effect size, P value, and Bayes odds

P value, effect size, and Bayes factor, and after one year

• 2021
• Cole (2020): Surprise! (P value vs. S value)
• KDnuggets by Bills (2021): Null Hypothesis Significance Testing is Still Useful
• 2019:Another wave beyond the P-value:
• Greenland (2019): Valid p-values behave exactly as they should: Some misleading criticisms of p-values and their resolution with s-values.
• Benjamin (2019): Three recommendations for improving the use of p-values
• Blume (2019): An introduction to second-generation p-values
• Tarran (2019): Is this the end of "statistical significance"?
• American Statistician (2019): A few articles relate to the p-value and alternative
• American Statistician (2019 Supl.): Statistical Inference in the 21st Century: A World Beyond p < 0.05
• Wasserstein (2019): Moving to a World Beyond “p < 0.05”
• McShane (2019): Abandon Statistical Significance
• Amrhein (2019): Comment of Nature: Scientists rise up against statistical significance,
• Editorial of Nature (2019): It’s time to talk about ditching statistical significance
• Different opinions
• Adams (2019): a trillion P values and counting
• Zhang (2019): P values akin to ‘beyond reasonable doubt’
• 2017: Robert Matthews published an article about the changes of statistical practice after ASA's statement on the statistical significance and p-values: The ASA's p-value statement, one year on. I agree on one highlight in this article: "It should be possible to establish firm general principles which focus on what is right rather than what is wrong"
• Abstract:Its aim was to stop the misuse of statistical significance testing. But Robert Matthews argues that little has changed in the 12 months since the ASA's intervention.
• Why do people use p-values instead of computing probability of the model given data?
• 2016: ASA releases statement on statistical significance and p-values (03/07/2016). The statement's six principles:
1. P-values can indicate how incompatible the data are with a specified statistical model.
2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
4. Proper inference requires full reporting and transparency.
5. A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.
6. By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.
• p-value is the likelihood of null hypothesis (H0), a conditional probability given H0:
• p(X ≥ x|H0) for a right tail event
• p(X ≤ x|H0) for a left tail event
• 2 * min{P(X ≥ x|H0), p(X ≤ x|H0)} for a 2-tail event
• α level, the level of significance, is a pre-defined probability of falsely rejecting the null hypothesis we accept. Typically, the statistical significance means the p-value < α level at 0.05.
• False discovery rate (Wikipedia)
• Many times, the p-value was incorrectly interpreted as one of posterior probabilities given the observed data, probabilities given the observed data, the false discovery rate (Colquhoun, 2014) or false positive rate.
• Storey (2003): The Positive False Discovery Rate: A Bayesian Interpretation and the q-Value
• Type I error (α) = 1 - specificity, Type II error (β) = 1 - sensitivity, Power = sensitivity = 1 - β
• Leek (2017). Five ways to fix statistics (Nature)
• Benjamin (2017). Redefine statistical significance
• Baker (2016). Statisticians issue warning over misuse of P values (pdf)
• Matloff (2016). 1) After 150 years, the ASA says no to p-value, 2) Further comments on the ASA manifesto, 3) P-values: the continuing saga
• Benjamini & Galili (2016). It's not the p-values' fault reflections on the recent ASA statement
• Kass (2016). Ten Simple Rules for Effective Statistical Practice. It acknowledged three websites:
• xkcd.com "for conveying statistical ideas with humor"
• Simply Statistics "for thoughtful commentary"
• FiveThirtyEight "for bringing statistics to the world (or at least to the media)".
• Aschwanden (2016). Science Isn’t Broken - It’s just a hell of a lot harder than we give it credit for
• Halsey (2015). The fickle P value generates irreproducible results
• Lazzeroni (2016). Solutions for quantifying P value uncertainty and replication power, and response of Halsey;
• Nuzzo (2013). Scientific method: Statistical errors
• Epimonitor (2016). Growing Concern About Statistical Errors Triggers Statement of P-Values
• Youngquist (2012). Part 19: What is a P value;
• GraphPad's Advice: how to interpret a small P value
• Frost (2014). How to Correctly Interpret P Values, Five Guidelines for Using P values
