BayesRegDTR (version 1.1.2)

BayesRegDTR-package: 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) tools:::Rd_expr_doi("10.1093/jrsssb/qkad016") Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing.

Author

Maintainer: Jeremy Lim

Authors:

• Weichang Yu (ORCID)

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. tools:::Rd_expr_doi("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:

• https://github.com/jlimrasc/BayesRegDTR

• Report bugs at https://github.com/jlimrasc/BayesRegDTR/issues