A Simulation Comparison of Approximate Tests for Fixed Effects in Random Coefficients Growth Curve Models
A Simulation Comparison of Approximate Tests for Fixed Effects in Random Coefficients Growth Curve Models
复制标题
随机系数增长曲线模型中固定效应近似检验的模拟比较
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
L. Lamotte
中科院分区：
文献类型：
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作者：
J. Volaufova;L. Lamotte
通常，随着时间的推移，抽样单位的响应变量会被重复观察。抽样单位可能来自不同的人群，例如治疗组。该设置通常通过随机系数增长曲线模型进行建模，并应用一般线性混合模型的技术来解决主要研究目标。另一种方法是将每个受试者的数据简化为汇总度量，例如受试者内平均值或回归系数。然后可以测试治疗组之间汇总测量（或它们的函数）的均值是否相等。在这里，我们通过模拟比较了基于汇总测量的三种近似测试和基于完整数据的一种近似测试的性能特征，主要关注 p 值的准确性。我们发现，对于小样本，在几种不同的参数值配置中，这些过程的性能可能会有很大不同。汇总测量方法的表现至少与全数据混合模型方法一样好。
Often, the response variables on sampling units are observed repeatedly over time. The sampling units may come from different populations, such as treatment groups. This setting is routinely modeled by a random coefficients growth curve model, and the techniques of general linear mixed models are applied to address the primary research aim. An alternative approach is to reduce each subject’s data to summary measures, such as within-subject averages or regression coefficients. One may then test for equality of means of the summary measures (or functions of them) among treatment groups. Here, we compare by simulation the performance characteristics of three approximate tests based on summary measures and one based on the full data, focusing mainly on accuracy of p-values. We find that performances of these procedures can be quite different for small samples in several different configurations of parameter values. The summary-measures approach performed at least as well as the full-data mixed models approach.
一般线性增长曲线模型的两阶段估计方法。
影响因子：
1.9
作者：
Stukel,TA;Demidenko,E
通讯作者：
Demidenko,E