• Held (2010). A nomogram for P values
• Cumming (2012). Mind your confidence interval: how statistics skew research results
• Gumming (2013). The problem with p values: how significant are they, really?
• Ioannidis (2015). Why most published research findings are false
• Ranstam (2012). Why the P value culture is bad and confidence intervals a better alternative. Ranstam (2009) Sampling uncertainty in medical research. Austin (2002). A brief note on overlapping confidence intervals
• Aschwanden. Statisticians found one thing they can agree on: it's time to stop misusing P-values.
• Lew (2013). Give p a chance: significance testing is misunderstood
• Sullivan (2012). Using Effect Size—or Why the P Value Is Not Enough.
• Fraser (2016). Crisis in Science? or Crisis in Statistics! Mixed messages in Statistics with impact on Science
• Gelman (2014). Data-dependent analysis—a “garden of forking paths”—
• Capital of Statistics. 美国统计协会开始正式吐槽（错用）P值啦
• Wikipedia. Type I & II errors, sensitivity & specificity, effect size, Bayes factor.
• Hubers (2013). Measures of effect size in Stata 13
• Robert Coe (2002). It's the Effect Size, Stupid
• Andrew Gelman (2013). P value and statistical practice, Misunderstanding the p-value
• Simonsohn (2013). Just Post It: The Lesson From Two Cases of Fabricated Data Detected by Statistics Alone
• Goodman (2001). Of P-values and Bayes: a modest proposal
• Goodman (1999). Toward evidence-based medical statistics: the P value fallacy and The Bayes factor (notes: I like the Kass's formula, which uses the likelihood of alternative hypothesis as a numerator, and gives a BF without many decimals).  
• Kass (1995). Bayes factors [on the basis of observed data D, for the dichotomous conditions / models / hypotheses (H1 or H0), Bayes factor = p(D|H1)/p(D|H0)], the rules of thumb assess the quality of the evidence favoring one hypothesis over another as a reference:
• 1 to 3    (not worth more than a bare mention)
• 3 to 20   (positive)
• 20 to 150 (strong)
• > 150     (very strong)
• The odds form of Bayes's theorem for two hypotheses is convenient for calculating a Bayesian update of a chance.
• If there are mutually exclusive hypotheses H1 (= Alternative hypothesis, H1 = Disease) and H0 (= Null hypothesis = No disease) by given D (= Observed data/evidence = Postive test),
• p(H1 and D) = p(H1|D)*p(D) = p(D|H1)*p(H1)
• p(H1|D) = p(H1)*p(D|H1)/p(D)
• p(H0|D) = p(H0)*p(D|H0)/p(D)
• p(H1|D)/p(H0|D) = p(H1)/p(H0)*p(D|H1)/p(D|H0)
• p(H1|D)/[1-p(H1|D)] = p(H1)/[1-p(H1)]*p(D|H1)/p(D|H0)
• odds(H1|D) = odds(H1)*p(D|H1)/p(D|H0)
• Bayes factor (BF) = p(D|H1)/p(D|H0)
• Prior odds of H1 = odds(H1) = p(H1)/p(H0) = p(H1)/[1-p(H1)]
• Posterior odds of H1 given data = odds(H1|D) = p(H1|D)/p(H0|D)= (prior odds)*(BF or likelihood ratio)
• p(H1)=odds(H1)/[1+odds(H1)], p(H1|D)=odds(H1|D)/[1+odds(H1|D)]
• Suppose a person with disease had 3/4 possiblity of positive test, and a person without disease had 1/5 possibility of positive test. and we have no idea of p(D) of that person (the population), which means the prior proability is 50% and the prior odds(D) = p(D) / (1 - p(D) = 1:1. When a person had a positive test:
• the posterior odds (odds(D|Pos)) = (1:1) * (3/4 / 1/5) = 15/4 = 3.75 = (p(D) / (1-p(D)) * (p(Pos|D) / (1 - p(Pos|D)) = odds(D) * BF
• or, the probability had a disease given a positive test (p(D|Pos)) = ((3/4) * 0.5) / (0.5 * 3/4 + (1 - 0.5) * 1/5) = 15/19 = odds(D|Pos) / (1 + odds(D|Pos)) = .79
• Eight versions of Bayes's Theorem (pdf): simple, explicity, general, Sigma, canceled, odds, relative odds, and compound odds.
• Currell (2009). Chapter 7 Bayesian statistics
• O’Hagan (2006). Bayes factors, Deeks (2004). Diagnostic tests 4: likelihood ratios, Lindley (2004). Bayesian thoughts, Griffis (2006). Statistics and the Bayesian mind
• Berger (1988). The likelihood principle: a review, generalizations, and statistical implications
• Masson (2011): A tutorial on a practical Bayesian alternative to null-hypothesis significance testing 
• Faulkenberry (2018): A Simple Method for Teaching Bayesian Hypothesis Testing in the Brain and Behavioral Sciences
• Sellke (2001): Calibration of p Values for Testing Precise Null Hypotheses
• Berger (1987): Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence
• Bayesian p-value
• Nowozin (2015). Bayesian P-Values
• Lin (2009). Using Bayesian p-values in a 2 x 2 table of matched pairs with incompletely classified data
• NISS Webinar (2019): Alternatives to the Traditional P-value
  

verification or validation

Verification or Validation

• Verification and validation are quite different and used for different purposes. For example, verification answers, "Am I building the model right?", and validation answers, "Am I building the right model?"
• Wikipedia: Verification and validation of computer simulation models
• Wikipedia: Verification and validation
• Verification: The evaluation of whether or not a product, service, or system complies with a regulation, requirement, specification, or imposed condition. It is often an internal process
• Validation: The assurance that a product, service, or system meets the needs of the customer and other identified stakeholders. It often involves acceptance and suitability with external customers.

Curve-Fitting

XKCD: Curve-Fitting

Effect of population screening for type 2 diabetes

Effect of population screening for type 2 diabetes
by Medical Xpress August 24, 2017
"Three large trials published today in Diabetologia (the journal of the European Association for the Study of Diabetes) show that screening for type 2 diabetes and cardiovascular risk factors may not reduce mortality and cardiovascular disease in the general population. However, for individuals diagnosed with diabetes, screening is associated with a reduction in mortality and cardiovascular disease risk. " full text

Articles mentioned in this news:

• Simmons (2017). Effect of population screening for type 2 diabetes and cardiovascular risk factors on mortality rate and cardiovascular events: a controlled trial among 1,912,392 Danish adults
• Simmons (2017). Effect of screening for type 2 diabetes on risk of cardiovascular disease and mortality: a controlled trial among 139,075 individuals diagnosed with diabetes in Denmark between 2001 and 2009
• Feldman (2017). Screening for type 2 diabetes: do screen-detected cases fare better?
• Simmons (2017). Should we screen for type 2 diabetes among asymptomatic individuals? Yes
• Shaw (2017). Does the evidence support population-wide screening for type 2 diabetes? No

Battle of the data science Venn Diagrams

Battle of the Data Science Venn Diagrams
by David Taylor    

Data science is a rather fuzzily defined field; some of the definitions I've heard are:

• "Work that takes more programming skills than most statisticians have, and more statistics skills than a programmer has."
• "Applied statistics, but in San Francisco."
• "The field of people who decide to print 'Data Scientist' on their business cards and get a salary bump."

Personally, I've recently decided to avoid the controversy by calling myself a data spelunker. (Data miners are out of vogue anyway.)
As a field in search of a definition, it's unsurprising that you can find a lot of different attempts to define it.
As a field full of data nerds with a penchant for visualization, it's also unsurprising that a lot of them use Venn diagrams. (Fun fact: John Venn, who invented the eponymous diagrams, and his son filed a patent in 1909 for an lawn bowling machine.)... Full Text

The 21 greatest graduation speeches of the last 60 years

Vox: The 21 greatest graduation speeches of the last 60 years
by German Lopez on May 11, 2016
"Graduation speeches are the last opportunity for a high school or college to educate its students. It's unsurprising, then, that these institutions often pull in some of the world's most powerful people to leave an equally powerful impression on their students. Here are the best of those speeches and some of the sections that resonate the most..." (May 11, 2016)
To read and watch the full article on the Vox website here.

