glmmPen: High Dimensional Penalized Generalized Linear Mixed Models

TL;DR

The paper introduces glmmPen, an R package that enables high-dimensional joint selection of fixed and random effects in generalized linear mixed models using penalization, with efficient estimation via MCECM algorithm.

Contribution

It presents one of the first methods for high-dimensional fixed and random effects selection in GLMMs using penalization and efficient computation.

Findings

Good performance in selecting effects in high-dimensional settings

Supports multiple distributions and penalty functions

Application to pancreatic cancer subtyping

Abstract

Generalized linear mixed models (GLMMs) are widely used in research for their ability to model correlated outcomes with non-Gaussian conditional distributions. The proper selection of fixed and random effects is a critical part of the modeling process since model misspecification may lead to significant bias. However, the joint selection of fixed and random effects has historically been limited to lower-dimensional GLMMs, largely due to the use of criterion-based model selection strategies. Here we present the R package glmmPen, one of the first to select fixed and random effects in higher dimension using a penalized GLMM modeling framework. Model parameters are estimated using a Monte Carlo Expectation Conditional Minimization (MCECM) algorithm, which leverages Stan and RcppArmadillo for increased computational efficiency. Our package supports the Binomial, Gaussian, and Poisson families and multiple penalty functions. In this manuscript we discuss the modeling procedure, estimation scheme, and software implementation through application to a pancreatic cancer subtyping study. Simulation results show our method has good performance in selecting both the fixed and random effects in high dimensional GLMMs.