In mediational settings, the main focus is on the estimation of the indirect effect of an exposure on an outcome through a third variable called mediator. The traditional maximum likelihood estimation method presents several problems in the estimation of the standard error and the confidence interval of the indirect effect. In this paper, we propose a Bayesian approach to obtain the posterior distribution of the indirect effect through MCMC, in the context of mediational mixed models for longitudinal data. A simulation study shows that our method outperforms the traditional maximum likelihood approach in terms of bias and coverage rates.

Chiara Di Maria, Antonino Abbruzzo, & Gianfranco Lovison (2022). Bayesian causal mediation analysis through linear mixed-effect models. In Book of Short Papers - SIS2022.

Bayesian causal mediation analysis through linear mixed-effect models

Chiara Di Maria;Antonino Abbruzzo;Gianfranco Lovison
2022

Abstract

In mediational settings, the main focus is on the estimation of the indirect effect of an exposure on an outcome through a third variable called mediator. The traditional maximum likelihood estimation method presents several problems in the estimation of the standard error and the confidence interval of the indirect effect. In this paper, we propose a Bayesian approach to obtain the posterior distribution of the indirect effect through MCMC, in the context of mediational mixed models for longitudinal data. A simulation study shows that our method outperforms the traditional maximum likelihood approach in terms of bias and coverage rates.
Settore SECS-S/01 - Statistica
Chiara Di Maria, Antonino Abbruzzo, & Gianfranco Lovison (2022). Bayesian causal mediation analysis through linear mixed-effect models. In Book of Short Papers - SIS2022.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/566242
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