Sample size determination for multiple comparison studies treating confidence interval width as random

作     者：Pan, ZY Kupper, LL 

作者机构：Univ N Carolina Sch Publ Hlth Dept Biostat Chapel Hill NC 27599 USA Family Hlth Int Div Biostat Res Triangle Pk NC 27709 USA 

出 版 物：《STATISTICS IN MEDICINE》 (医学统计学)

年 卷 期：1999年第18卷第12期

页      面：1475-1488页

核心收录：

学科分类：0710[理学-生物学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1001[医学-基础医学(可授医学、理学学位)] 0714[理学-统计学（可授理学、经济学学位）] 10[医学] 

基　　金：NIEHS NIH HHS [5 T32 ES07018] Funding Source: Medline 

主　　题：置信区间 HIV感染/药物疗法 偏头痛/药物疗法 病人/统计学和数值数据 随机分配 样本大小 软件 随机过程 人类 

摘      要：Methods for optimal sample size determination are developed using four popular multiple comparison procedures (Scheffe s, Bonferroni s, Tukey s and Dunnett s procedures), where random samples of the same size n are to be selected from k(greater than or equal to 2) normal populations with common variance sigma(2), and where primary interest concerns inferences about a family of L linear contrasts among the k population means. For a simultaneous coverage probability of (1 - alpha), the optimal sample size is defined to be the smallest integer value n(m)* such that, simultaneously for all L confidence intervals, the width of the lth confidence interval will be no greater than tolerance 2 delta(t) (l = 1, 2, ..., L) with tolerance probability at least (1 - gamma), treating the pooled sample variance S-p(2) as a random variable. Using Scheffe s procedure as an illustration, comparisons are made to usual sample size methods that incorrectly ignore the stochastic nature of S-p(2). The latter approach can lead to serious underestimation of required sample sizes and hence to unacceptably low values of the actually tolerance probability (1 - gamma ). Our approach guarantees a lower bound of [1 - (alpha + gamma)] for the probability that the L confidence intervals will both cover the parametric functions of interest and also be sufficiently narrow. Recommendations are provided regarding the choices among the four multiple comparison procedures for sample size determination and inference-making. Copyright (C) 1999 John Wiley & Sons, Ltd.