Xu W, Hedeker D
Pfizer, Inc, Ann Arbor, Michigan, USA.
J Biopharm Stat. 2001 Nov;11(4):253-73.
A random-effects regression model that allows the random coefficients to have a multivariate normal mixture distribution is described for classifying treatment response in longitudinal clinical trials. The proposed model is capable of dealing with longitudinal data from unknown heterogeneous populations. As applied to longitudinal clinical trials, for example, the model can distinguish subgroups of treatment response. Use of the proposed model is illustrated by analyzing data from two psychiatric clinical trials. The first includes depressed patients assigned to drug treatment who are repeatedly measured in terms of their level of depression. The second trial examined schizophrenic patients longitudinally who were assigned to either a drug or placebo condition. For both, the random-effects mixture model allows an assessment of whether patients comprise distinct populations in terms of their treatment response. Based on parameter estimates of the mixture model, ample evidence for a mixture of response to treatment is observed for both datasets.
本文描述了一种随机效应回归模型，该模型允许随机系数具有多元正态混合分布，用于纵向临床试验中的治疗反应分类。所提出的模型能够处理来自未知异质人群的纵向数据。例如，应用于纵向临床试验时，该模型可以区分治疗反应的亚组。通过分析两项精神病临床试验的数据来说明所提出模型的使用。第一项试验包括分配接受药物治疗的抑郁症患者，他们的抑郁水平被反复测量。第二项试验纵向研究了分配到药物或安慰剂组的精神分裂症患者。对于这两项试验，随机效应混合模型都允许评估患者在治疗反应方面是否构成不同的群体。基于混合模型的参数估计，在两个数据集中都观察到了充分的治疗反应混合证据。
