Please use this identifier to cite or link to this item: https://doi.org/10.1002/sim.2322
Title: Decision-theoretic designs for dose-finding clinical trials with multiple outcomes
Authors: Fan, S.K. 
Wang, Y.-G. 
Keywords: Bandit process
Optimality
Sequential clinical trials
Sub-optimality
Issue Date: 30-May-2006
Citation: Fan, S.K., Wang, Y.-G. (2006-05-30). Decision-theoretic designs for dose-finding clinical trials with multiple outcomes. Statistics in Medicine 25 (10) : 1699-1714. ScholarBank@NUS Repository. https://doi.org/10.1002/sim.2322
Abstract: A decision-theoretic framework is proposed for designing sequential dose-finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose-finding studies involving k doses, the computational complexity is the same as the bandit problem with k-dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available. Copyright © 2005 John Wiley & Sons, Ltd.
Source Title: Statistics in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/105080
ISSN: 02776715
DOI: 10.1002/sim.2322
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