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Some critical issues in adaptive experimental designs for treatment comparisons

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Design and Analysis of Experiments

The study of adaptive designs has received impetus over the past 20 years, mainly because of its potential in applications to clinical trials, and a large number of these designs is now available in the statistical and biomedical literature, starting from simple ones like Efrons Biased Coin Design and Zelens Play-the-Winner, to the more sophisticated ones: D_A-optimum Biased Coin designs, Doubly-Adaptive designs, ERADE , Randomly Reinforced Urns, Reinforced Doubly-Adaptive Biased Coins, etc., not forgetting covariates (Covariate-Adjusted Response-Adaptive, Covariate-Adaptive Biased Coin, Covariate-adjusted Doubly-adaptive Biased Coin designs, etc.).

A complicating factor is that nowadays adaptive experiments are in general multipurpose: they try to simultaneously achieve inferential efficiency, bias avoidance, and utilitarian or ethical gains. Another is the very nature of an adaptive design, which is a random experiment that may or may not converge to a fixed treatment allocation.

This talk does not intend to be a survey of the existing literature, rather it is an effort to highlight potentially critical points: inference after an adaptive experiment, combined versus constrained optimization, speed of convergence to a desired target, possible misuse of simulations. Each of these points will be discussed with reference to one or more specific adaptive designs.

This talk is part of the Isaac Newton Institute Seminar Series series.

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