Cancer isn’t just bad luck

Cancer isn’t just bad luck
By Thomas Lumley

"
From Stuff, "Bad luck is responsible for two-thirds of adult cancer while the remaining cases are due to environmental risk factors and inherited genes, researchers from the Johns Hopkins Kimmel Cancer Center found."

...

So, in summary: the “two-thirds of cancers explained” is Just Wrong. Doing a mathematically correct calculation gives about one third. Doing a calculation that’s actually relevant to cancer in the population gives even smaller values. (update) That’s not to say that DNA replication errors are unimportant — the paper makes it clear that they are important.
"

The fulltext: Cancer isn't just bad luck

Data Cleaning is a critical part of the Data Science process

Data Cleaning is a critical part of the Data Science process
by David Smith

A New York Times article yesterday discovers the 80-20 rule: that 80% of a typical data science project is sourcing cleaning and preparing the data, while the remaining 20% is actual data analysis. The article gives short shrift to this important task by calling it "janitorial work", but whether you call it data munging, data wrangling or anything else, it's a critical part of the data science. I'm in agreement with Jeffrey Heer, professor of computer science at the University of Washington and a co-founder of Trifacta, who is quoted in the article saying,

     “It’s an absolute myth that you can send an algorithm over raw data and have insights pop up.”

As an illustration of this point, check out the essay by Julia Evans, Machine learning isn't Kaggle competitions (hat tip: Drew Conway). A Kaggle competion typically presents a nice, clean, regularized data set to the competitors, but this isn't representative of the real-world process of making predictions from data. As Julia points out:

     Cleaning up data to the point where you can work with it is a huge amount of work. If you’re trying to reconcile a lot of sources of data that you don’t control like in this flight search example, it can take 80% of your time.

While there are projects underway to help automate the data cleaning process and reduce the time it takes, the task of automation is made difficult by the fact that the process is as much art as science, and no two data preparation tasks are the same. That's why flexible, high-level langauages like R are a key part of the process. As Mitchell Sanders notes in a Tech Republic article,

     Data science requires a difficult blend of domain knowledge, math and statistics expertise, and code hacking skills. In particular, he suggests that expert knowledge of tools like R and SAS are critical. "If you can't use the tools, you can't analyze the data."

This is a critical step to gaining any kind of insight from data, which is why data scientists still command premium salaries today, according to data from Indeed.com.

uncertainty

When scientist and policy maker plus uncertainty

• Schmitt (2014). "The Pitfall of Uncertainty" (The Scientist)
• The Scientist published an opinion article about what uncertainty means to scientisit and policy maker. "...they must be crystal clear: science is about discovery and (decreasing) uncertainty, policymaking is about achieving consensus (if not certainty). Together, scientists and policymakers alike must strive to make responsible decisions for the benefit of society."
• NPR (2017). Alan Alda's Experiment: Helping Scientists Learn To Talk To The Rest Of Us
• Wikipedia:
  • Truth
  • Certainty
  • Wikiquote. Truth
• A intuitive figure of true vs truth of Starecat.com: This is TRUE, this is TRUE, this is TRUTH. I like this figure indeed. However, 'True' is a an adjective; 'truth' is a noun. I would like to change this into "This is TURE, this is TURE, this is THE THUTH" or "This is a FACT, this is a FACT, this is THE TRUTH"

    

Unconventional view of type 2 diabetes causation proposed

Unconventional view of type 2 diabetes causation proposed
Source: MedicalPress

At 85, Nobel laureate James D. Watson, the co-discoverer of the double-helix structure of DNA, continues to advance intriguing scientific ideas. His latest, a hypothesis on the causation of type 2 diabetes, is to appear 7 pm Thursday US time in the online pages of The Lancet, the prestigious British medical journal.
 
Watson's hypothesis suggests that diabetes, dementias, cardiovascular disease, and some cancers are linked to a failure to generate sufficient biological oxidants, called reactive oxygen species (ROS). Watson also argues the case for a better understanding of the role of exercise in helping to remedy this deficiency. ...
 

