How to apply Bayesian stochastic search variable selection with multiply imputed data

Modern regularization and variable selection methods, such as least absolute shrinkage and selection operator (lasso) and Bayesian variable selection, are important tools for psychological researchers to reduce the risk of overfitting, improve prediction in future samples, and increase model interpretability. Although missing data are common in psychological data, it is not straightforward to combine principled methods for addressing missing data with these modern variable selection methods. This challenge is well illustrated in a recent article by Gunn et al. (2023) with a comparison of three approaches for combining lasso with multiple imputation to address missing data. Each of the surveyed approaches results in markedly different results in terms of predictors selected. Their findings underscore limitations of the lasso for the purpose of variable selection. In this article, we show how to implement a Bayesian variable selection method, stochastic search variable selection (SSVS), with multiply imputed data. SSVS is a principled and consistent method for variable selection, and we demonstrate advantages relative to lasso in an example data set and simulation study. It is straightforward to apply an ITS strategy for SSVS using existing software. (PsycInfo Database Record (c) 2026 APA, all rights reserved).