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Viagra Causation Goes Limp

By James M. Beck and Mark Herrmann

Imagine this: The only published article in the medical literature reaching a statistically significant result concerning a drug and an outcome turns out not to have been what it seems. Rather, over a third of the subjects in the relevant category turn out to be misclassified. When properly reclassified, the statistical significance between the drug and the outcome goes away.

If that study had been sponsored by the drug company, and involved the benefit of the drug, imagine how the plaintiffs would have reacted. Would they demand that the drug be removed from the market? Would they seek punitive damages? Sanctions? A criminal investigation?

Of course they would. We see this kind of response just for results unearthed by new studies or supposed e-discovery violations -- let alone the publication of a ground-breaking, but false, study.

But what if the study happens to be published by a plaintiff's expert?

Hardly a peep. In fact, expect a belated attempt to make excuses for misstated data.

That's what just happened in the Viagra MDL. The last plaintiff's causation expert standing turns out to have published a study based upon data that was, at best, misclassified.

Kudos to the defendant for persistence in the face of a published, supposedly peer-reviewed article.

It's results like this that demonstrate why it may be worthwhile to seek discovery of the data that underlie even published studies.

So what exactly happened?

The study was suspicious anyway, since it consisted solely of telephone interviews with Viagra users who had been diagnosed with non-arteritic anterior ischemicoptic neuropathy (?NAION?) and a case-matched control group of the same size. Slip op. at 2.

The defendant subpoenaed the underlying data, but the expert, at the direction of the plaintiff's counsel, didn't produce anything. Slip op. at 4. That's one way to confirm suspicions. The defendant deposed the guy anyway, and after that the expert had to write the scientific journal and tell it about the deficiencies in the article. Slip op. at 4-5.

The biggest problem was that a significant portion of the patient data had been seriously miscoded. Specifically, eleven subjects were erroneously listed as "exposed" to Viagra when their telephone interviews indicated that they began taking Viagra after being diagnosed with NAION. As the court explained:

There are eleven instances where the date of first use on the original telephone survey forms is later than the date of NAION diagnosis on the same form. However, each of those individuals was still coded as exposed in Dr. McGwin?s [the expert's] electronic dataset. Dr. McGwin acknowledged that the statistics in the McGwin Study would have been different had those individuals (11 of 27 patients who reported Viagra or Cialis use) been coded as unexposed rather than as exposed.

Slip op. at 7.

Eleven of 27 is a pretty high error rate for anything. And every one of those errors just happened to occur in such a way that would bias the study in favor of a supposed association that otherwise has not been proven to exist.

One would think plaintiffs would be content simply to avoid the sanctions that they surely would have sought had the shoe been on the other foot. But they had chutzpah. They tried to explain away these "discrepencies" by arguing that (1) their own expert's survey forms were hearsay, and (2) some phantom researcher (never identified) supposedly went back and recontacted these persons and got differing information from what was recorded on the forms. Slip op. at 7-8.

The court was having none of it. It considered the forms to be admissible business records. Slip op. at 8. As for the supposed recontacting of the survey participants, plaintiffs eventually had to concede that they were pushing a fantasy -- there was not a scrap of evidence to prove that any recontacting had ever happened:

Plaintiffs have failed to produce any competent witness or documentary evidence to verify that such a step [recontacting survey participants] was actually taken. Indeed, as Plaintiffs concede, ?Dr. McGwin [their expert] was unable to authenticate any of the underlying documents. . . . Plaintiffs have not cited to any other admissible testimony from [anyone] who is able to verify that patients were recontacted.

Slip op. at 10. With admirable understatement, the court concluded "discrepancies between the dates of first use on the original survey forms and in the electronic dataset raise serious concerns about the reliability of the McGwin Study as originally published." Id.

But wait, there's more.

More than misclassifying more than a third of study participants in a way that biases the results in favor of the conclusion that the expert is paid to reach? More than making up cock-and-bull excuses about phantom recontacting?

Yes.

The published study also misrepresented the type of statistical analysis that had been conducted. "The McGwin Study said that it used a paired t-test; Dr. McGwin admitted that
he in fact used a two sample t-test instead, which he conceded was 'not the most
appropriate.'? Slip op. at 10-11. Beyond that, "the code that Dr. McGwin wrote to produce the numbers in the McGwin Study contained errors that would affect the odds ratios and confidence intervals." Id. at 11.

Once again, with notable restraint, the court concluded, "the fact that the methodologies described in the study were not the actual methodologies used undermines the reliability of the McGwin Study as published." Slip op. at 11.

But wait, there's more!

More than misrepresenting how the study's numbers were crunched?

Yes.

One of the study's "main findings" was "mischaracterized." The study claimed that Viagra users with "personal histories" of heart attack were at significantly greater risk of NAION. But in fact there was no "personal history" data collected. As the court summed up the evidence:

The patients were actually asked whether they had a family history of myocardial infarction; no one was asked about personal history. Dr. McGwin conceded that he mistakenly assumed that the variable ?MI? in his electronic dataset referred to a personal history of myocardial infarction.

Slip op. at 11 (emphasis original). This was, as the court held, "yet another layer of unreliability." Id. at 12.

Adding all this together, the published study competely failed Daubert analysis, notwithstanding that it appeared in a scientific journal:

Taken together, the miscodings and errors described above effectively undermine the reliability of the McGwin Study as published. As Plaintiffs concede, there are
?acknowledged inaccuracies in the published study? that need to be corrected. In light of those acknowledged inaccuracies, the Court finds good reason to vacate its original Daubert Order permitting Dr. McGwin to testify as a general causation expert based on the McGwin Study as published. Almost every indicia of reliability the Court relied on in its previous Daubert Order regarding the McGwin Study has been shown now to be unreliable.

Slip op. at 12.

Unfortunately, neither plaintiffs nor their expert knew to quit when they're behind. The expert ginned up an unpublished "reanalysis" - submitted after the fact - that he claimed salvaged the result of the published study, and thus his causation opinion.

Didn't help.

First, the unpublished reanalysis wasn't peer-reviewed. Second, the letter wasn't published. Third, the letter was created "post-litigation." Not only that, upon receipt of the letter, the journal in question "referred the Letter to the Committee on Publication Ethics." Slip op. at 14. In other words, the expert is being investigated for academic fraud.

The moral of this story (if not of certain of the participants) is clear - defendants can't give up the Daubert ship just because the other side comes up with a published, supposedly peer reviewed article. As here, a full investigation may reveal that the peer-reviewer was asleep at the switch, or that the article subverted the entire peer-review process by stating things that were simply false.

In short, there may be a reason beyond statistics why an outlier study is an outlier.

We can only hope that the Committee on Publication Ethics does the right thing.

Full post as published by Drug and Device Law on August 21, 2009 (boomark / email).