Analysis of Incomplete Data from a Clinical Trial with Repeated Measurements
针对重复测量析因设计中单调缺失样本，在多元正态和前置依赖结构假设下，提出了基于似然的协方差分析模型，用于检验位置参数的线性假设并估计调整均值。
This paper deals with an analysis of covariance model for analyzing monotone samples from a factorial design with repeated measurements under the assumption of multivariate normality with an ante-dependence structure. The likelihood-based procedures are valid when the missing data are missing either at random or completely at random (Rubin, 1976). The likelihood ratio test of a general linear hypothesis on location parameters is derived. The adjusted means of the location parameters along with their dispersion matrix are also derived. A numerical example is given to illustrate the procedures. The factorization property of the likelihood is also used in estimating intermediate missing values, if any.