Understanding how health interventions or exposures produce their effects using mediation analysis : vimarsana.com

Understanding how health interventions or exposures produce their effects using mediation analysis

Mediation analysis is a method that quantifies how health exposures, such as medical interventions, change patient outcomes. Evidence that is generated from mediation analyses is important for intervention development and clinical and policy decision making. Mediation analysis has many applications that require specific and careful consideration for design, conduct, analysis, and interpretation. This article outlines motivations, effect types, causal assumptions, estimation, and reporting guidance for mediation analysis studies that aim to improve their conduct, interpretation, and implementation.

The reasons that health exposures, such as medical interventions, change patient outcomes are often poorly understood. Health exposures change patient outcomes through biological, psychological, and social mediators. Generally, a mediator is a variable that lies on the causal path between an exposure and an outcome. The causal role of a mediator can be communicated through a directed acyclic graph,1 which visually represents the direction of causal effects from exposures to outcomes and distinguishes between other variables such as confounders and colliders2 (fig 1). In health research, identifying mediators has the potential to inform theory, optimise interventions, and facilitate the implementation of policies and interventions in clinical and public health practice.3 The value of identifying the mediators of health interventions has been noted by the UK National Institute for Health and Care Research and the US National Institutes of Health.45 The mediators of health exposures in randomised trials and observational studies can be quantified using mediation analysis.67



Fig 1
A directed acyclic graph visually representing causal effects and variables relevant to mediation analysis. A causal path in a directed acyclic graph is represented by a sequence of variables connected by arrows. Figure shows a causal path from the exposure to outcome, and from the exposure to outcome through the mediator. This directed path follows the arrow direction from cause …

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United Kingdom , Aidang Cashin , Jamesh Mcauley , Tylerj Vanderweele , National Health , Research Council Leadership Investigator Grant , Care Research , United Kingdom National Institute For Health , Research Council Emerging Leadership Investigator Grant , Us National Institutes Of Health , United Kingdom National Institute , Statistical Horizons ,

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