Berk K
Biometrics. 1987 Jun;43(2):385-98.
Repeated-measures experiments involve two or more intended measurements per subject. If the within-subjects design is the same for each subject and no data are missing, then the analysis is relatively simple and there are readily available programs that do the analysis automatically. However, if the data are incomplete, and do not have the same arrangement for each subject, then the analysis becomes much more difficult. Beginning with procedures that are not optimal but are comparatively simple, we discuss unbalanced linear model analysis and then normal maximum likelihood (ML) procedures. Included are ML and REML (restricted maximum likelihood) estimators for the mixed model and also estimators for a model that allows arbitrary within-subject covariance matrices. The objective is to give procedures that can be implemented with available software.
重复测量实验涉及对每个受试者进行两次或更多次的预定测量。如果每个受试者的受试者内设计相同且没有数据缺失，那么分析相对简单，并且有现成的程序可以自动进行分析。然而，如果数据不完整，并且每个受试者的数据排列不同，那么分析就会变得困难得多。从并非最优但相对简单的程序开始，我们讨论不平衡线性模型分析，然后是正态最大似然（ML）程序。其中包括混合模型的ML和REML（限制最大似然）估计器，以及允许任意受试者内协方差矩阵的模型的估计器。目的是给出可以用现有软件实现的程序。
