Interpretable inference on the mixed effect model with the Box-Cox transformation
Interpretable inference on the mixed effect model with the Box-Cox transformation
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DOI：
10.1002/sim.7279
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发表时间：
2017-07-10
影响因子：
2
通讯作者：
Gosho, M.
中科院分区：
文献类型：
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作者：
Maruo, K.;Yamaguchi, Y.;Gosho, M.
基于渐近理论方法，利用Box-Cox变换推导出混合效应模型边缘模型参数的推断结果。考虑到模型的错误规范，我们还提供了该模型参数的最大似然估计量的稳健方差估计量。利用这些结果，我们开发了一个推理程序，用于随机临床试验重复测量分析的混合效应模型背景下，在特定场合治疗组之间模型中位数的差异，该程序提供了治疗效果的可解释估计。仿真研究表明，与现有方法相比，本文提出的方法在几乎所有情况下都控制了模型中位数差统计检验的I类误差，并且具有中等或较高的功率性能。我们用艾滋病临床试验中的CD4细胞簇数据说明了我们的方法，其中基于我们提出的方法的分析结果的可解释性得到了证明。版权所有2017 John Wiley & Sons， Ltd.
We derived results for inference on parameters of the marginal model of the mixed effect model with the Box-Cox transformation based on the asymptotic theory approach. We also provided a robust variance estimator of the maximum likelihood estimator of the parameters of this model in consideration of the model misspecifications. Using these results, we developed an inference procedure for the difference of the model median between treatment groups at the specified occasion in the context of mixed effects models for repeated measures analysis for randomized clinical trials, which provided interpretable estimates of the treatment effect. From simulation studies, it was shown that our proposed method controlled type I error of the statistical test for the model median difference in almost all the situations and had moderate or high performance for power compared with the existing methods. We illustrated our method with cluster of differentiation 4 (CD4) data in an AIDS clinical trial, where the interpretability of the analysis results based on our proposed method is demonstrated. Copyright (c) 2017 John Wiley & Sons, Ltd.