Missing Data in Clinical Studies: Issues and Methods
Missing Data in Clinical Studies: Issues and Methods
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DOI：
10.1200/jco.2011.38.7589
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发表时间：
2012-09-10
影响因子：
45.3
通讯作者：
Chen, Ming-Hui
中科院分区：
文献类型：
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作者：
Ibrahim, Joseph G.;Chu, Haitao;Chen, Ming-Hui
缺失数据是任何类型的数据分析中普遍存在的问题。如果未观察到参与者变量（结局或协变量）的值，则认为参与者变量缺失。在这篇文章中，各种问题，在分析研究与缺失数据进行了讨论。特别是，我们关注具有离散、连续或至事件时间终点的研究的缺失应答和/或协变量数据，其中使用广义线性模型、纵向数据模型（如广义线性混合效应模型）或考克斯回归模型。我们讨论了在研究中可能出现的缺失数据的各种分类，并在几种情况下证明了将所有参与者与任何缺失数据一起抛出的常用方法可能导致不正确的结果和结论。所描述的方法适用于从东部肿瘤协作组肝癌的第二阶段临床试验和晚期非小细胞肺癌的第三阶段临床试验的数据。虽然这里讨论的主要应用领域是癌症，但我们讨论的问题和方法适用于任何类型的研究。
Missing data are a prevailing problem in any type of data analyses. A participant variable is considered missing if the value of the variable (outcome or covariate) for the participant is not observed. In this article, various issues in analyzing studies with missing data are discussed. Particularly, we focus on missing response and/or covariate data for studies with discrete, continuous, or time-to-event end points in which generalized linear models, models for longitudinal data such as generalized linear mixed effects models, or Cox regression models are used. We discuss various classifications of missing data that may arise in a study and demonstrate in several situations that the commonly used method of throwing out all participants with any missing data may lead to incorrect results and conclusions. The methods described are applied to data from an Eastern Cooperative Oncology Group phase II clinical trial of liver cancer and a phase III clinical trial of advanced non-small-cell lung cancer. Although the main area of application discussed here is cancer, the issues and methods we discuss apply to any type of study.