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arXiv · 1509.07510

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

Lei Gong·James M. Flegal·Stephen R. Spindler·Patricia L. Mote
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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.

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Lei Gong, James M. Flegal, Stephen R. Spindler, Patricia L. Mote. 2015-09-24. Bayesian model selection on linear mixed-effects models for comparisons between multiple treatments and a control. https://arxiv.org/abs/1509.07510

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