Regression-Based Causal Mediation Analysis with Interaction and Effect Modification Terms
Create a vector of coefficients from the mediator model (mreg)
Return mediation analysis functions given mediator and outcome models ...
Create calculators for effects and se (mreg linear / yreg linear)
Create calculators for effects and se (mreg linear / yreg logistic)
Create calculators for effects and se (mreg logistic / yreg linear)
Create calculators for effects and se (mreg logistic / yreg logistic)
Extract point estimates.
Extract the result matrix from a summary_regmedint object.
Confidence intervals for mediation prameter estimates.
Fit a model for the mediator given the treatment and covariates.
Fit a model for the outcome given the treatment, mediator, and covaria...
Calculate the gradient of the proportion mediated for yreg linear.
Calculate the gradient of the proportion mediated for yreg logistic.
Low level constructor for a regmedint S3 class object.
print method for regmedint object
Print method for summary objects from summary.regmedint
Calculate the proportion mediated for yreg linear.
Calculate the proportion mediated for yreg logistic.
regmedint: A package for regression-based causal mediation analysis
Report variables with missing data
summary method for regmedint object
Summary with robust sandwich variance estimator for modified Poisson
Create a vector of coefficients from the outcome model (yreg)
Validate arguments to regmedint before passing to other functions
Validate soundness of a regmedint object.
Extract variance estimates in the vcov form.
Robust sandwich variance estimator for modified Poisson
This is an extension of the regression-based causal mediation analysis first proposed by Valeri and VanderWeele (2013) <doi:10.1037/a0031034> and Valeri and VanderWeele (2015) <doi:10.1097/EDE.0000000000000253>). It supports including effect measure modification by covariates(treatment-covariate and mediator-covariate product terms in mediator and outcome regression models) as proposed by Li et al (2023) <doi:10.1097/EDE.0000000000001643>. It also accommodates the original 'SAS' macro and 'PROC CAUSALMED' procedure in 'SAS' when there is no effect measure modification. Linear and logistic models are supported for the mediator model. Linear, logistic, loglinear, Poisson, negative binomial, Cox, and accelerated failure time (exponential and Weibull) models are supported for the outcome model.