BayesGmed (version 0.0.3)

bayesgmed: Estimate a causal mediation effects

Description

Estimates various quantities for causal mediation analysis using 'Stan'.

Usage

bayesgmed(
  outcome,
  mediator,
  treat,
  covariates = NULL,
  dist.y = "continuous",
  dist.m = "continuous",
  link.y = "identity",
  link.m = "identity",
  data,
  priors = NULL,
  ...
)

Value

An object of 'S4' class 'stanfit', with all its available methods.

Arguments

outcome

a character string indicating the name of the outcome variable.

mediator

a character string indicating the name of the mediator variable.

treat

a character string indicating the name of the treatment variable. The treatment variable is considered binary and should be coded as 0 for control and 1 for treated.

covariates

a character vector indicating the name of the confounding variables.

dist.y

a character string indicating the family distribution of the outcome. E.g., dist.y = "bernoulli" will fit a logistic regression for the outcome.

dist.m

a character string indicating the family distribution of the mediator, E.g., dist.m = "bernoulli" will fit a logistic regression model for the mediator

link.y

a character string indicating the link function to be used for the outcome model.

link.m

a character string indicating the link function to be used for the mediator model.

data

A data.frame or a tibble object.

priors

A list of named values for the prior scale parameters. See details.

...

Other optional parameters passed to rstan::stan().

Author

Belay Birlie Yimer

Details

This is the main function for estimating causal mediation effects from several types of data within the Bayesian framework. We followed the potential outcome framework for effects definition and the package uses the 'rstan' utility functions for exploring the posterior distribution.

priors

Users may pass a list of named values for the priors argument. The values will be used to define the scale parameter of the respective prior distributions. This list may specify some or all of the following parameters: priors <- list( scale_m = 2.5diag(P_m) scale_y = 2.5diag(P_y), location_m = rep(0, P_m) location_y = rep(0, P_y), scale_sd_y = 2.5, scale_sd_m = 2.5) where P_m is the number of regression parameters (including the intercept) in the mediator model and P_y is the number of regression parameters in the outcome model.

References

1. McCandless, L.C. and J.M. Somers, Bayesian sensitivity analysis for unmeasured confounding in causal mediation analysis. Statistical Methods in Medical Research, 2019. (28)(2): p. 515-531.

2. Comment, L., Coull, B. A., Zigler, C., and Valeri, L. (2019). Bayesian data fusion for unmeasured confounding. arXiv preprint arXiv:1902.10613.

Examples

# \donttest{
## Run example using the example_data
data(example_data)
fit1 <- bayesgmed(outcome = "Y", mediator =  "M", treat = "A", covariates = c("Z1", "Z2"),
 dist.y = "binary", dist.m = "binary", link.y = "logit", link.m = "logit", data = example_data)
bayesgmed_summary(fit1)

# With priors
P <- 3 # number of covariates plus the intercept term 
priors <- list(scale_m = 2.5*diag(P+1), scale_y = 2.5*diag(P+2), 
location_m = rep(0, P+1), location_y = rep(0, P+2))
fit1 <- bayesgmed(outcome = "Y", mediator =  "M", treat = "A", covariates = c("Z1", "Z2"), 
dist.y = "binary", dist.m = "binary", link.y = "logit", link.m = "logit", priors = priors,  
data = example_data)
bayesgmed_summary(fit1)
# }