Bartroff Jay, Lai Tze Leung
Department of Mathematics, University of Southern California, Los Angeles, CA 90089, U.S.A.
Stat Med. 2008 May 10;27(10):1593-611. doi: 10.1002/sim.3201.
Adaptive designs have been proposed for clinical trials in which the nuisance parameters or alternative of interest are unknown or likely to be misspecified before the trial. Although most previous works on adaptive designs and mid-course sample size re-estimation have focused on two-stage or group-sequential designs in the normal case, we consider here a new approach that involves at most three stages and is developed in the general framework of multiparameter exponential families. This approach not only maintains the prescribed type I error probability but also provides a simple but asymptotically efficient sequential test whose finite-sample performance, measured in terms of the expected sample size and power functions, is shown to be comparable to the optimal sequential design, determined by dynamic programming, in the simplified normal mean case with known variance and prespecified alternative, and superior to the existing two-stage designs and also to adaptive group-sequential designs when the alternative or nuisance parameters are unknown or misspecified.
自适应设计已被提议用于临床试验，在这类试验中，干扰参数或感兴趣的备择假设在试验前是未知的或可能被错误设定。尽管之前关于自适应设计和中期样本量重新估计的大多数工作都集中在正态情况下的两阶段或组序贯设计，但我们在此考虑一种新方法，该方法最多涉及三个阶段，并且是在多参数指数族的一般框架下发展而来的。这种方法不仅能保持规定的一类错误概率，还提供了一种简单但渐近有效的序贯检验，其有限样本性能（以期望样本量和功效函数衡量）在已知方差和预先指定备择假设的简化正态均值情况下，被证明与通过动态规划确定的最优序贯设计相当，并且当备择假设或干扰参数未知或被错误设定时，优于现有的两阶段设计以及自适应组序贯设计。
