Bayesian model selection on linear mixed-effects models for comparisons between multiple treatments and a control

TL;DR

This paper introduces a Bayesian model selection method for linear mixed-effects models to compare multiple treatments against a control, providing direct probabilistic measures of treatment effects.

Contribution

It develops a fully Bayesian approach with default priors and an efficient Gibbs sampler for model selection in treatment comparison studies.

Findings

Effective in simulated data scenarios

Successfully applied to longitudinal mouse data

Provides clear probabilistic treatment effect measures

Abstract

We propose a novel Bayesian model selection technique on linear mixed-effects models to compare multiple treatments with a control. A fully Bayesian approach is implemented to estimate the marginal inclusion probabilities that provide a direct measure of the difference between treatments and the control, along with the model-averaged posterior distributions. Default priors are proposed for model selection incorporating domain knowledge and a component-wise Gibbs sampler is developed for efficient posterior computation. We demonstrate the proposed method based on simulated data and an experimental dataset from a longitudinal study of mouse lifespan and weight trajectories.