Pharma’s Cutting Edge

Yesterday’s Hearing on FDA’s Role in Evaluating Safety of Avandia held by the U.S. House Committee on Oversight and Government Reform made for entertaining TV.  Most of the elements of a good courtroom drama were on display:  a protaganist whose interests in ethics and science keep him too busy to pay attention to the effects his pronouncements have on financial markets (played by Dr. Steve Nissen); a fact-challenged villain (the TZEs?) out to destroy the reputation of said protagonist by exposing his lust for glory (played by Rep. Darrell Issa); and a captive forum filled with minor characters who fill the backstory of the hero, challenge the villain, and stand at the ready to interject the bits of comic relief and moralizing needed for pacing.  On the downside, the third act was very weak, and I missed the caustic irony and Adamsesque speechifying of James Spader’s character on Boston Legal (hey…why not invite Mr. Spader and a couple of BL writers to sit in on some of these hearings; it would surely boost rating).

For those of you who didn’t catch the action live, you can watch it using the above link.  Other than the entertainment value, though, there was much of substance to take away from the event.  One of the take aways of some interest was that FDA Commish von Eschenbach stated, after several minutes of hemming and hawing that FDA has all the power it needs to enforce existing rules governing DTC advertising.  It simply lacks the resources to do the job properly.  Other than that, much of what was said by FDA staff and the Congressmen present was rehash of what we’ve heard surrounding the PDUFA renewal debates.  There were some hints that an appetite exists among FDA and in Congress to rethink the extent of premarketing risk assessments for chronic-use drugs intended for large populations, but I wasn’t convinced that this event was enough impetus to keep the ball rolling.  Regular readers know that I support such rethinking.

I also wanted to at least try to clarify the panelists’ (esp. Bruce Psaty’s) response to the question that Rep. Issa kept asking everyone he could corner regarding the exclusion of zero-event studies from the Nissen meta-analysis.  Issa kept asserting that exclusion of the zero-event studies (i.e. those without any events of myocardial infarction, MI, or death) from the meta-analysis was inappropriate, because it reduced the apparent incidence of MI.  It is correct that eliminating zero-event studies would reduce the apparent MI incidence rate (i.e. the rate of appearance of new MI events during the observation intervals).  However, the meta-analysis used by Nissen did not use the incidence rate of MI in the rosiglitazone group and compare it with the incidence rates in the other treatment groups.  If it had, the authors would reported an incidence rate ratio (IRR), or a relative risk, which displays the ratio of probabilities of observing the outcome for each treatment comparison.  In order to use the relative risk, the meta-analysis should have access to every study and all observations, and, to get the best estimate, the observation interval should be the same among the pooled studies.  Given these restrictions, use of the relative risk can be problematic in the real world.  Nissen couldn’t be sure these conditions would be met by his analysis, so he wisely chose to calculate an odds ratio for each of the treatment comparisons, which isn’t subject to as many restrictions for proper interpretation. 

The odds ratio as used in the paper is simply the ratio of the odds of observing an event in each of the various treatment groups compared the pooled studies.  It is calculated by dividing the number of events by the number of non-events in each group then calculating the ratio between groups.  In other words, if there were 10 total observations in one group and 6 were MI or death events, the odds of an MI or death event in that group would be 6/4 or 1.5.  If the same total observations (10) occurred in a second group, but only 4 MI or death events were observed, the odds ratio would be:  (6/4)/(4/6)=2.25.  If the observation interval were the same in each study and the relative risk were calculated instead, here is what it would be for the same data:  (6/10)/(4/10)=1.5.  So, in this example, the odds ratio would be higher the relative risk, making the problem appear worse then it might otherwise be perceived (as Issa accused Nissen of doing).  But this example uses a common event that occurs in around half of the patients.  Instead, if we consider a relatively uncommon event that occurs in less than 2% of patients (like in the Nissen paper), we’ll see a different result.  Let’s say that the total observations in each group is 100.  In group A, the event occurs in 2 patients and does not occur in 98.  In group B, the event occurs in 1 patient and does not occur in 99.  The odds ratio for A:B is:  (2/98)/(1/99)=2.02.  The relative risk for A:B is (2/100)/(1/100)=2.00.  Now, you can see that the odds ratio closely approximates the relative risk.  This is simply a reflection of the nature of numbers, fractions in particular, where a risk reduction of 20% (i.e. a rel. risk of 0.8) is not analogous to a risk increase of 20% (i.e. a rel risk of 1.2) but is instead analogous to a risk increase of 25% (i.e. rel risk of 1.25, the inverse of 0.8).  I’ve found that this point is easily understood but frequently not considered by clinicians.  Perhaps it is not as easily understood by Republican Congressmen from California ?