Anil Potti

A new front in the statistics wars? Peaceful negotiation in the face of so-called ‘methodological terrorism’

images-30I haven’t been blogging that much lately, as I’m tethered to the task of finishing revisions on a book (on the philosophy of statistical inference!) But I noticed two interesting blogposts, one by Jeff Leek, another by Andrew Gelman, and even a related petition on Twitter, reflecting a newish front in the statistics wars: When it comes to improving scientific integrity, do we need more carrots or more sticks? 

Leek’s post, from yesterday, called “Statistical Vitriol” (29 Sep 2016), calls for de-escalation of the consequences of statistical mistakes:

Over the last few months there has been a lot of vitriol around statistical ideas. First there were data parasites and then there were methodological terrorists. These epithets came from established scientists who have relatively little statistical training. There was the predictable backlash to these folks from their counterparties, typically statisticians or statistically trained folks who care about open source.
Continue reading

Categories: Anil Potti, fraud, Gelman, pseudoscience, Statistics

“What does it say about our national commitment to research integrity?”



There’s an important guest editorial by Keith Baggerly and C.K. Gunsalus in today’s issue of the Cancer Letter: Penalty Too Light” on the Duke U. (Potti/Nevins) cancer trial fraud*. Here are some excerpts.

publication date: Nov 13, 2015

Penalty Too Light

What does it say about our national commitment to research integrity that the Department of Health and Human Services’ Office of Research Integrity has concluded that a five-year ban on federal research funding for one individual researcher is a sufficient response to a case involving millions of taxpayer dollars, completely fabricated data, and hundreds to thousands of patients in invasive clinical trials?

This week, ORI released a notice of “final action” in the case of Anil Potti, M.D. The ORI found that Dr. Potti engaged in several instances of research misconduct and banned him from receiving federal funding for five years.

(See my previous post.)

The principles involved are important and the facts complicated. This was not just a matter of research integrity. This was also a case involving direct patient care and millions of dollars in federal and other funding. The duration and extent of deception were extreme. The case catalyzed an Institute of Medicine review of genomics in clinical trials and attracted national media attention.

If there are no further conclusions coming from ORI and if there are no other investigations under way—despite the importance of the issues involved and the five years that have elapsed since research misconduct investigation began, we do not know—a strong argument can be made that neither justice nor the research community have been served by this outcome. Continue reading

Categories: Anil Potti, fraud, science communication, Statistics

Findings of the Office of Research Misconduct on the Duke U (Potti/Nevins) cancer trial fraud: No one is punished but the patients

imgres-2Findings of Research Misconduct
A Notice by the Health and Human Services Dept
on 11/09/2015
AGENCY: Office of the Secretary, HHS.
ACTION: Notice.


SUMMARY: Notice is hereby given that the Office of Research Integrity 
(ORI) has taken final action in the following case:
    Anil Potti, M.D., Duke University School of Medicine: Based on the 
reports of investigations conducted by Duke University School of 
Medicine (Duke) and additional analysis conducted by ORI in its 
oversight review, ORI found that Dr. Anil Potti, former Associate 
Professor of Medicine, Duke, engaged in research misconduct in research 
supported by National Heart, Lung, and Blood Institute (NHLBI), 
National Institutes of Health (NIH), grant R01 HL072208 and National 
Cancer Institute (NCI), NIH, grants R01 CA136530, R01 CA131049, K12 
CA100639, R01 CA106520, and U54 CA112952.
    ORI found that Respondent engaged in research misconduct by 
including false research data in the following published papers, 
submitted manuscript, grant application, and the research record as 
specified in 1-3 below. Specifically, ORI found that: Continue reading 
Categories: Anil Potti, reproducibility, Statistical fraudbusting, Statistics

Your (very own) personalized genomic prediction varies depending on who else was around?


personalized medicine roulette

As if I wasn’t skeptical enough about personalized predictions based on genomic signatures, Jeff Leek recently had a surprising post about a “A surprisingly tricky issue when using genomic signatures for personalized medicine“.  Leek (on his blog Simply Statistics) writes:

My student Prasad Patil has a really nice paper that just came out in Bioinformatics (preprint in case paywalled). The paper is about a surprisingly tricky normalization issue with genomic signatures. Genomic signatures are basically statistical/machine learning functions applied to the measurements for a set of genes to predict how long patients will survive, or how they will respond to therapy. The issue is that usually when building and applying these signatures, people normalize across samples in the training and testing set.

….it turns out that this one simple normalization problem can dramatically change the results of the predictions. In particular, we show that the predictions for the same patient, with the exact same data, can change dramatically if you just change the subpopulations of patients within the testing set.

Here’s an extract from the paper,”Test set bias affects reproducibility of gene signatures“:

Test set bias is a failure of reproducibility of a genomic signature. In other words, the same patient, with the same data and classification algorithm, may be assigned to different clinical groups. A similar failing resulted in the cancellation of clinical trials that used an irreproducible genomic signature to make chemotherapy decisions (Letter (2011)).

This is a reference to the Anil Potti case:

Letter, T. C. (2011). Duke Accepts Potti Resignation; Retraction Process Initiated with Nature Medicine.

But far from the Potti case being some particularly problematic example (see here and here), at least with respect to test set bias, this article makes it appear that test set bias is a threat to be expected much more generally. Going back to the abstract of the paper: Continue reading

Categories: Anil Potti, personalized medicine, Statistics

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