Grouped generalized estimating equations for longitudinal data analysis

Tsubasa Ito, Shonosuke Sugasawa

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such an assumption may not be realistic when there is potential heterogeneity in regression coefficients among subjects. In this paper, we develop a flexible and interpretable approach, called grouped GEE analysis, to modeling longitudinal data with allowing heterogeneity in regression coefficients. The proposed method assumes that the subjects are divided into a finite number of groups and subjects within the same group share the same regression coefficient. We provide a simple algorithm for grouping subjects and estimating the regression coefficients simultaneously, and show the asymptotic properties of the proposed estimator. The number of groups can be determined by the cross validation with averaging method. We demonstrate the proposed method through simulation studies and an application to a real data set.

Original languageEnglish
Pages (from-to)1868-1879
Number of pages12
JournalBiometrics
Volume79
Issue number3
DOIs
Publication statusPublished - 2023 Sept
Externally publishedYes

Keywords

  • estimating equation
  • grouping
  • k-means algorithm
  • unobserved heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics

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