Debating obesity the disease

Debating obesity the disease
Source: MedScape

Follow the Money?

Fat Equals Sick: Is This About the Money?
Getting Paid for Treating Obesity, Now That It's a Disease
AMA: Diagnosis by Majority

Is It an Addiction? Can Obesity Be an Addiction?

Also Weighing in on the Disease Debate Obesity: It's a Risk! It's a Symptom! It's a Disease!
Obesity as a Disease? 'It's Academic'

News Behind the Perspectives AMA Declares Obesity a Disease
Obesity Disease Classification Will Help With Treatment, Docs Say

Diease or not disease, a diabetic question

Diease or not disease, a diabetic question

Last a few days, I read two diabetic related articles, which both are not my cup of tea; but I am really appreciating their thoughts and contrast.

• The Lie That's Killing Us: Pre-Diabetes (Riva Greenberg) on Huffpost defines the Type 2 diabetes as a diesase into four stages, which includes pre-diabetes as the stage 1.
• No Such Thing As Type 2 Diabetes? (Alice G. Walton) on Forbes is on the another side. The original article is published on Lancet (Is type 2 diabetes a category error? by Edwin AM Gale)

Both of authors are creative and thoughtful. Riva Greenberg is much more on the side of novelty/grassroot side (without enough evidence, to me) and Edwin Gale is much more on the side of classic/ivy tower (with typical theory/evidence, to me). I enjoy reading both articles.

Self-Medication in Animals

Self-Medication in Animals

A entertaining article from Science about how animals find the meds for themselves. It reminds me when we are using the evidences from clinic trials as the best source (?). Please don't forget the other types of studies.

"Finally, the study of animal medication will have direct relevance for human food production and health. Disease problems in agricultural organisms can worsen when humans interfere with the ability of animals to medicate. For example, increases in parasitism and disease in honeybees can be linked to selection by beekeepers for reduced resin deposition by their bees. A re-introduction of such behavior in managed bees would likely have great benefits for disease management. In addition, as self-medicating animals, humans still derive many of their medicines from natural products, and plants remain the most promising source of future drugs. Studies of animal medication may lead the way in discovering new drugs to relieve human suffering. "

Full text

Ecological fallacy & atomistic fallacy

Ecological fallacy & atomistic fallacy

As epidemiologists, usually we are told to avoid ecological fallacy, which is making a incorrect inference at lower level (individual) using the information at higher level (group); However, we may think little about the opposite fallacy, atomistic fallacy, which is making a incorrect inference at higher level (group) using the information at lower level (individual). Even worse, some may think the individual level information are only gold standard for policy making.

References

• Ecological fallacy - Wikipedia
• A Glossary for Multilevel Analysis - Epidemiologic Bulletin, PAHO. This article by A V Diez Roux also is on the J Epidemiol Community Health (2002)
• The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences. - S Schwartz (1994)

Obesity paradox

Obesity paradox

Today Medscape released a special report about 'Obesity Paradox':

Expert Commentary

License to Eat? Obesity and Lower Mortality
The Obesity Paradox: Does It Matter?
Obesity Bests Thinness in Hypertension? Check the Meds

The Current State of Obesity

2013: A Turning Point for Obesity?

Studies on the Obesity Paradox

Lesser Degrees of Excess Weight Do Not Increase Mortality
Obesity Paradox Also Applies to Stroke
Thinner May Not Always Be Better for People With Diabetes
No Evidence of Obesity Paradox After Drug-Eluting Stent Placement
Obesity Paradox Strengthened by New SCAAR Data in ACS

"YOUTH" - Samuel Ullman
Source: Samuel Ullman Museum

Youth is not a time of life; it is a state of mind; it is not a matter of rosy cheeks, red lips and supple knees; it is a matter of the will, a quality of the imagination, a vigor of the emotions; it is the freshness of the deep springs of life.

Youth means a temperamental predominance of courage over timidity of the appetite, for adventure over the love of ease. This often exists in a man of sixty more than a boy of twenty. Nobody grows old merely by a number of years. We grow old by deserting our ideals.

