Sample size calculations with multiplicity adjustment for longitudinal clinical trials with missing data

被引:5
|
作者
Lu, Kaifeng [1 ]
机构
[1] Forest Labs Inc, Dept Biostat, Jersey City, NJ 07311 USA
关键词
Hochberg procedure; inflation factor; longitudinal data; multivariate ANOVA; treatment comparison; BONFERRONI PROCEDURE; TESTS; MMRM; LOCF;
D O I
10.1002/sim.4415
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Missing data are ubiquitous in longitudinal clinical trials, and the impact on power has been extensively assessed in the literature. Multiple doses of the investigational product and multiple efficacy endpoints are often studied in randomized clinical trials and multiplicity adjustment needs to be considered in the sample size calculations. In this paper, I show how to perform sample size calculations with multiplicity adjustment for longitudinal clinical trials with missing data by converting longitudinal data with missing data to cross-sectional data without missing data. The proposed approach can drastically simplify the simulation work and facilitate the evaluation of power for various scenarios. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:19 / 28
页数:10
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