Group-Based Trajectory Modeling in Clinical Research

被引：1948

作者：

Nagin, Daniel S. ^{[1
]}

Odgers, Candice L. ^{[2
]}

机构：

[1] Carnegie Mellon Univ, Heinz Sch Publ Policy, Pittsburgh, PA 15213 USA

[2] Univ Calif Irvine, Sch Social Ecol, Irvine, CA 92697 USA

来源：

ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL 6 | 2010年 / 6卷

关键词：

growth mixture modeling; longitudinal data; dual trajectory models; causal inference; ANALYZING DEVELOPMENTAL TRAJECTORIES; GROWTH MIXTURE-MODELS; CAUSAL INFERENCES; PROPENSITY SCORE; DISTRIBUTIONAL ASSUMPTIONS; DEPRESSIVE SYMPTOMS; GRADE RETENTION; SUBSTANCE-ABUSE; CHILDHOOD; HETEROGENEITY;

D O I：

10.1146/annurev.clinpsy.121208.131413

中图分类号：

B849 [应用心理学];

学科分类号：

040203 ;

摘要：

Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.

引用

页码：109 / 138

页数：30

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