Monthly Archives: July 2014

Roger Berger on Stephen Senn’s “Blood Simple” with a response by Senn (Guest posts)

Roger BergerRoger L. Berger

School Director & Professor
School of Mathematical & Natural Science
Arizona State University

Comment on S. Senn’s post: Blood Simple? The complicated and controversial world of bioequivalence”(*)

First, I do agree with Senn’s statement that “the FDA requires conventional placebo-controlled trials of a new treatment to be tested at the 5% level two-sided but since they would never accept a treatment that was worse than placebo the regulator’s risk is 2.5% not 5%.” The FDA procedure essentially defines a one-sided test with Type I error probability (size) of .025. Why it is not just called this, I do not know. And if the regulators believe .025 is the appropriate Type I error probability, then perhaps it should be used in other situations, e.g., bioequivalence testing, as well.

Senn refers to a paper by Hsu and me (Berger and Hsu (1996)), and then attempts to characterize what we said. Unfortunately, I believe he has mischaracterized. Continue reading

Categories: bioequivalence, frequentist/Bayesian, PhilPharma, Statistics | Tags: , | 22 Comments

S. Senn: “Responder despondency: myths of personalized medicine” (Guest Post)

Stephen Senn

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Stephen Senn
Head, Methodology and Statistics Group
Competence Center for Methodology and Statistics (CCMS)
Luxembourg

Responder despondency: myths of personalized medicine

The road to drug development destruction is paved with good intentions. The 2013 FDA report, Paving the Way for Personalized Medicine  has an encouraging and enthusiastic foreword from Commissioner Hamburg and plenty of extremely interesting examples stretching back decades. Given what the report shows can be achieved on occasion, given the enthusiasm of the FDA and its commissioner, given the amazing progress in genetics emerging from the labs, a golden future of personalized medicine surely awaits us. It would be churlish to spoil the party by sounding a note of caution but I have never shirked being churlish and that is exactly what I am going to do. Continue reading

Categories: evidence-based policy, Statistics, Stephen Senn | 50 Comments

Continued:”P-values overstate the evidence against the null”: legit or fallacious?

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continued…

Categories: Bayesian/frequentist, CIs and tests, fallacy of rejection, highly probable vs highly probed, P-values, Statistics | 39 Comments

“P-values overstate the evidence against the null”: legit or fallacious? (revised)

0. July 20, 2014: Some of the comments to this post reveal that using the word “fallacy” in my original title might have encouraged running together the current issue with the fallacy of transposing the conditional. Please see a newly added Section 7.

Continue reading

Categories: Bayesian/frequentist, CIs and tests, fallacy of rejection, highly probable vs highly probed, P-values, Statistics | 71 Comments

Higgs discovery two years on (2: Higgs analysis and statistical flukes)

Higgs_cake-sI’m reblogging a few of the Higgs posts, with some updated remarks, on this two-year anniversary of the discovery. (The first was in my last post.) The following, was originally “Higgs Analysis and Statistical Flukes: part 2″ (from March, 2013).[1]

Some people say to me: “This kind of reasoning is fine for a ‘sexy science’ like high energy physics (HEP)”–as if their statistical inferences are radically different. But I maintain that this is the mode by which data are used in “uncertain” reasoning across the entire landscape of science and day-to-day learning (at least, when we’re trying to find things out)[2] Even with high level theories, the particular problems of learning from data are tackled piecemeal, in local inferences that afford error control. Granted, this statistical philosophy differs importantly from those that view the task as assigning comparative (or absolute) degrees-of-support/belief/plausibility to propositions, models, or theories.  Continue reading

Categories: Higgs, highly probable vs highly probed, P-values, Severity, Statistics | 14 Comments

Higgs Discovery two years on (1: “Is particle physics bad science?”)

Higgs_cake-s

July 4, 2014 was the two year anniversary of the Higgs boson discovery. As the world was celebrating the “5 sigma!” announcement, and we were reading about the statistical aspects of this major accomplishment, I was aghast to be emailed a letter, purportedly instigated by Bayesian Dennis Lindley, through Tony O’Hagan (to the ISBA). Lindley, according to this letter, wanted to know:

“Are the particle physics community completely wedded to frequentist analysis?  If so, has anyone tried to explain what bad science that is?”

Fairly sure it was a joke, I posted it on my “Rejected Posts” blog for a bit until it checked out [1]. (See O’Hagan’s “Digest and Discussion”) Continue reading

Categories: Bayesian/frequentist, fallacy of non-significance, Higgs, Lindley, Statistics | Tags: , , , , , | 4 Comments

Winner of June Palindrome Contest: Lori Wike

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Winner of June 2014 Palindrome Contest: First Second* Time Winner! Lori Wike

*Her April win is here

Palindrome:

Parsec? I overfit omen as Elba sung “I err on! Oh, honor reign!” Usable, sane motif revoices rap.

The requirement: A palindrome with Elba plus overfit. (The optional second word: “average” was not needed to win.)

Bio:

Lori Wike is principal bassoonist of the Utah Symphony and is on the faculty of the University of Utah and Westminster College. She holds a Bachelor of Music degree from the Eastman School of Music and a Master of Arts degree in Comparative Literature from UC-Irvine.

Continue reading

Categories: Announcement, Palindrome | Leave a comment

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