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Validating effectiveness of subgroup identification for longitudinal data

为纵的数据验证亚群鉴定的有效性

作     者:Andrews, Nichole Cho, Hyunkeun 

作者机构:Western Michigan Univ Dept Stat Kalamazoo MI 49008 USA Univ Iowa Dept Biostat Iowa City IA 52242 USA 

出 版 物:《STATISTICS IN MEDICINE》 (Stat. Med.)

年 卷 期:2018年第37卷第1期

页      面:98-106页

核心收录:

学科分类:0710[理学-生物学] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1001[医学-基础医学(可授医学、理学学位)] 0714[理学-统计学(可授理学、经济学学位)] 10[医学] 

主  题:classification algorithm effectiveness of subgrouping personalized treatment random effects linear model 

摘      要: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.

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