Monitoring potential adverse event rate differences using data from blinded trials: the canary in the coal mine.
The development of drugs and biologicals whose mechanisms of action may extend beyond their target indications has led to a need to identify unexpected potential toxicities promptly even while blinded clinical trials are under way. One component of recently issued FDA rules regarding safety reporting requirements raises the possibility of breaking the blind for pre-identified serious adverse events that are not the clinical endpoints of a blinded study. Concern has been expressed that unblinding individual cases of frequently occurring adverse events could compromise the overall validity of the study. However, if external information is available about adverse event rates among patients not receiving the test product in populations similar to the study population, then it may be possible to address the potential for elevated risk without unblinding the trial. This article describes a Bayesian approach for determining the likelihood of elevated risk suitable binomial or Poisson likelihoods that applies regardless of the metric used to express the difference. The method appears to be particularly appropriate for routine monitoring of safety information for project development programs that include large blinded trials. Copyright © 2016 John Wiley & Sons, Ltd.
Merck Research Laboratories, Upper Gwynedd, PA, 19454, U.S.A.
A Lawrence
0000-0002-9661-4474
AL
Gould
Merck Research Laboratories, Upper Gwynedd, PA, 19454, U.S.A.
William B
WB
Wang
D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016013:Likelihood Functions; D016012:Poisson Distribution; D012107:Research Design
Bayes; Poisson; binomial; blinded study; external databases; observational data