METHODS FOR THE ANALYSIS OF INFORMATIVELY CENSORED LONGITUDINAL DATA
METHODS FOR THE ANALYSIS OF INFORMATIVELY CENSORED LONGITUDINAL DATA
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DOI：
10.1002/sim.4780111408
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发表时间：
1992-10-01
影响因子：
2
通讯作者：
SCHLUCHTER, MD
中科院分区：
文献类型：
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作者：
SCHLUCHTER, MD
这篇文章描述了纵向研究中的信息审查问题，其中主要结果是连续变量的变化率。基于线性随机效应模型的标准方法只有在数据以不可忽视的方式丢失时才有效。信息性审查是一种不可忽视的特殊类型的缺失数据，当提前终止的概率与单个受试者的真实变化率相关时，就会发生信息性审查。如果存在信息量审查，则会导致基于标准似然分析的偏差，以及单个最小二乘斜率的加权平均。本文回顾了信息删失纵向数据分析的几种方法，并提出了一种基于对数正态生存模型的新方法。最大似然估计可以通过EM算法获得。这种方法的优点是，它允许由交错进入和不等时就诊引起的一般不平衡数据，它利用所有可用的数据，包括只有一次测量的患者的数据，并且它提供了估计所有模型参数的统一方法。还讨论了可能发生信息性审查时与研究设计有关的问题。
This paper describes the problem of informative censoring in longitudinal studies where the primary outcome is rate of change in a continuous variable. Standard approaches based on the linear random effects model are valid only when the data are missing in a non-ignorable fashion. Informative censoring, which is a special type of non-ignorably missing data, occurs when the probability of early termination is related to an individual subject's true rate of change. When present, informative censoring causes bias in standard likelihood-based analyses, as well as in weighted averages of individual least-squares slopes. This paper reviews several methods proposed by others for analysis of informatively censored longitudinal data, and outlines a new approach based on a log-normal survival model. Maximum likelihood estimates may be obtained via the EM algorithm. Advantages of this approach are that it allows general unbalanced data caused by staggered entry and unequally-timed visits, it utilizes all available data, including data from patients with only a single measurement, and it provides a unified method for estimating all model parameters. Issues related to study design when informative censoring may occur are also discussed.