Analysis of change in the presence of informative censoring: application to a longitudinal clinical trial of progressive renal disease
Analysis of change in the presence of informative censoring: application to a longitudinal clinical trial of progressive renal disease
复制标题
DOI：
10.1002/sim.720
复制
发表时间：
2001-04-15
影响因子：
2
通讯作者：
Beck, GJ
中科院分区：
文献类型：
--
作者：
Schluchter, MD;Greene, T;Beck, GJ
连续变量随时间连续测量的变化率通常用作纵向研究或临床试验的结果。当患者在研究计划结束前终止研究时，使用忽略缺失数据机制的标准方法估计变化率可能存在偏倚。这些方法包括使用未加权的广义估计方程方法和基于似然的方法，假设一个可验证的缺失数据机制。我们提出了一个模型的分析信息删失数据，基于两阶段线性随机效应模型的扩展，其中每个主题的随机截距和斜率被允许与一个潜在的时间事件。然后使用EM算法通过最大似然估计连续响应和事件发生时间变量的联合分布，并使用自助法计算标准误。我们说明了这种方法，并将其与更简单的方法和通常的最大似然法进行比较，使用来自饮食和血压控制对肾脏疾病进展的影响的多中心研究的数据，肾脏疾病饮食改良（MDRD）研究。敏感性分析和模拟被用来评估这种方法的性能在MDRD数据的背景下，在各种情况下，辍学机制是可验证的，以及非可验证的。版权所有（C）2001约翰威利父子有限公司
The rate of change in a continuous variable, measured serially over time, is often used as an outcome in longitudinal studies or clinical trials. When patients terminate the study before the scheduled end of the study, there is a potential for bias in estimation of rate of change using standard methods which ignore the missing data mechanism. These methods include the use of unweighted generalized estimating equations methods and likelihood-based methods assuming an ignorable missing data mechanism. We present a model for analysis of informatively censored data, based on an extension of the two-stage linear random effects model, where each subject's random intercept and slope are allowed to be associated with an underlying time to event. The joint distribution of the continuous responses and the time-to-event variable are then estimated via maximum likelihood using the EM algorithm, and using the bootstrap to calculate standard errors. We illustrate this methodology and compare it to simpler approaches and usual maximum likelihood using data from a multi-centre study of the effects of diet and blood pressure control on progression of renal disease, the Modification of Diet in Renal Disease (MDRD) Study. Sensitivity analyses and simulations are used to evaluate the performance of this methodology in the context of the MDRD data, under various scenarios where the drop-out mechanism is ignorable as well as non-ignorable. Copyright (C) 2001 John Wiley & Sons, Ltd.