Years may wrinkle the skin, but to give up enthusiasm wrinkles the soul. Worry, fear, self-distrust bows the heart and turns the spirit back to dust.

Whether sixty or sixteen, there is in every human being's heart the lure of wonder, the unfailing child-like appetite of what's next, and the joy of the game of living. In the center of your heart and my heart there is a wireless station; so long as it receives messages of beauty, hope, cheer, courage and power from men and from the infinite, so long are you young.

When the aerials are down, and your spirit is covered with snows of cynicism and the ice of pessimism, then you are grown old, even at twenty, but as long as your aerials are up, to catch the waves of optimism, there is hope you may die young at eighty.

青  春 - 塞缪尔.乌尔曼

人生匆匆，青春不是易逝的一段。青春应是一种永恒的心态。满脸红光，嘴唇红润，腿脚灵活，这些都不是青春的全部。真正的青春啊，它是一种坚强的意志，是一种想象力的高品位，是感情充沛饱满，是生命之泉的清澈常新。

青春意味着勇敢战胜怯懦，青春意味着进取战胜安逸，年月的轮回就一定导致衰老吗？要知道呵，老态龙钟是因为放弃了对真理的追求。

无情岁月的流逝，留下了深深的皱纹，而热忱的丧失，会在深处打下烙印。焦虑、恐惧、自卑，终会使心情沮丧，意志消亡。

60也罢，16也罢，每个人的心田都应保持着不泯的意志，去探索新鲜的事物，去追求人生乐趣。我们的心中都应有座无线电台，只要不断地接受来自人类和上帝的美感、希望、勇气和力量，我们就会永葆青春。

倘若你收起天线，使自己的心灵蒙上玩世不恭的霜雪和悲观厌世的冰凌，即使你年方20，你已垂垂老矣；倘若你已经80高龄，临于辞世，若竖起天线去收听乐观进取的电波，你仍会青春焕发。 

Fifty Shades of Brown

Fifty Shades of Brown: The Evolving View of Fat
Source: Circulation

This is an editorial article of a PVAT (Perivascular adipose tissue) study supports fat is friend not foe. I have learned decades ago in my medical school that the BAT (brown adipose tissue) which is major thermogenetic fat for an infant, especially cold area of world without heat. Now, researchers find that adults also have the BAT a little bit here and a little bit there.

"...
The relationship between increased body mass index and risk for diabetes mellitus or cardiovascular disease is well established. Such observations have driven considerable interest into the nature of adipose tissue and what mechanisms might help explain how adipose tissue and specific aspects of adipocyte biology influence cardiometabolic disorders. For example, adipocytes are now recognized as a source of mediators released into the circulation, like the adipokines resistin and adiponectin, which can modulate inflammation, insulin sensitivity, and atherosclerosis. Other molecules released from adipocytes like free fatty acids and reactive oxygen species can also exert both local and distant effects that may be integral to the development of diabetes mellitus, atherosclerosis, and their complications. To an increasing extent, adipose tissue is now understood as an organ playing important physiological and pathological roles. Both the absence of fat, as with certain lipodystrophies, and excess adiposity are associated with diabetes mellitus, with mechanisms that appear to include infiltration of inflammatory cells into adipose tissue and the release of systemic mediators.
..."

Special issue on lipotoxicity

Special Issue on Lipotoxicity

Source: Biochimica et Biophysica Acta 2010;1801(3)

Editors: A. Vidal-Puig & R.Unger

“It was sometime in March 2009 when our colleague Fritz Spener first proposed a special issue of BBA Molecular and Cell Biology of Lipids focused on the concept of lipotoxicity and its relevance as an integrative pathogenic mechanism of the metabolic syndrome. Although we might have hesitated a little initially, this did not last long as we realised that: a) ours would be a unique, high quality publication addressing the topic in depth and globally; b) this is an important area of research with enormous implications for metabolic disease and, in our opinion, its relevance is underestimated and relatively unknown among both the biomedical community and general public; and c) the great opportunities offered by new technologies and experimental models to understand the role of lipotoxicity in common metabolic diseases makes this a very timely issue. And also, of course, we expected strong support from the “lipotoxic community”. Certainly we have not been disappointed. In fact our colleagues have provided enormous support and their generosity has made this issue viable. Our only regret is that we have not been able to involve as many of the key experts as we wanted due to space constrains and the time scale of the project, and we hope this will not be the cause of any lost friendships!”


