Optimal dose-finding designs with correlated continuous and discrete responses

被引:30
|
作者
Fedorov, Valerii [2 ]
Wu, Yuehui [2 ]
Zhang, Rongmei [1 ]
机构
[1] Univ Penn, Dept Biostat & Epidemiol, Philadelphia, PA 19104 USA
[2] GlaxoSmithKline, Biomed Data Sci, Res Stat Unit, Collegeville, PA 19426 USA
关键词
multiple endpoints; bivariate probit models; optimal experimental design; model-based dose-finding approaches; utility functions; penalty functions; BAYESIAN DECISION PROCEDURES; LATENT VARIABLE MODELS; ADAPTIVE DESIGNS; CLINICAL-TRIALS; MIXED DISCRETE; BINARY; REGRESSION; EFFICACY; OUTCOMES; ESCALATION;
D O I
10.1002/sim.4388
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In dose-finding clinical studies, it is common that multiple endpoints are of interest. For instance, in phase I/II studies, efficacy and toxicity are often the primary endpoints, which are observed simultaneously and which need to be evaluated together. Motivated by this, we confine ourselves to bivariate responses and focus on the most analytically difficult case: a mixture of continuous and categorical responses. We adopt the bivariate probit doseresponse model and quantify our goal by a utility function. We study locally optimal designs, two-stage optimal designs, and fully adaptive designs under different ethical and cost constraints in the experiments. We assess the performance of two-stage designs and fully adaptive designs via simulations. Our simulations suggest that the two-stage designs are as efficient as and may be more efficient than the fully adaptive designs if there is a moderate sample size in the initial stage. In addition, two-stage designs are easier to construct and implement and thus can be a useful approach in practice. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:217 / 234
页数:18
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