COMPUTING FOR INCOMPLETE REPEATED MEASURES
COMPUTING FOR INCOMPLETE REPEATED MEASURES
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DOI：
10.2307/2531820
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发表时间：
1987-06-01
期刊：
影响因子：
1.9
通讯作者：
BERK, K
中科院分区：
文献类型：
--
作者：
BERK, K
重复测量实验涉及每个受试者的两个或更多个预期测量。如果每个受试者的受试者内设计相同，并且没有数据缺失，则分析相对简单，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，并且每个主题没有相同的安排，则分析变得更加困难。开始的程序，不是最佳的，但比较简单，我们讨论不平衡的线性模型分析，然后正常的最大似然（ML），程序。包括混合模型的ML和REML（限制最大似然）估计量，以及允许任意受试者内协方差矩阵的模型的估计量。其目的是提供可以用现有软件实现的程序。
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 become 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.