Premium
Efficient adaptive designs with mid‐course sample size adjustment in clinical trials
Author(s) -
Bartroff Jay,
Lai Tze Leung
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3201
Subject(s) - sample size determination , exponential family , adaptive design , computer science , sequential analysis , type i and type ii errors , optimal design , statistics , exponential function , nuisance parameter , sample (material) , mathematics , variance (accounting) , asymptotically optimal algorithm , mathematical optimization , clinical trial , medicine , mathematical analysis , chemistry , pathology , chromatography , estimator , business , accounting
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. Copyright © 2008 John Wiley & Sons, Ltd.