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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

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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 & Epidemiology – Taylor & Francis

Published: Jan 1, 2019

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

References

  • Random-effects models for longitudinal data
  • Longitudinal data analysis using generalized linear models
  • Comparing personal trajectories and drawing causal inferences from longitudinal data
  • Finite mixture modeling with mixture outcomes using the EM algorithm
  • Tree-structured methods for longitudinal data
  • Binary partitioning for continuous longitudinal data: categorizing a prognostic variable
  • Unbiased recursive partitioning: a conditional inference framework
  • Tree-structured subgroup analysis for censored survival data: validation of computationally inexpensive model selection criteria
  • Regression trees with unbiased variable selection and interaction detection
  • A note on split selection bias in classification trees
  • Unbiased split selection for classification trees based on the Gini index
  • Regression trees for longitudinal and multiresponse data
  • Techniques for testing the constancy of regression relationships over time
  • Testing for the constancy of parameters over time
  • Tests for constancy of model parameters over time
  • Model-based recursive partitioning
  • Tests for parameter instability and structural change with unknown change point
  • Approximate asymptotic p values for structuraS-change tests
  • Multivariate regression trees: a new technique for modeling species-environment relationships
  • Multivariate regression trees for analysis of abundance data
  • Multivariate adaptive splines for analysis of longitudinal data
  • RE-EM trees: a data mining approach for longitudinal and clustered data
  • Regression trees for longitudinal data with time-dependent covariates
  • Unbiased regression trees for longitudinal and clustered data
  • Tree-structured mixed-effects regression modeling for longitudinal data
  • A glimpse of the impact of pál erd# ous on probability and statistics
  • On the Kolmogorov-Smirnov test for normality with mean and variance unknown
  • The Kolmogorov-Smirnov test for goodness of fit
  • Numerical tabulation of the distribution of Kolmogorov's statistic for finite sample size
  • A sharper Bonferroni procedure for multiple tests of significance
  • Progressive cerebral injury in the setting of chronic HIV infection and antiretroviral therapy
  • Relationships among brain metabolites, cognitive function, and viral loads in antiretroviral-naıve HIV patients