Andrews Nichole, Cho Hyunkeun
Department of Statistics, Western Michigan University, Kalamazoo, MI 49008, USA.
Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA.
Stat Med. 2018 Jan 15;37(1):98-106. doi: 10.1002/sim.7500. Epub 2017 Sep 25.
In clinical trials and biomedical studies, treatments are compared to determine which one is effective against illness; however, individuals can react to the same treatment very differently. We propose a complete process for longitudinal data that identifies subgroups of the population that would benefit from a specific treatment. A random effects linear model is used to evaluate individual treatment effects longitudinally where the random effects identify a positive or negative reaction to the treatment over time. With the individual treatment effects and characteristics of the patients, various classification algorithms are applied to build prediction models for subgrouping. While many subgrouping approaches have been developed recently, most of them do not check its validity. In this paper, we further propose a simple validation approach which not only determines if the subgroups used are appropriate and beneficial but also compares methods to predict individual treatment effects. This entire procedure is readily implemented by existing packages in statistical software. The effectiveness of the proposed method is confirmed with simulation studies and analysis of data from the Women Entering Care study on depression.
在临床试验和生物医学研究中，会对各种治疗方法进行比较，以确定哪种方法对疾病有效；然而，个体对相同治疗的反应可能会有很大差异。我们针对纵向数据提出了一个完整的流程，该流程能够识别出可能从特定治疗中受益的人群亚组。我们使用随机效应线性模型对个体治疗效果进行纵向评估，其中随机效应可确定随着时间推移对治疗的正向或负向反应。结合个体治疗效果和患者特征，应用各种分类算法来构建用于亚组划分的预测模型。虽然最近已经开发出了许多亚组划分方法，但其中大多数都没有检验其有效性。在本文中，我们进一步提出了一种简单的验证方法，该方法不仅能确定所使用的亚组是否合适且有益，还能比较预测个体治疗效果的方法。整个过程可以通过统计软件中的现有程序轻松实现。通过模拟研究以及对女性抑郁症护理研究数据的分析，证实了所提方法的有效性。
