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Regression trees for longitudinal data with baseline covariates

Regression trees for longitudinal data with baseline covariates Longitudinal changes in a population of interest are often heterogeneous and influenced by a combination of baseline factors. In such cases, classical linear mixed effects models [Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974.] for the mean structure provide poor fit to the data. We propose regression tree methodology for the longitudinal data identifying and characterizing homogeneous subgroups. Currently available regression tree construction methods are either limited to a repeated measures scenario or combine the heterogeneity among subgroups with the random inter-subject variability. We propose a longitudinal classification and regression tree (LongCART) algorithm under conditional inference framework that overcomes these limitations utilizing a two-step approach. The LongCART first selects the partitioning variable via a parameter instability test and then finds the optimal split for the selected partitioning variable. Thus, at each node, the decision of further splitting is type I error controlled, guarding against variable selection bias, over-fitting and spurious splitting. We obtained asymptotic results for the proposed instability test and examined its finite sample behavior through simulation studies. Comparative performance of LongCART algorithm was evaluated empirically via simulation studies. Finally, we applied LongCART to study the longitudinal changes in choline levels among HIV-positive patients. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

Regression trees for longitudinal data with baseline covariates

Regression trees for longitudinal data with baseline covariates

Abstract

Longitudinal changes in a population of interest are often heterogeneous and influenced by a combination of baseline factors. In such cases, classical linear mixed effects models [Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974.] for the mean structure provide poor fit to the data. We propose regression tree methodology for the longitudinal data identifying and characterizing homogeneous subgroups. Currently available regression tree...
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Publisher
Taylor & Francis
Copyright
© 2018 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2018.1557797
Publisher site
See Article on Publisher Site

Abstract

Longitudinal changes in a population of interest are often heterogeneous and influenced by a combination of baseline factors. In such cases, classical linear mixed effects models [Laird NM, Ware JH. Random-effects models for longitudinal data. Biometrics. 1982;38:963–974.] for the mean structure provide poor fit to the data. We propose regression tree methodology for the longitudinal data identifying and characterizing homogeneous subgroups. Currently available regression tree construction methods are either limited to a repeated measures scenario or combine the heterogeneity among subgroups with the random inter-subject variability. We propose a longitudinal classification and regression tree (LongCART) algorithm under conditional inference framework that overcomes these limitations utilizing a two-step approach. The LongCART first selects the partitioning variable via a parameter instability test and then finds the optimal split for the selected partitioning variable. Thus, at each node, the decision of further splitting is type I error controlled, guarding against variable selection bias, over-fitting and spurious splitting. We obtained asymptotic results for the proposed instability test and examined its finite sample behavior through simulation studies. Comparative performance of LongCART algorithm was evaluated empirically via simulation studies. Finally, we applied LongCART to study the longitudinal changes in choline levels among HIV-positive patients.

Journal

Biostatistics & EpidemiologyTaylor & Francis

Published: Jan 1, 2019

Keywords: LongCART; regression tree; instability test; longitudinal data; mixed models; score process; brownian bridge

References