R package bayesmsm

This package provides tools for estimating causal effects using Bayesian Marginal Structural Models. It includes functions for estimating Bayesian weights using JAGS and for Bayesian non-parametric bootstrap to calculate causal effects.

Reference

Reference paper on Bayesian marginal structural models:

• Saarela, O., Stephens, D. A., Moodie, E. E., & Klein, M. B. (2015). On Bayesian estimation of marginal structural models. Biometrics, 71(2), 279-288.

• Liu, K., Saarela, O., Feldman, B. M., & Pullenayegum, E. (2020). Estimation of causal effects with repeatedly measured outcomes in a Bayesian framework. Statistical methods in medical research, 29(9), 2507-2519.

Installation

Install using devtools package:

## install.packages(devtools) ## make sure to have devtools installed 
devtools::install_github("Kuan-Liu-Lab/bayesmsm")
library(bayesmsm)

Dependency

This package depends on the following R packages:

• MCMCpack
• doParallel
• foreach
• parallel
• R2jags
• coda

Examples

Here are some examples demonstrating how to use the bayesmsm package:

# Load example data
testdata <- read.csv(system.file("extdata", "continuous_outcome_data.csv", package = "bayesmsm"))

# Calculate Bayesian weights
weights <- bayesweight(
  trtmodel.list = list(
    a_1 ~ w1 + w2 + L1_1 + L2_1,
    a_2 ~ w1 + w2 + L1_1 + L2_1 + L1_2 + L2_2 + a_1
  ),
  data = testdata,
  n.iter = 250,
  n.burnin = 150,
  n.thin = 5,
  n.chains = 2,
  seed = 890123,
  parallel = TRUE
)

# Perform Bayesian non-parametric bootstrap
model <- bayesmsm(
  ymodel = y ~ a_1 + a_2,
  nvisit = 2,
  reference = c(rep(0, 2)),
  comparator = c(rep(1, 2)),
  family = "gaussian",
  data = testdata,
  wmean = weights,
  nboot = 1000,
  optim_method = "BFGS",
  seed = 890123,
  parallel = TRUE,
  ncore = 2
)

# View model summary
summary.bayesmsm(model)

Documentation

For comprehensive documentation and examples, visit the Vignette.

License

This package is licensed under the MIT License. See the LICENSE file for details.

Citation

If you use the bayesmsm package in your research, please cite the reference papers listed above.

Developers

• Kuan Liu
• Xiao Yan