research — may be the “letter of recommendation effect” […] research participants may tend to nominate informants who think well of them, leading to I data that provide a more positive picture than might have been obtained from more neutral parties.”

“L data […] are verifable, concrete, real-life facts that may hold psychological significance. […] An advantage of using archival records is that they are not prone to the potential biases of self-report or the judgments of others. […] [However] L data have many causes, so trying to establish direct connections between specific attributes of personality and life outcomes is chancy. […] a psychologist can predict a particular outcome from psychological data only to the degree that the outcome is psychologically caused. L data often are psychologically caused only to a small degree.”

“The idea of B data is that participants are found, or put, in some sort of a situation, sometimes referred to as a testing situation, and then their behavior is directly observed. […] B data are expensive [and] are not used very often compared to the other types. Relatively few psychologists have the necessary resources.”

“Reliable data […] are measurements that reflect what you are trying to assess and are not affected by anything else. […] When trying to measure a stable attribute of personality—a trait rather than a state — the question of reliability reduces to this: Can you get the same result more than once? […] Validity is the degree to which a measurement actually reflects what one thinks or hopes it does. […] for a measure to be valid, it must be reliable. But a reliable measure is not necessarily valid. […] A measure that is reliable gives the same answer time after time. […] But even if a measure is the same time after time, that does not necessarily mean it is correct.”

“[M]ost personality tests provide S data. […] Other personality tests yield B data. […] IQ tests […] yield B data. Imagine trying to assess intelligence using an S-data test, asking questions such as “Are you an intelligent person?” and “Are you good at math?” Researchers have actually tried this, but simply asking people whether they are smart turns out to be a poor way to measure intelligence”.

“The answer an individual gives to any one question might not be particularly informative […] a single answer will tend to be unreliable. But if a group of similar questions is asked, the average of the answers ought to be much more stable, or reliable, because random fluctuations tend to cancel each other out. For this reason, one way to make a personality test more reliable is simply to make it longer.”

“The factor analytic method of test construction is based on a statistical technique. Factor analysis identifies groups of things […] that seem to have something in common. […] To use factor analysis to construct a personality test, researchers begin with a long list of […] items […] The next step is to administer these items to a large number of participants. […] The analysis is based on calculating correlation coefficients between each item and every other item. Many items […] will not correlate highly with anything and can be dropped. But the items that do correlate with each other can be assembled into groups. […] The next steps are to consider what the items have in common, and then name the factor. […] Factor analysis has been used not only to construct tests, but also to decide how many fundamental traits exist […] Various analysts have come up with different answers.”

[The Big Five were derived from factor analyses.]

The empirical strategy of test construction is an attempt to allow reality to speak for itself. […] Like the factor analytic approach described earlier, the frst step of the empirical approach is to gather lots of items. […] The second step, however, is quite different. For this step, you need to have a sample of participants who have already independently been divided into the groups you are interested in. Occupational groups and diagnostic categories are often used for this purpose. […] Then you are ready for the third step: administering your test to your participants. The fourth step is to compare the answers given by the different groups of participants. […] The basic assumption of the empirical approach […] is that certain kinds of people answer certain questions on personality inventories in distinctive ways. If you answer questions the same way as members of some occupational or diagnostic group did in the original derivation study, then you might belong to that group too. […] responses to empirically derived tests are difficult to fake. With a personality test of the straightforward, S-data variety, you can describe yourself the way you want to be seen, and that is indeed the score you will get. But because the items on empirically derived scales sometimes seem backward or absurd, it is difficult to know how to answer in such a way as to guarantee the score you want. This is often held up as one of the great advantages of the empirical approach […] [However] empirically derived tests are only as good as the criteria by which they are developed or against which they are cross-validated. […] the empirical correlates of item responses by which these tests are assembled are those found in one place, at one time, with one group of participants. If no attention is paid to item content, then there is no way to be confident that the test will work in a similar manner at another time, in another place, with different participants. […] A particular concern is that the empirical correlates of item response might change over time. The MMPI was developed decades ago and has undergone a major revision only once”.

“It is not correct, for example, that the significance level provides the probability that the substantive (non-null) hypothesis is true. […] the significance level gives the probability of getting the result one found if the null hypothesis were true. One statistical writer offered the following analogy (Dienes, 2011): The probability that a person is dead, given that a shark has bitten his head off, is 1.0. However, the probability that a person’s head was bitten off by a shark, given that he is dead, is much lower. The probability of the data given the hypothesis, and of the hypothesis given the data, is not the same thing. And the latter is what we really want to know. […] An effect size is more meaningful than a significance level. […] It is both facile and misleading to use the frequently taught method of squaring correlations if the intention is to evaluate effect size.”

June 30, 2017 Posted by | Books, Psychology, Statistics | Leave a comment