Lemon Stephenie C, Roy Jason, Clark Melissa A, Friedmann Peter D, Rakowski William
Brown University School of Medicine, USA.
Ann Behav Med. 2003 Dec;26(3):172-81. doi: 10.1207/S15324796ABM2603_02.
Audience segmentation strategies are of increasing interest to public health professionals who wish to identify easily defined, mutually exclusive population subgroups whose members share similar characteristics that help determine participation in a health-related behavior as a basis for targeted interventions. Classification and regression tree (C&RT) analysis is a nonparametric decision tree methodology that has the ability to efficiently segment populations into meaningful subgroups. However, it is not commonly used in public health.
This study provides a methodological overview of C&RT analysis for persons unfamiliar with the procedure.
An example of a C&RT analysis is provided and interpretation of results is discussed. Results are validated with those obtained from a logistic regression model that was created to replicate the C&RT findings. Results obtained from the example C&RT analysis are also compared to those obtained from a common approach to logistic regression, the stepwise selection procedure. Issues to consider when deciding whether to use C&RT are discussed, and situations in which C&RT may and may not be beneficial are described.
C&RT is a promising research tool for the identification of at-risk populations in public health research and outreach.
受众细分策略越来越受到公共卫生专业人员的关注，他们希望识别易于定义、相互排斥的人群亚组，这些亚组的成员具有相似的特征，有助于确定参与健康相关行为的情况，以此作为有针对性干预措施的基础。分类与回归树（C&RT）分析是一种非参数决策树方法，能够有效地将人群细分为有意义的亚组。然而，它在公共卫生领域并不常用。
本研究为不熟悉该程序的人员提供C&RT分析的方法概述。
提供了一个C&RT分析的示例，并讨论了结果的解释。结果与通过创建的逻辑回归模型获得的结果进行了验证，该模型旨在复制C&RT的研究结果。示例C&RT分析获得的结果还与逻辑回归的常用方法逐步选择程序获得的结果进行了比较。讨论了决定是否使用C&RT时需要考虑的问题，并描述了C&RT可能有益和可能无益的情况。
C&RT是公共卫生研究和推广中识别高危人群的一种有前景的研究工具。
