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PARAMED: Stata module to perform causal mediation analysis using parametric regression models
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2013
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Treatment EffectCausal Relation ExtractionCausal InferencePsychologyStata ModuleCausal Mediation AnalysisPublic HealthStatisticsStructural Equation ModelingCausal ModelBehavioral SciencesBinary MediatorsCausal ReasoningMarginal Structural ModelsTime-varying ConfoundingCausalityParametric Regression ModelsMedicineOutcome Regression ModelPersuasion
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.