Practical Implementation of Advanced Causal Inference Method: Development of an R Package for Bayesian Marginal Structural Models with Time-Varying Treatment

Background: Observational studies offer a viable, efficient, and low-cost design to readily gather evidence on exposure effects. Although more practical, the exposure mechanism is non-randomized and causal inference methods are required to draw causal conclusions. Objectives: Bayesian approaches to causal inference have unique estimation features that are useful in many settings; however, there is a lack of open-access software packages to carry out these analyses. Our project seeks to address this gap by developing a user-friendly R package named “bayesmsm” for the implementation of the Bayesian Marginal Structural Models for longitudinal observational data with extensions to handle right-censoring which is common for longitudinal health data. This R package provides an elegant approach to conduct Bayesian causal inference with time-varying exposure and confounding. Methods: We use simulated datasets to assess the performance of the package and to illustrate its use. This package first implements Bayesian parametric treatment assignment weight estimation through Markov Chain Monte Carlo (MCMC) computation. It then uses Bayesian non-parametric bootstrap to maximize the utility function with respect to the causal effect. The bootstrap calculation process was optimized for performance through parallel computing. The package has additional features, including handling right-censored data and generating analysis summaries and visualizations. Results: Using simulated datasets with and without right-censoring, the subject-specific treatment assignment weights were estimated using the package's weight estimation functions. The Bayesian posterior bootstrap results were further visualized by functions built within this package. Conclusions: The “bayesmsm” package provides a reliable tool to implement complex Bayesian Marginal Structural Model analysis. Future work will focus on extending this package to handle time-to-event data.