Testing simultaneous hypotheses in pharmaceutical trials: a Bayesian approach.
The purpose of this paper is to compare the Bayes factor and the likelihood ratio test in a pharmaceutical trial where the two treatments are a new drug and a control (a positive control or a placebo). The goal is to jointly answer the questions (1) is the new drug or the control toxic? (2) Is the new drug more effective and safer than the control? We consider a bivariate model where each treatment is characterized by a target effect (a continuous primary response y) and by a side effect (a continuous supplementary response xi). Using a Bayesian approach, we account for the uncertainty resulting from prediction of the side effect, by making use of the physician's prior inputs about the target-toxicity relationship and the maximum tolerated target effects that are considered to be safe. Finally, we consider an example about a sleeplessness drug, and we show that the Bayes factor provides a more flexible and informative tool than the likelihood ratio test in simultaneous testing. Advantages are greater when the number of experimental subjects is small.
Dominici
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Department of Biostatistics, School of Hygiene and Public Health, Johns Hopkins University, Baltimore, Maryland 21205-2179, USA.
D014151:Anti-Anxiety Agents / Q000009:adverse effects / Q000627:therapeutic use; D001499:Bayes Theorem; D004358:Drug Therapy; D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D012107:Research Design / Q000706:statistics & numerical data*; D012894:Sleep Stages / Q000187:drug effects
D014151:Anti-Anxiety Agents