Mediation Analysis With Multiple Exposures And Multiple Mediators Deepai

Mediation Analysis With Multiple Exposures And Multiple Mediators Deepai In this article, we develop a general framework for causal mediation analysis with multiple exposures, multivariate mediators, and continuous, binary, and survival responses. A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework.

A Framework For Mediation Analysis With Multiple Exposures In this article, we develop a general framework for causal mediation analysis with multiple exposures, multivariate mediators, and continuous, binary, and survival responses. A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. 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. In this paper, we develop a simple two step method of moments estimation procedure to assess mediation with multiple mediators simultaneously in the presence of potential unmeasured mediator outcome confounding.

A Framework For 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. In this paper, we develop a simple two step method of moments estimation procedure to assess mediation with multiple mediators simultaneously in the presence of potential unmeasured mediator outcome confounding. In this paper, we develop a simple two step method of moments estimation procedure to assess mediation with multiple mediators simultaneously in the presence of potential unmeasured mediator outcome confounding. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. Motivated by an imaging proteomics study for alzheimer's disease (ad), in this article, we propose a mediation analysis approach with high dimensional exposures and high dimensional mediators to integrate data collected from multiple platforms. A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework.