Wang-Clow F, Lange M, Laird N M, Ware J H
Genentech Inc., South San Francisco, California, USA.
Stat Med. 1995 Feb 15;14(3):283-97. doi: 10.1002/sim.4780140307.
Many longitudinal studies and clinical trials are designed to compare rates of change over time in one or more outcome variables in several groups. Most such studies have incomplete data because some patients drop out before completing the study. The missing data may induce bias and inefficiency in naive estimates of important parameters. This paper uses Monte Carlo methods to compare the bias and efficiency of several two-stage estimators of the effect of treatment on the mean rate of change when the missing data arise from one of four processes. We also study the validity of confidence intervals and the power of hypothesis tests based on these estimates and their standard errors. In general, the weighted least squares estimator does relatively well, as does an analysis of covariance type estimator proposed by Wu et al. The best estimates of variance components are based on complete cases or maximum likelihood.
许多纵向研究和临床试验旨在比较多组中一个或多个结果变量随时间的变化率。大多数此类研究都存在数据不完整的情况，因为一些患者在完成研究前退出了。缺失的数据可能会在重要参数的简单估计中导致偏差和低效。本文使用蒙特卡罗方法比较了在缺失数据源于四个过程之一时，几种两阶段估计器在治疗对平均变化率影响方面的偏差和效率。我们还基于这些估计及其标准误差研究了置信区间的有效性和假设检验的功效。一般来说，加权最小二乘估计器表现相对较好，吴等人提出的协方差分析型估计器也是如此。方差分量的最佳估计基于完整病例或最大似然法。
