Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions
61 Pages Posted: 25 May 2018 Last revised: 1 Aug 2018
Date Written: May 25, 2018
We integrate emerging trends intended to improve clinical trial design: design for cost-effectiveness, which ensures health-economic improvement of a new intervention over the current standard intervention; adaptive design, which dynamically adjusts the sample size and allocation of patients to different interventions; and multi-arm trial design, which compares multiple interventions simultaneously. Our goal is to identify a sequential sampling policy that dynamically decides the interventions to which patients should be allocated, as well as when to stop patient recruitment, in order to maximize the expected population-level benefit minus the cost of the trial. The literature on sequential sampling develops indices that either accommodate correlation among the mean rewards of alternatives or are based on optimal stopping times that can dynamically change as samples are taken, but not both. We develop the first tractable allocation and stopping rules whose indices capture both correlation and dynamic stopping times, and our numerical experiments demonstrate the value of considering both problem elements in the context of clinical trials.
Keywords: Bayesian, Clinical Trials, Health Economics, Sequential Experimentation, Ranking and Selection, Simulation Optimization, Dynamic Programming
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