Dataset Information

A Bayesian regularized mediation analysis with multiple exposures.

ABSTRACT: Mediation analysis assesses the effect of study exposures on an outcome both through and around specific mediators. While mediation analysis involving multiple mediators has been addressed in recent literature, the case of multiple exposures has received little attention. With the presence of multiple exposures, we consider regularizations that allow simultaneous effect selection and estimation while stabilizing model fit and accounting for model selection uncertainty. In the framework of linear structural-equation models, we analytically show that a two-stage approach regularizing regression coefficients does not guarantee a unimodal posterior distribution and that a product-of-coefficient approach regularizing direct and indirect effects tends to penalize excessively. We propose a regularized difference-of-coefficient approach that bypasses these limitations. Using the connection between regularizations and Bayesian hierarchical models with Laplace prior, we develop an efficient Markov chain Monte Carlo algorithm for posterior estimation and inference. Through simulations, we show that the proposed approach has better empirical performances compared to some alternatives. The methodology is illustrated using data from two epidemiological studies in human reproduction.

SUBMITTER: Wang YB

PROVIDER: S-EPMC8327705 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Bayesian regularized mediation analysis with multiple exposures.

Wang Yu-Bo YB Chen Zhen Z Goldstein Jill M JM Buck Louis Germaine M GM Gilman Stephen E SE

Statistics in medicine 20181029 5

Mediation analysis assesses the effect of study exposures on an outcome both through and around specific mediators. While mediation analysis involving multiple mediators has been addressed in recent literature, the case of multiple exposures has received little attention. With the presence of multiple exposures, we consider regularizations that allow simultaneous effect selection and estimation while stabilizing model fit and accounting for model selection uncertainty. In the framework of linear ...[more]

PMID: 30375022

Similar Datasets

Project description:BackgroundThe past decade has seen an explosion of research in causal mediation analysis. However, most analytic tools developed so far rely on frequentist methods which may not be robust in the case of small sample sizes. In this paper, we propose a Bayesian approach for causal mediation analysis based on Bayesian g-formula, which will overcome the limitations of the frequentist methods.MethodsWe created BayesGmed, an R-package for fitting Bayesian mediation models in R. The application of the methodology (and software tool) is demonstrated by a secondary analysis of data collected as part of the MUSICIAN study, a randomised controlled trial of remotely delivered cognitive behavioural therapy (tCBT) for people with chronic pain. We tested the hypothesis that the effect of tCBT would be mediated by improvements in active coping, passive coping, fear of movement and sleep problems. We then demonstrate the use of informative priors to conduct probabilistic sensitivity analysis around violations of causal identification assumptions.ResultThe analysis of MUSICIAN data shows that tCBT has better-improved patients' self-perceived change in health status compared to treatment as usual (TAU). The adjusted log-odds of tCBT compared to TAU range from 1.491 (95% CI: 0.452-2.612) when adjusted for sleep problems to 2.264 (95% CI: 1.063-3.610) when adjusted for fear of movement. Higher scores of fear of movement (log-odds, -0.141 [95% CI: -0.245, -0.048]), passive coping (log-odds, -0.217 [95% CI: -0.351, -0.104]), and sleep problem (log-odds, -0.179 [95% CI: -0.291, -0.078]) leads to lower odds of a positive self-perceived change in health status. The result of BayesGmed, however, shows that none of the mediated effects are statistically significant. We compared BayesGmed with the mediation R- package, and the results were comparable. Finally, our sensitivity analysis using the BayesGmed tool shows that the direct and total effect of tCBT persists even for a large departure in the assumption of no unmeasured confounding.ConclusionThis paper comprehensively overviews causal mediation analysis and provides an open-source software package to fit Bayesian causal mediation models.

Dataset Information

A Bayesian regularized mediation analysis with multiple exposures.

Publications

A Bayesian regularized mediation analysis with multiple exposures.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets