Journal
BIOMETRICS
Volume 66, Issue 2, Pages 532-540Publisher
WILEY-BLACKWELL PUBLISHING, INC
DOI: 10.1111/j.1541-0420.2009.01302.x
Keywords
Adaptive design; Bayesian design; Clinical trial; Combination dose-finding; Utility
Funding
- NCI NIH HHS [R01 CA083932, R01 CA083932-08A2] Funding Source: Medline
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An outcome-adaptive Bayesian design is proposed for choosing the optimal close pair of a chemotherapeutic agent and a biological agent used in combination in a phase clinical trial. Patient outcome is characterized as a vector of two ordinal variables accounting for toxicity and treatment efficacy. A generalization of the Aranda-Ordaz model (1981, Biometrika 68, 357-363) is used for the marginal outcome probabilities as functions of a close pair, and a Gaussian copula is assumed to obtain joint distributions. Numerical utilities of all elementary patient outcomes, allowing the possibility that efficacy is inevaluable due to severe toxicity, are obtained using an elicitation method aimed to establish consensus among the physicians planning the trial. For each successive patient cohort, a. dose pair is chosen to maximize the posterior mean utility. The method is illustrated by a trial in bladder cancer, including simulation studies of the method's sensitivity to prior parameters, the numerical utilities, correlation between the outcomes, sample size, cohort size, and starting dose pair.
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