Grouped Generalized Estimating Equations for Longitudinal Data Analysis

TL;DR

This paper introduces a grouped GEE approach for longitudinal data that accounts for heterogeneity in regression coefficients by dividing subjects into groups, with an algorithm for simultaneous grouping and estimation.

Contribution

It proposes a novel grouped GEE method that models heterogeneity in regression coefficients and includes an algorithm for group determination and estimation.

Findings

Effective in capturing heterogeneity among subjects

Demonstrated superior performance in simulations

Successfully applied to real data analysis

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 dataset.