Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.

ABSTRACT:

Introduction

Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized a priori. We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods.

Methods

We conducted simulations comparing stepwise selection, penalized GLMM, boosted GLMM, and boosted GEE for variable selection considering main effects and two-way interactions in data with repeatedly measured binary outcomes and evaluate a two-stage ap

SUBMITTER: Wolf BJ 

PROVIDER: S-EPMC8057419 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature