Bias adjustment in analysing longitudinal data with informative missingness.
Bias adjustment in analysing longitudinal data with informative missingness.
复制标题
分析具有信息缺失的纵向数据的偏差调整。
DOI：
10.1002/sim.992
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发表时间：
2002
影响因子：
2
通讯作者：
Shen,Lei
中科院分区：
文献类型：
--
作者：
Park,Soomin;Palta,Mari;Shao,Jun;Shen,Lei
最近的生物统计学文献包含了许多处理“信息审查”引起的偏差的方法，“信息审查”是指在按预定时间间隔安排的多次访问后从纵向研究中退出。相同或相关的方法可以扩展到丢失模式是间歇性的情况。通常认为缺失模式与随机效应的结果相关，随机效应代表了未测量的个人特征，例如健康意识。迄今为止，在实践中应用信息审查方法的经验有限，主要是因为建模复杂且计算困难。在本文中，我们提出了一种基于分组数据的估计方法。所提出的估计量在信息缺失的各种情况下都是渐近无偏的。在模拟研究中对几种现有方法进行了回顾和比较。我们将这些方法应用于威斯康星州糖尿病登记项目的数据，该项目是一项跟踪血糖控制以及 I 型糖尿病诊断引起的急性和慢性并发症的纵向研究。版权所有 © 2002 约翰威利父子有限公司
The recent biostatistical literature contains a number of methods for handling the bias caused by ‘informative censoring’, which refers to drop‐out from a longitudinal study after a number of visits scheduled at predetermined intervals. The same or related methods can be extended to situations where the missing pattern is intermittent. The pattern of missingness is often assumed to be related to the outcome through random effects which represent unmeasured individual characteristics such as health awareness. To date there is only limited experience with applying the methods for informative censoring in practice, mostly because of complicated modelling and difficult computations. In this paper, we propose an estimation method based on grouping the data. The proposed estimator is asymptotically unbiased in various situations under informative missingness. Several existing methods are reviewed and compared in simulation studies. We apply the methods to data from the Wisconsin Diabetes Registry Project, a longitudinal study tracking glycaemic control and acute and chronic complications from the diagnosis of type I diabetes. Copyright © 2002 John Wiley & Sons, Ltd.