Here is the special issue: Special issue on lipotoxicity.

This issue isn’t new. I have got the similar hypothesis after I attended a lecture by J. Denis McGarry in 2001 (In memory of Dr. John Denis McGarry. His article "What if Minkowski Had Been Ageusic?"  is on the wall of my office all the time). I put this special issue on my blog to remind me keeping work on this hypothesis.

In memory of Dr. John Denis McGarry

In memory of Dr. John Denis McGarry

Dr. Denis McGarry was a great teacher and sage. His lecture affects my way of thinking on diabetes. Unforgettable.

“In 1992, he published a famous paper in Science entitled “What if Minkowski Had Been Ageusic?” (1). In this paper he suggested that scientific concentration on abnormal glucose metabolism had masked the critical importance of abnormal fat metabolism, especially in type 2 diabetes. Subsequent to this paper there was a huge swing by investigators toward the key role of abnormal lipid metabolism in insulin resistance and lipotoxic damage to tissues as diverse as the heart and the β-cell of the pancreas.”

Please find the full article here.

Hedgehog reappears, loses to fox

Hedgehog reappears, loses to fox
International Journal of Epidemiology 2007; 36:3-10
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In a famous essay, Isaiah Berlin used a fragment from an ancient Greek poem to characterize '[O]ne of the deepest differences which divide writers and thinkers, and, it may be, human beings in general.' That fragment is: 'The fox knows many things, but the hedgehog knows one big thing.' He continued, [T]here exists a great chasm between those, on one side, who relate everything to a single central vision, one system less or more coherent or articulate, in terms of which they understand, think and feel ... and, on the other side, those who pursue many ends, often unrelated and even contradictory, connected, if at all, only in some de facto way ... The first kind of intellectual and artistic personality belongs to the hedgehogs, the second to the foxes.40 Hedgehogs are likely to think of prediction as a deductive exercise, whether based upon functionalism, free market economics or Marxism, whereas foxes are likely to make predictions based upon careful observations of particular cases. And studies of political forecasting indicate that foxes are better forecasters than hedgehogs, precisely because foxes are not committed to an overarching theory but are able to learn from their mistakes and remain open to new information. In a study of the forecasting accuracy of political experts, Philip Tetlock41 found that those who were least accurate looked very much like hedgehogs: '[T]hinkers who "know one big thing", aggressively extend the explanatory reach of that one big thing into new domains, display bristly impatience with those who "do not get it", and express considerable confidence that they are already pretty proficient forecasters, at least in the long term.'42 They are people who are likely to 'trivialize evidence that undercuts their preconceptions and to embrace evidence that reinforces their preconceptions.'43 Those who were more accurate 'look like foxes': [T]hinkers who know many small things (tricks of their trade), are skeptical of grand schemes, see explanation and prediction not as deductive exercises but rather as exercises in flexible 'ad hocery' that require stitching together diverse sources of information, and are rather diffident about their own forecasting  prowess, and ... rather dubious that the cloudlike subject of politics can by the object of a clocklike science.44 Foxes have a 'more balanced style of thinking about the world-a style of thought that elevates no thought above criticism.'45 Social epidemiology is more nearly akin to political forecasting than to physics. When considering the ssociations between sex, race and social roles on the one hand and health and disease on the other, accurate prediction is unlikely to rest upon deductive science and more likely to result from stitching together all that one can know about the context-institutional, cultural, political, epidemiological-in which particular populations live and work. Thus, social epidemiology is scientific as it reconstructs the past and explains the present, but it is not likely to be powerfully predictive. When it is successfully predictive, it is not likely to be because it is based upon deductions from scientifically valid generalizations that are true across time and place, but because analysts understand more or less intimately the people and places with which they are concerned, and because they can extrapolate sensibly from relevant experiences and groups elsewhere.