B0805
Title: Grouped generalized estimating equations for heterogeneous longitudinal data
Authors: Tsubasa Ito - Hokkaido University (Japan) [presenting]
Shonosuke Sugasawa - Keio University (Japan)
Abstract: Generalized estimating equation (GEE) is widely adopted for regression modelling 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 is not realistic when there are potential heterogeneities in regression coefficients among subjects. A flexible and interpretable approach, called grouped GEE analysis, is proposed to model longitudinal data by allowing heterogeneity in regression coefficients. The proposed method assumes that the subjects are divided into a finite number of groups and that subjects within the same group share the same regression coefficient. A simple algorithm for grouping subjects and estimating the regression coefficients simultaneously is proposed, and the asymptotic properties of the proposed estimator are shown. Finally, the proposed methods are demonstrated through simulation studies and a real dataset in biology.
