Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures
Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures
复制标题
假设多个协方差结构，对不平衡重复测量的检验进行小样本调整
DOI：
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发表时间：
1990
期刊：
影响因子：
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通讯作者：
J. Elashoff
中科院分区：
文献类型：
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作者：
M. Schluchter;J. Elashoff
纵向数据和不完全重复测量分析方法的最新进展是在最大似然（ML）和限制最大似然（REML）方法领域（例如，Laird和Ware， 1982 Biometrics， jenrich和Schluchter， 1986 Biometrics）。本文概述了ML和REML方法对不完全重复测量数据和生长曲线的分析，然后检验了在四种不同的假设协方差结构下由ML和REML估计构建的渐近wald型卡方检验的小样本调整方法。这些调整包括将瓦尔德吉平方统计量转换为近似的f统计量。在某些情况下，当数据完整和平衡时，转换后的检验统计量在零假设下具有精确的f分布。前三种协方差结构：(1)复合对称，(2)一阶自回归，(3)多元（非结构化），在重复测量分析的背景下进行检验。
Recent advances in methods for analysis of longitudinal data arid incomplete repeated measures have been in the area of maximum likelihood (ML) and restricted maximum likelihood (REML) methods (e.g., Laird and Ware, 1982 Biometrics, Jennrich and Schluchter, 1986 Biometrics). This paper outlines the ML and REML approaches to the analysis of incomplete repeated measures data and growth curves, and then examines methods for small-sample adjustment of asymptotic Wald-type chi-square tests constructed from ML and REML estimates under four different assumed covariance structures. These adjustments involve transformation of the Wald Ghi-square statistic to an approximate F-statistic. In certain cases when data are complete and balanced, the transformed test statistics have exact F-distribution under the null hypothesis. The first three covariance structures: (1) Compound Symmetry, (2) First-Order Autoregressive, and (3) Multivariate (unstructured), are examined in the context of the analysis of a repeated measur...