(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)
Someone writes in:
I have MS and take a disease-modifying drug called Copaxone. Sandoz developed a generic version of Copaxone and filed for FDA approval. Teva, the manufacturer of Copaxone, filed a petition opposing that approval (surprise!). FDA rejected Teva’s petitions and approved the generic.
My insurance company encouraged me to switch to the generic. Specifically, they increased the copay for the non-generic from $50 to $950 per month. That got my attention. My neurologist recommended against switching to the generic.
Consequently, I decided to try to review the FDA decision to see if I could get any insight into the basis for my neurologist’s recommendationdation.
What appeared on first glance to be a telling criticism of the Teva submission was a reference by the FDA to “non-standard statistical criteria” together with the FDA’s statement that reanalysis with standard practices found different results than those found by Teva. So, I looked up back at the Teva filing to identify the non-standard statistical criteria they used. If I found the right part of the Teva filing, they used R packages named ComBat and LIMMA—both empirical Bayes tools.
Now, it is possible that I have made a mistake and have not properly identified the statistical criteria that the FDA found wanting. I was unable to find any specific statement w.r.t. the “non-standard” statistics.
But, if empirical Bayes works better than older methods, then falling back to older methods would result in weaker inferences—and the rejection of the data from Teva.
It seems to me that this case raises interesting questions about the adoption and use of empirical Bayes. How should the FDA have treated the “non-standard statistical criteria”? More generally, is there a problem with getting regulatory agencies to accept Bayesian models? Maybe there is some issue here that would be appropriate for a masters student in public policy.
My correspondent included some relevant documentation:
The FDA docket files are available at http://www.regulations.gov/#!docketBrowser;rpp=25;po=0;dct=SR;D=FDA-2015-P-1050
The test below is from April 15, 2015 content/uploads/2016/07/Citizen_Petition_Denial_Letter_From_CDER_to_Teva_Pharmaceuticals.pdf”>FDA Denial Letter to Teva at pp. 41-42
Specifically, we concluded that the mouse splenocyte studies were poorly designed, contained a high level of residual batch bias, and used non-standard statistical criteria for assessing the presence of differentially expressed genes. When FDA reanalyzed the microarray data from one Teva study using industry standard practices and criteria, Copaxone and the comparator (Natco) product were found to have very similar effects on the efficacy-related pathways proposed for glatiramer acetate’s mechanism of action.
The image below is from the Teva Petition, July 2, 2014 at p. 60
And he adds:
My interest in this topic arose only because of my MS treatment—I have had no contact with Teva, Sandoz, or the FDA. And I approve of the insurance company’s action—that is, I think that creating incentives to encourage consumers to switch to generic medicines is usually a good idea.
I have no knowledge of any of this stuff, but the interaction of statistics and policy seems generally relevant so I thought I would share this with all of you.
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