Subgroup analysis based on structured mixed-effects models for longitudinal data
Year: 2020
Type: article
Abstract: Juan Shena & Annie Qub* a Department of Statistics, Fudan University, Shanghai, Chinab Department of Statistics, University of California at Irvine, Irvine, California, USACONTACT Annie Qu [email protected] Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, USAABSTRACTIn recent years, subgroup analysis has emerged as an important tool to identify unknown subgroup memberships. However, subgroup analysis is still under-studied for longitudinal data. In this paper, we propose a structured mixed-effects approach for longitudinal data to model subgroup distribution and identify subgroup membership simultaneously. In the proposed structured mixed-effects model, the heterogeneous treatment effect is modeled as a random effect from a two-component mixture model, while the membership of the mixture model is incorporated using a logistic model with respect to some covariates. One advantage of our approach is that we are able to derive the estimation of the treatment effects through an EM-type algorithm which keeps the subgroup membership unchanged over time. Our numerical studies and real data example demonstrate that the proposed model outperforms other competing methods.
Authors: Juan Shen, Annie Qu
Institutions: Fudan University, University of California, Irvine
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W3010477577