Subgroup analysis based on structured mixed-effects models for longitudinal data

ABSTRACT

In 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.

KEYWORDS:

• EM algorithm
• heterogeneous components
• mixed-effects models
• mixture model
• subgroup identification

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (11501123, 11871165, 11690012), and the National Science Foundation (DMS 1613190 and DMS 182119). In addition, the authors are very grateful for constructive comments and suggestions from Associate Editor and referees.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China (11501123, 11871165, 11690012), and the National Science Foundation (DMS 1613190 and DMS 182119).