Lipsitz S R, Laird N M, Harrington D P
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
Biometrics. 1994 Mar;50(1):11-24.
In this paper, we describe a two-step weighted least squares method for analyzing repeated categorical outcomes when some individuals are not observed at all times of follow-up. Other weighted least squares methods for analyzing repeated measures data with missing responses have previously been proposed by Koch, Imrey, and Reinfurt (1972, Biometrics 28, 663-692) and Woolson and Clarke (1984, Journal of the Royal Statistical Society, Series A 147, 87-99). These methods give consistent estimators if the responses are missing completely at random, as discussed in Rubin (1976, Biometrika 63, 581-592). We propose a two-step method that will give consistent results under the weaker condition of missing at random, and compare it with the other two methods.
在本文中，我们描述了一种两步加权最小二乘法，用于分析当一些个体在随访的所有时间点都未被观察到时的重复分类结局。此前，科赫、伊姆雷和赖因富特（1972年，《生物统计学》28卷，663 - 692页）以及伍尔森和克拉克（1984年，《皇家统计学会学报》，A辑147卷，87 - 99页）提出了其他用于分析存在缺失响应的重复测量数据的加权最小二乘法。如鲁宾（1976年，《生物计量学》63卷，581 - 592页）所讨论的，如果响应是完全随机缺失的，这些方法会给出一致的估计量。我们提出一种两步法，该方法在随机缺失这一较弱条件下将给出一致的结果，并将其与其他两种方法进行比较。
