Tree-Structured Methods for Longitudinal Data
作者:
MarkRobert Segal,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 418
页码: 407-418
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10475220
出版商: Taylor & Francis Group
关键词: Covariance structure;Human immune virus (HIV);Missing values;Multiple response;Regression tree
数据来源: Taylor
摘要:
The thrust of tree techniques is the extraction of meaningful subgroups characterized by common covariate values and homogeneous outcome. For longitudinal data, this homogeneity can pertain to the mean and/or to covariance structure. The regression tree methodology is extended to repeated measures and longitudinal data by modifying the split function so as to accommodate multiple responses. Several split functions are developed based either on deviations around subgroup mean vectors or on two sample statistics measuring subgroup separation. For the methods to be computationally feasible, it is necessary to devise updating algorithms for the split function. This has been done for some commonly used covariance specifications: independence, compound symmetry, and first-order autoregressive models. Data analytic issues, such as handling missing values and time-varying covariates and determining appropriate tree size are discussed. An illustrative example concerning immune function loss in a cohort of human immunodeficiency virus (HIV)-seropositive gay men also is presented.
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