regmedint (version 1.0.1)

regmedint: regmedint: A package for regression-based causal mediation analysis

Description

The package is an R implementation of regression-based closed-form causal mediation as originally described in Valeri & VanderWeele 2013 and Valeri & VanderWeele 2015 https://www.hsph.harvard.edu/tyler-vanderweele/tools-and-tutorials/. The earlier version is a sister program of the SAS macro. The current extended version (version 1.0 and later) supports effect modification by covariates (treatment-covariate and mediator-covariate product terms) in mediator and outcome models.

This is a user-interface for regression-based causal mediation analysis as described in Valeri & VanderWeele 2013 and Valeri & VanderWeele 2015.

Usage

regmedint(
  data,
  yvar,
  avar,
  mvar,
  cvar,
  emm_ac_mreg = NULL,
  emm_ac_yreg = NULL,
  emm_mc_yreg = NULL,
  eventvar = NULL,
  a0,
  a1,
  m_cde,
  c_cond,
  mreg,
  yreg,
  interaction = TRUE,
  casecontrol = FALSE,
  na_omit = FALSE
)

Value

regmedint object, which is a list containing the mediator regression object, the outcome regression object, and the regression-based mediation results.

Arguments

data

Data frame containing the following relevant variables.

yvar

A character vector of length 1. Outcome variable name. It should be the time variable for the survival outcome.

avar

A character vector of length 1. Treatment variable name.

mvar

A character vector of length 1. Mediator variable name.

cvar

A character vector of length > 0. Covariate names. Use NULL if there is no covariate. However, this is a highly suspicious situation. Even if avar is randomized, mvar is not. Thus, there are usually some confounder(s) to account for the common cause structure (confounding) between mvar and yvar.

emm_ac_mreg

A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the mediator model.

emm_ac_yreg

A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the outcome model.

emm_mc_yreg

A character vector of length > 0. Effect modifiers names. The covariate vector in mediator-covariate product term in outcome model.

eventvar

An character vector of length 1. Only required for survival outcome regression models. Note that the coding is 1 for event and 0 for censoring, following the R survival package convention.

a0

A numeric vector of length 1. The reference level of treatment variable that is considered "untreated" or "unexposed".

a1

A numeric vector of length 1.

m_cde

A numeric vector of length 1. Mediator level at which controlled direct effect is evaluated at.

c_cond

A numeric vector of the same length as cvar. Covariate levels at which natural direct and indirect effects are evaluated at.

mreg

A character vector of length 1. Mediator regression type: "linear" or "logistic".

yreg

A character vector of length 1. Outcome regression type: "linear", "logistic", "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull".

interaction

A logical vector of length 1. The presence of treatment-mediator interaction in the outcome model. Default to TRUE.

casecontrol

A logical vector of length 1. Default to FALSE. Whether data comes from a case-control study.

na_omit

A logical vector of length 1. Default to FALSE. Whether to remove NAs in the columns of interest before fitting the models.

Fitting models

Use the regmedint function to fit models and set up regression-based causal mediation analysis.

Examining results

Several methods are available to examine the regmedint object. print summary coef confint

Examples

library(regmedint)
data(vv2015)
regmedint_obj1 <- regmedint(data = vv2015,
                            ## Variables
                            yvar = "y",
                            avar = "x",
                            mvar = "m",
                            cvar = c("c"),
                            eventvar = "event",
                            ## Values at which effects are evaluated
                            a0 = 0,
                            a1 = 1,
                            m_cde = 1,
                            c_cond = 3,
                            ## Model types
                            mreg = "logistic",
                            yreg = "survAFT_weibull",
                            ## Additional specification
                            interaction = TRUE,
                            casecontrol = FALSE)
summary(regmedint_obj1)

regmedint_obj2 <- regmedint(data = vv2015,
                            ## Variables
                            yvar = "y",
                            avar = "x",
                            mvar = "m",
                            cvar = c("c"),
                            emm_ac_mreg = c("c"), 
                            emm_ac_yreg = c("c"), 
                            emm_mc_yreg = c("c"), 
                            eventvar = "event",
                            ## Values at which effects are evaluated
                            a0 = 0,
                            a1 = 1,
                            m_cde = 1,
                            c_cond = 3,
                            ## Model types
                            mreg = "logistic",
                            yreg = "survAFT_weibull",
                            ## Additional specification
                            interaction = TRUE,
                            casecontrol = FALSE)
summary(regmedint_obj2)