弹道
推论
集合(抽象数据类型)
心理干预
因果推理
计算机科学
群(周期表)
心理学
人工智能
机器学习
数据挖掘
数据科学
计量经济学
数学
精神科
物理
有机化学
化学
程序设计语言
天文
作者
Daniel S. Nagin,Candice L. Odgers
标识
DOI:10.1146/annurev.clinpsy.121208.131413
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.
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