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PARAMED: Stata module to perform causal mediation analysis using parametric regression models

Richard Emsley () and Hanhua Liu ()
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Richard Emsley: University of Manchester
Hanhua Liu: University of Manchester

Statistical Software Components from Boston College Department of Economics

Abstract: paramed performs causal mediation analysis using parametric regression models. Two models are estimated: a model for the mediator conditional on treatment (exposure) and covariates (if specified), and a model for the outcome conditional on treatment (exposure), the mediator and covariates (if specified). It extends statistical mediation analysis (widely known as Baron and Kenny procedure) to allow for the presence of treatment (exposure)-mediator interactions in the outcome regression model using counterfactual definitions of direct and indirect effects. paramed allows continuous, binary or count outcomes, and continuous or binary mediators, and requires the user to specify an appropriate form for the regression models. paramed provides estimates of the controlled direct effect, the natural direct effect, the natural indirect effect and the total effect with standard errors and confidence intervals derived using the delta method by default, with a bootstrap option also available.

Language: Stata
Requires: Stata version 10
Keywords: mediation; causal mediation effects; continuous outcomes; count outcomes; binary outcomes (search for similar items in EconPapers)
Date: 2013-01-17, Revised 2013-04-26
Note: This module should be installed from within Stata by typing "ssc install paramed". The module is made available under terms of the GPL v3 ( Windows users should not attempt to download these files with a web browser.
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