Return mediation analysis functions given mediator and outcome models specifications.

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).

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