bayesmsm

An R package for Bayesian Marginal Structural Models

https://github.com/kuan-liu-lab/bayesmsm

Keywords

Repository

An R package for Bayesian Marginal Structural Models

README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: inline
---



```{r setup, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
```

## bayesmsm




# Overview

*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.


# Installation

Install using `devtools` package:

```{r echo=TRUE, eval=FALSE}
## 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`


# Quick Start

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

```{r echo=TRUE, eval=FALSE}
# Load example data without right-censoring
testdata <- read.csv(system.file("extdata",
                                 "sim_causal.csv",
                                 package = "bayesmsm"))

# Calculate Bayesian weights
weights <- bayesweight(
  trtmodel.list = list(
    A1 ~ L11 + L21,
    A2 ~ L11 + L21 + L12 + L22 + A1,
    A3 ~ L11 + L21 + L12 + L22 + A1 + L13 + L23 + A2
  ),
  data = testdata,
  n.chains = 2,
  n.iter = 250,
  n.burnin = 150,
  n.thin = 5,
  seed = 890123,
  parallel = TRUE
)

# Perform Bayesian non-parametric bootstrap
model <- bayesmsm(
  ymodel = Y ~ A1 + A2 + A3,
  nvisit = 3,
  reference = c(rep(0,3)),
  comparator = c(rep(1,3)),
  treatment_effect_type = "sq",
  family = "binomial",
  data = testdata,
  wmean = weights$weights,
  nboot = 1000,
  optim_method = "BFGS",
  seed = 890123,
  parallel = TRUE,
  ncore = 2
)

# View model summary
summary.bayesmsm(model)
```


# License

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


# Citation

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 = {2024},
  note = {https://github.com/Kuan-Liu-Lab/bayesmsm},
  url = {https://kuan-liu-lab.github.io/bayesmsm/},
}
```

# Contact

* e-mail: , 
* Please report bugs by opening an [issue](https://github.com/Kuan-Liu-Lab/bayesmsm/issues/new). 

Owner

GitHub Events

Committers

Last synced: 7 months ago

Issues and Pull Requests

Last synced: 7 months ago

Packages