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J Stat Plan Inference. 2017 Mar;182:88-99. doi: 10.1016/j.jspi.2016.09.002. Epub 2016 Oct 7.

Implementing Optimal Allocation in Clinical Trials with Multiple Endpoints.

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Department of Biostatistics, University of Texas Health Science Center School of Public Health at Houston, 1200 Pressler St, Houston, Texas 77030, USA.
Department of Biostatistics and Epidemiology, University of Pennsylvania, 210 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104.


Modern clinical trials are often complex, with multiple competing objectives and multiple endpoints. Such trials should be both ethical and efficient. In this paper, we overcome the obstacles introduced by the large number of unknown parameters and the possible correlations between the multiple endpoints. We obtain the optimal allocation proportions for the following two optimization problems: (1) maximizing the power of the test of homogeneity with a fixed sample size, and (2) minimizing the expected weighted number of failures with a fixed power. Further, we implement these optimal allocations through response-adaptive randomization procedures. Our theoretical results provide the foundation for the implementation and further investigation of the procedure, and our numerical studies demonstrate its ability to achieve diverse objectives.


Clinical trial; Multiple endpoints; Optimal allocation; Power; Response-adaptive design

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