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|a Bertsimas, Dimitris J
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|a Sloan School of Management
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|a Korolko, Nikita
|q (Nikita E.)
|e author
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|a Weinstein, Alexander Michael
|e author
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|a Covariate-Adaptive Optimization in Online Clinical Trials
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|b Institute for Operations Research and the Management Sciences (INFORMS),
|c 2021-02-18T15:24:43Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/129812
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|a The decision of how to allocate subjects to treatment groups is of great importance in experimental clinical trials for novel investigational drugs, a multibillion-dollar industry. Statistical power, the ability of an experiment to detect a positive treatment effect when one exists, depends in part on the similarity of the groups in terms of measurable covariates that affect the treatment response. We present a novel algorithm for online allocation that leverages robust mixed-integer optimization. In all tested scenarios, the proposed method yields statistical power at least as high as, and sometimes significantly higher than, state-of-the-art covariate-adaptive randomization approaches. We present a setting in which our algorithm achieves a desired level of power at a sample size 25%-. smaller than that required with randomization-based approaches. Correspondingly, we expect that our covariate-adaptive optimization approach could substantially reduce both the duration and operating costs of clinical trials in many commonly observed settings while maintaining computational efficiency and protection against experimental bias.
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|a United States. Office of Naval Research (Grant 021152-00001)
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|a en
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|a Article
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|t 10.1287/OPRE.2018.1818
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|t Operations Research
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