Incomplete Repeated Measures Data Analysis in the Presence of Treatment Effects
本文提出了一种针对重复测量数据中部分观测缺失的分析策略，适用于首次处理后数据缺失的情况，通过近似最大似然估计获得参数估计，并讨论了小样本下的推断方法。
Abstract This article illustrates an analysis strategy for repeated measures data where some of the measurements are missing from the subjects on one or more occasions, with a special application to data arising from repeated measurement designs when some observations are missing after the first treatment period. Using all available data, “almost” maximum likelihood estimators for the parameters of interest are obtained under the assumption of a multivariate normal distribution of the data. Inference procedures for these parameters in small samples from popular two-treatment designs are discussed.