calc_myreg {regmedint}

Return mediation analysis functions given mediator and outcome models specifications.

Description

This function returns functions that can be used to calculate the causal effect measures, given the mediator model fit (mreg_fit) and the outcome model fit (yreg_fit).

Usage

calc_myreg(
  mreg,
  mreg_fit,
  yreg,
  yreg_fit,
  avar,
  mvar,
  cvar,
  emm_ac_mreg,
  emm_ac_yreg,
  emm_mc_yreg,
  interaction
)

Arguments

mreg

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

mreg_fit

Model fit from fit_mreg

yreg

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

yreg_fit

Model fit from fit_yreg

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.

interaction

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

Value

A list containing two functions. The first is for calculating point estimates. The second is for calculating the correspoding