BayesRegDTR-package {BayesRegDTR}R Documentation

BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes

Description

Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) doi:10.1093/jrsssb/qkad016 Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.

Author(s)

Maintainer: Jeremy Lim jeremylim23@gmail.com

Authors:

References

Yu, W., & Bondell, H. D. (2023), “Bayesian Likelihood-Based Regression for Estimation of Optimal Dynamic Treatment Regimes”, Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(3), 551-574. doi:10.1093/jrsssb/qkad016

See Also

generate_dataset() for generating a toy dataset to test the model fitting on

BayesLinRegDTR.model.fit() for obtaining an estimated posterior distribution of the optimal treatment option at a user-specified prediction stage

Useful links:


[Package BayesRegDTR version 1.0.1 Index]