The Conceptual Approach to Causal Mediation Analysis - Nursing Assignment Help

Assignment Task:

Objectives 

At the end of this module students will be able to: 

• Explain the model-based approach to statistical mediation analysis that was prevalent prior to the incorporation of the potential outcomes approach; 

• Explain the conceptual approach to causal mediation analysis based on the potentials outcomes framework for causal inference; 

• Use nested potential outcomes to define average, direct and indirect causal effects, including controlled direct effects, and natural (in)direct effects; 

• Explain the meaning of the causal effect of an exposure on an outcome being mediated by a third intermediate variable; 

• Perform and interpret the output from a basic, single-variable mediation anal ysis in R or Stata. 

Introduction 

Recall Example 2.7 from Module 2 where the causal effect of physical activity (PA) on the risk of cardiovascular disease (CVD) involved an intermediate variable, body size, for which we use body mass index (BMI) as a proxy measure. In that example, there were two causal paths from PA to CVD: one directly from PA to CVD, and another via the BMI, that is, PA −→ BMI −→ CVD. We interpret the direct path from PA to CVD as the causal effect of PA on CVD via pathways other than those that lead to PA reducing BMI. We would like to estimate the magnitude of the effect of PA on CVD through each of these causal pathways, and do so in such a way that these two component causal effects sum to the average causal effect define in Module 1. 

This is an example of a problem that requires mediation analysis, a technique that seeks to understand contributions of intermediate variables to the causal effect when there is more than one causal path from an exposure to an outcome. The application of the potential outcomes approach to causal inference to mediation analysis led to a complete overhaul of the way that statisticians think about mediated pathways and has resulted in many theoretical and methodological advances in the last 30 years. These advances have provided definitions for mediated effects that have causal interpretations, clarified the assumptions required to identify these effects in data structures from observational studies, and led to a large array of techniques for effect estimation and associated software packages in which these methods are implemented. 

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