Bayesian Monitoring and Bootstrap Trial Simulation: A New Paradigm to Implement Adaptive Trial Design for Testing Antidepressant Drugs

Abstract

A novel methodology is proposed for continuous monitoring of efficacy data in ongoing antidepressant clinical trials and for decision making to support progression or discontinuation of the trial or one of the treatment arms. The Posterior Probability of Superiority (PPS) resulting from the application of Monte Carlo Markov Chain approach to a longitudinal model describing the time course of placebo and antidepressant drugs was used to estimate criteria to discontinue a treatment arm or the trial for futility, and to predict the treatment effect at study-end while the trial was still ongoing. The decision to stop the study was based on PPS, Predictive Power and on risk analysis based on a non-parametric bootstrap trial simulation. The performance of the Bayesian monitoring was evaluated by the retrospective analysis of 3 clinical trials. The Bootstrap-based methodology was compared to the Conditional Power and the Predictive Probability approaches. The application of the proposed methodology showed the possibility to stop a trial for futility when about 50% of total information was available and to detect signal of a treatment effect when limited information (<40%) was available. The comparisons with the Condition Power and the Predictive Probability approaches indicated that the Bayesian Bootstrap method, based on data-driven assumptions for priors, provided a better control for the risk of inappropriate decisions. The results suggest that the proposed methodology to monitor the accumulating information and to provide a scenario- based risk analysis could constitute a valuable approach to re-engineer the development process of novel drugs.