Bayesian predictive approach to interim monitoring in clinical trials.
This paper reviews Bayesian strategies for monitoring clinical trial data. It focuses on a Bayesian stochastic curtailment method based on the predictive probability of observing a clinically significant outcome at the scheduled end of the study given the observed data. The proposed method is applied to derive efficacy and futility stopping rules in clinical trials with continuous, normally distributed and binary endpoints. The sensitivity of the resulting stopping rules to the choice of prior distributions is examined and guidelines for choosing a prior distribution of the treatment effect are discussed. The Bayesian predictive approach is compared to the frequentist (conditional power) and mixed Bayesian-frequentist (predictive power) approaches. The interim monitoring strategies discussed in the paper are illustrated using examples from a small proof-of-concept study and a large mortality trial.
Dmitrienko
Alexei
A
Lilly Research Laboratories, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA. dmitrienko_alex@lilly.com
Wang
Ming-Dauh
MD
D014151:Anti-Anxiety Agents / Q000627:therapeutic use; D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D001008:Anxiety Disorders / Q000188:drug therapy; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D018805:Sepsis / Q000188:drug therapy; D016896:Treatment Outcome
D014151:Anti-Anxiety Agents; D000900:Anti-Bacterial Agents