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基于稳健非参数检验的配对复发事件数据的功效和样本量计算

Power and sample size calculation for paired recurrent events data based on robust nonparametric tests.

作者信息

Su Pei-Fang, Chung Chia-Hua, Wang Yu-Wen, Chi Yunchan, Chang Ying-Ju

机构信息

Department of Statistics, National Cheng Kung University, Tainan, 70101, Taiwan.

Institute of Allied Health Science, National Cheng Kung University, Tainan, 70101, Taiwan.

出版信息

Stat Med. 2017 May 20;36(11):1823-1838. doi: 10.1002/sim.7241. Epub 2017 Feb 9.

Abstract

The purpose of this paper is to develop a formula for calculating the required sample size for paired recurrent events data. The developed formula is based on robust non-parametric tests for comparing the marginal mean function of events between paired samples. This calculation can accommodate the associations among a sequence of paired recurrent event times with a specification of correlated gamma frailty variables for a proportional intensity model. We evaluate the performance of the proposed method with comprehensive simulations including the impacts of paired correlations, homogeneous or nonhomogeneous processes, marginal hazard rates, censoring rate, accrual and follow-up times, as well as the sensitivity analysis for the assumption of the frailty distribution. The use of the formula is also demonstrated using a premature infant study from the neonatal intensive care unit of a tertiary center in southern Taiwan. Copyright © 2017 John Wiley & Sons, Ltd.

摘要

本文的目的是推导一个用于计算配对复发事件数据所需样本量的公式。所推导的公式基于稳健的非参数检验,用于比较配对样本之间事件的边际均值函数。这种计算可以通过为比例强度模型指定相关的伽马脆弱变量来考虑配对复发事件时间序列之间的关联。我们通过全面的模拟评估了所提出方法的性能,包括配对相关性、齐次或非齐次过程、边际风险率、删失率、累积和随访时间的影响,以及对脆弱分布假设的敏感性分析。还使用了台湾南部一家三级中心新生儿重症监护病房的一项早产儿研究来演示该公式的应用。版权所有© 2017约翰·威利父子有限公司。

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