The impact of loss to follow-up on hypothesis tests of the treatment effect for several statistical methods in substance abuse clinical trials
The impact of loss to follow-up on hypothesis tests of the treatment effect for several statistical methods in substance abuse clinical trials
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DOI：
10.1016/j.jsat.2008.09.011
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发表时间：
2009-07-01
影响因子：
3.9
通讯作者：
Malcolm, Robert J.
中科院分区：
文献类型：
--
作者：
Hedden, Sarra L.;Woolson, Robert F.;Malcolm, Robert J.
在药物滥用临床试验中，“失访”可能很严重。当出现大量失访时，必须谨慎地分析和解释研究性研究的结果。本项目的目的是介绍缺失数据机制的类型，并描述几种分析失访数据的方法。此外，模拟研究比较了I型错误和功率的几种方法时，丢失数据量和机制的变化。比较的方法如下：末次观察值结转（LOCF）、多重插补（MI）、改良分层汇总统计量（SSS）和混合效应模型。结果表明，所有方法的标称I型错误;除LOCF外，所有方法的功效均较高。通常推荐使用混合效应模型、改良SSS和MI;然而，许多方法要求数据随机缺失或完全随机缺失（即，“可替换的”）。如果假定缺失数据不可验证，则建议进行敏感性分析。(C)2009 Elsevier Inc. All rights reserved.
"Loss to follow-up" can be substantial in substance abuse clinical trials. When extensive losses to follow-up occur, one must cautiously analyze and interpret the findings of a research study. Aims of this project were to introduce the types of missing data mechanisms and describe several methods for analyzing data with loss to follow-up. Furthermore, a simulation study compared Type I error and power of several methods when missing data amount and mechanism varies. Methods compared were the following: Last observation carried forward (LOCF), multiple imputation (MI), modified stratified summary statistics (SSS), and mixed effects models. Results demonstrated nominal Type I error for all methods; power was high for all methods except LOCF. Mixed effect model, modified SSS, and MI are generally recommended for use; however, many methods require that the data are missing at random or missing completely at random (i.e., "ignorable"). If the missing data are presumed to be nonignorable, a sensitivity analysis is recommended. (C) 2009 Elsevier Inc. All rights reserved.