mediator: Causal Mediation Analysis

In jhcreed/mediator: Causal Mediation Analysis

Description

The 'mediator' R function conducts mediation analysis under the counterfactual framework assuming interation between the exposure and mediator. Currently the function works for binary and continuous outcomes and mediators.

Usage

mediator(...)

## Default S3 method:
mediator(
  data,
  out.model,
  med.model,
  treat,
  a = 1,
  a_star = 0,
  m = 0,
  boot_rep = 0,
  pm_ci = FALSE,
  ...
)

Arguments

...	other arguments
data	Data set to use for analysis
out.model	A fitted model object for the outcome. Can be of class 'glm','lm', or 'coxph'.
med.model	A fitted model object for the mediator. Can be of class 'glm','lm'.
treat	A character string indicating the name of the treatment/exposure variable used.
a	A numeric value indicating the exposure level. Default = 1
a_star	A numeric value indicating the compared exposure level. Default = 0.
m	A numeric value indicating the level of the mediator. Default = 0.
boot_rep	A numeric value indicating the number of repetitions to use when utalizing bootstrap to calculate confidence intervals. When 'boot_rep' = 0, the Delta method for calculating confidence intervals is used. Default = 0.
pm_ci	A logical indicator for calculating the CI for the proportion mediated. Default = FALSE. Currently, the CI can only be determined using boostrapping. If 'pm_ci' = TRUE and 'boot_rep' = 0 then 100 replicated are automatically used.

Value

Tibble containing point estimates and 95 percent CI for the CDE, NDE, NIE and TE and the point estimate for the proportion mediated.

jhcreed/mediator documentation built on Dec. 13, 2020, 12:43 p.m.