Hierarchical Linear Models for Multiregional Clinical Trials
Hierarchical Linear Models for Multiregional Clinical Trials
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DOI：
10.1080/19466315.2019.1654914
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发表时间：
2020-07
影响因子：
1.8
通讯作者：
Saemina Kim;Seung-Ho Kang
中科院分区：
文献类型：
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作者：
Saemina Kim;Seung-Ho Kang
摘要多区域临床试验中观察到的数据在结构上是分层的，即患者人群由几个区域组成，患者嵌套在自己的区域内。为了反映这种层次结构，在这篇文章中，我们提出了两级分层线性模型，其中第一级模型是基于患者级别的数据，如治疗指标和年龄，和第二级模型是基于区域级别的数据，如医疗实践。固定效应模型和连续随机效应模型是分层线性模型的特例。我们进行了模拟研究，以调查三种方法的经验I型错误率，以测试整体治疗效果。以样本比率为权重的检验方法和地区间变异性的经验贝叶斯估计的检验方法的性能优于其他两种检验方法。
Abstract Data observed in multiregional clinical trials are structurally hierarchical in the sense that the patient population consists of several regions and patients are nested within their own regions. To reflect such hierarchical structure, in this article, we propose two-level hierarchical linear models in which the level-1 model is based on patient-level data such as treatment indicator and age, and the level-2 model is based on region-level data such as medical practices. The fixed effect model and the continuous random effect model are shown to be special cases of hierarchical linear models. We conducted simulation studies to investigate the empirical Type I error rates of three methods for testing the overall treatment effect. The performance of the testing method with sample ratios as weights and the empirical Bayes estimator for between-region variability is better than that of the other two testing methods.