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bayesmsm is an R package that implements the Bayesian marginal structural models to estimate average treatment effect for drawing causal inference with time-varying treatment assignment and confounding with extension to handle informative right-censoring. The Bayesian marginal structural models is a semi-parametric approach and features a two-step estimation process. The first step involves Bayesian parametric estimation of the time-varying treatment assignment models and the second step involves non-parametric Bayesian bootstrap to estimate the average treatment effect between two distinct treatment sequences of interest.
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.
Install using devtools package:
## install.packages(devtools) ## make sure to have devtools installed
devtools::install_github("Kuan-Liu-Lab/bayesmsm")
library(bayesmsm)This package depends on the following R packages:
MCMCpackdoParallelforeachparallelR2jagscodaHere are some examples demonstrating how to use the
bayesmsm package:
# Simulating longitudinal causal data without right-censoring
# 1) Define coefficient lists for 2 visits
amodel <- list(
# Visit 1: logit P(A1=1) = -0.3 + 0.4*L1_1 - 0.2*L2_1
c("(Intercept)" = -0.3, "L1_1" = 0.4, "L2_1" = -0.2),
# Visit 2: logit P(A2=1) = -0.1 + 0.3*L1_2 - 0.1*L2_2 + 0.5*A_prev
c("(Intercept)" = -0.1, "L1_2" = 0.3, "L2_2" = -0.1, "A_prev" = 0.5)
)
# 2) Define binary outcome model: logistic on treatments and last covariates
ymodel <- c(
"(Intercept)" = -0.8,
"A1" = 0.2,
"A2" = 0.4,
"L1_2" = 0.3,
"L2_2" = -0.3
)
# 3) Load package and simulate data without censoring
testdata <- simData(
n = 100,
n_visits = 2,
covariate_counts = c(2, 2),
amodel = amodel,
ymodel = ymodel,
y_type = "binary",
right_censor = FALSE,
seed = 123
)
# Calculate Bayesian weights
weights <- bayesweight(
trtmodel.list = list(
A1 ~ L1_1 + L2_1,
A2 ~ L1_2 + L2_2 + A1),
data = simdat,
n.chains = 1,
n.iter = 200,
n.burnin = 100,
n.thin = 1,
seed = 890123,
parallel = FALSE)
# Perform Bayesian non-parametric bootstrap
model <- bayesmsm(ymodel = Y ~ A1 + A2,
nvisit = 2,
reference = c(rep(0,2)),
comparator = c(rep(1,2)),
family = "binomial",
data = simdat,
wmean = weights$weights,
nboot = 50,
optim_method = "BFGS",
parallel = TRUE,
seed = 890123,
ncore = 2)
# View model summary
summary_bayesmsm(model)This package is licensed under the MIT License. See the LICENSE file for details.
Please cite our software using:
@Manual{,
title = {bayesmsm: An R package for longitudinal causal analysis using Bayesian Marginal Structural Models},
author = {Xiao Yan and Martin Urner and Kuan Liu},
year = {2025},
note = {https://github.com/Kuan-Liu-Lab/bayesmsm},
url = {https://kuan-liu-lab.github.io/bayesmsm/},
}These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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