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Implementation of Backward Induction for Sequentially Adaptive Clinical Trials

In many clinical trials, patients are enrolled and data are collected sequentially, with interim decisions, including what treatment the next patient should receive and whether or not the trial should be terminated or continued, being based on the accruing data. This naturally leads to application of Bayesian sequential procedures for trial monitoring. This article discusses the implementation and computational tasks involved in the use of backward induction for making decisions during a clinical trial. An efficient method is presented for storing and retrieving decision tables that represent the decision trees characterizing all possible decisions made when implementing a clinical trial using backward induction. We address the general computational needs, and illustrate the ideas with a specific example of a two-arm trial with a binary outcome and a maximum sample size of 200 patients.