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首页> 外文期刊>Statistics in medicine >Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.
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Impact of missing data due to drop-outs on estimators for rates of change in longitudinal studies: a simulation study.

机译：纵向研究中因遗失而导致的数据丢失对估计量的影响：模拟研究。

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Many cohort studies and clinical trials are designed to compare rates of change over time in one or more disease markers in several groups. One major problem in such longitudinal studies is missing data due to patient drop-out. The bias and efficiency of six different methods to estimate rates of changes in longitudinal studies with incomplete observations were compared: generalized estimating equation estimates (GEE) proposed by Liang and Zeger (1986); unweighted average of ordinary least squares (OLSE) of individual rates of change (UWLS); weighted average of OLSE (WLS); conditional linear model estimates (CLE), a covariate type estimates proposed by Wu and Bailey (1989); random effect (RE), and joint multivariate RE (JMRE) estimates. The latter method combines a linear RE model for the underlying pattern of the marker with a log-normal survival model for informative drop-out process. The performance of these methods in the presence of missing data completely at random (MCAR), at random (MAR) and non-ignorable (NIM) were compared in simulation studies. Data for the disease marker were generated under the linear random effects model with parameter values derived from realistic examples in HIV infection. Rates of drop-out, assumed to increase over time, were allowed to be independent of marker values or to depend either only on previous marker values or on both previous and current marker values. Under MACR all six methods yielded unbiased estimates of both group mean rates and between-group difference. However, the cross-sectional view of the data in the GEE method resulted in seriously biased estimates under MAR and NIM drop-out process. The bias in the estimates ranged from 30 per cent to 50 per cent. The degree of bias in the GEE estimates increases with the severity of non-randomness and with the proportion of MAR data. Under MCAR and MAR all the other five methods performed relatively well. RE and JMRE estimates were more efficient(that is, had smaller variance) than UWLS, WLS and CL estimates. Under NIM, WLS and particularly RE estimates tended to underestimate the average rate of marker change (bias approximately 10 per cent). Under NIM, UWLS, CL and JMRE performed better in terms of bias (3-5 per cent) with the JMRE giving the most efficient estimates. Given that markers are key variables related to disease progression, missing marker data are likely to be at least MAR. Thus, the GEE method may not be appropriate for analysing such longitudinal marker data. The potential biases due to incomplete data require greater recognition in reports of longitudinal studies. Sensitivity analyses to assess the effect of drop-outs on inferences about the target parameters are important. Copyright 2001 John Wiley & Sons, Ltd.
机译：许多队列研究和临床试验旨在比较几组中一种或多种疾病标志物随时间的变化率。这种纵向研究的一个主要问题是由于患者辍学而导致数据丢失。比较了在不完整的观测条件下纵向研究的六种不同的估计变化率的偏倚和效率：Liang and Zeger（1986）提出的广义估计方程估计（GEE）;单个变化率（UWLS）的非加权平均最小二乘法（OLSE）; OLSE（WLS）的加权平均值;条件线性模型估计（CLE），由Wu和Bailey（1989）提出的协变量类型估计;随机效应（RE）和联合多元RE（JMRE）估算值。后一种方法将用于标记的基础模式的线性RE模型与用于信息辍学过程的对数正态生存模型结合在一起。在模拟研究中比较了在完全随机（MCAR），随机（MAR）和不可忽略（NIM）数据丢失的情况下这些方法的性能。在线性随机效应模型下，使用从HIV感染中的实际实例得出的参数值来生成疾病标记的数据。假定随时间增加的辍学率可以独立于标记值，也可以仅取决于先前的标记值或既取决于先前的标记值又取决于当前的标记值。在MACR下，所有六种方法均得出了两组均值率和组间差异的无偏估计。但是，GEE方法中数据的横截面图导致在MAR和NIM退出过程下的估计严重偏差。估计数的偏差在30％至50％之间。 GEE估计中的偏差程度随非随机性的严重程度以及MAR数据的比例而增加。在MCAR和MAR下，所有其他五种方法的效果都相对较好。 RE和JMRE估计比UWLS，WLS和CL估计更有效（即，方差较小）。在NIM下，WLS尤其是RE的估算往往低估了标记变化的平均速度（偏差约为10％）。在NIM下，UWLS，CL和JMRE在偏见方面表现更好（3-5％），而JMRE提供了最有效的估计。考虑到标记是与疾病进展相关的关键变量，标记数据的缺失可能至少是MAR。因此，GEE方法可能不适用于分析此类纵向标记数据。由于数据不完整而造成的潜在偏差需要在纵向研究报告中得到更大的认可。进行敏感性分析以评估辍学对目标参数推论的影响非常重要。版权所有2001 John Wiley＆Sons，Ltd.

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