Bayesian Adaptive Methods for Clinical Trials (2010)

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As has been well discussed, the explosion of interest in Bayesian methods over the last 10 to 20 years has been the result of the convergence of modern computing power and efficient Markov chain Monte Carlo (MCMC) algorithms for sampling from and summarizing posterior distributions. Practitioners trained in traditional, frequentist statistical methods appear to have been drawn to Bayesian approaches for three reasons. One is that Bayesian approaches implemented with the majority of their informative content coming from the current data, and not any external prior information, typically have good frequentist properties (e.g., low mean squared error in repeated use). Second, these methods as now readily implemented in WinBUGS and other MCMC-driven software packages now offer the simplest approach to hierarchical (random effects) modeling, as routinely needed in longitudinal, frailty, spatial, time series, and a wide variety of other settings featuring interdependent data. Third, practitioners are attracted by the greater flexibility and adaptivity of the Bayesian approach, which permits stopping for efficacy, toxicity, and futility, as well as facilitates a straightforward solution to a great many other specialized problems such as dose-finding, adaptive randomization, equivalence testing, and others we shall describe.

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