期刊
STATISTICS IN MEDICINE
卷 34, 期 5, 页码 782-795出版社
WILEY
DOI: 10.1002/sim.6367
关键词
longitudinal data analysis; missing data; pattern-mixture model; sensitivity analysis
Pattern-mixture models provide a general and flexible framework for sensitivity analyses of nonignorable missing data in longitudinal studies. The delta-adjusted pattern-mixture models handle missing data in a clinically interpretable manner and have been used as sensitivity analyses addressing the effectiveness hypothesis, while a likelihood-based approach that assumes data are missing at random is often used as the primary analysis addressing the efficacy hypothesis. We describe a method for power calculations for delta-adjusted pattern-mixture model sensitivity analyses in confirmatory clinical trials. To apply the method, we only need to specify the pattern probabilities at postbaseline time points, the expected treatment differences at postbaseline time points, the conditional covariance matrix of postbaseline measurements given the baseline measurement, and the delta-adjustment method for the pattern-mixture model. We use an example to illustrate and compare various delta-adjusted pattern-mixture models and use simulations to confirm the analytic results. Copyright (C) 2014 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据