Journal
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume 142, Issue 4, Pages 944-955Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.jspi.2011.10.016
Keywords
Bayesian design; Clinical trial; Phase II/III design; Treatment selection
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Funding
- NCI NIH HHS [P01 CA049639, P30 CA016672, R01 CA083932, R01 CA083932-11] Funding Source: Medline
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The problem of comparing several experimental treatments to a standard arises frequently in medical research. Various multi-stage randomized phase II/III designs have been proposed that select one or more promising experimental treatments and compare them to the standard while controlling overall Type I and Type II error rates. This paper addresses phase II/III settings where the joint goals are to increase the average time to treatment failure and control the probability of toxicity while accounting for patient heterogeneity. We are motivated by the desire to construct a feasible design for a trial of four chemotherapy combinations for treating a family of rare pediatric brain tumors. We present a hybrid two-stage design based on two-dimensional treatment effect parameters. A targeted parameter set is constructed from elicited parameter pairs considered to be equally desirable. Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets. Decisions at each stage of the trial are based on the ratio of posterior probabilities of the alternative and null covariate-adjusted parameter sets. Design parameters are chosen to minimize expected sample size subject to frequentist error constraints. The design is illustrated by application to the brain tumor trial. (C) 2011 Elsevier B.V. All rights reserved.
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