A Bayesian Surrogate Evaluation Test

An implementation of the Bayesian Surrogate Evaluation Test (BSET) for assessing the validity of surrogate markers in clinical trials. Provides hypothesis testing tools to evaluate whether a surrogate can reliably estimate the causal effect of a treatment on a primary outcome. Implements the imputation-based Bayesian methodology of Carlotti and Parast (2026) , extending the frequentist rank-based approach of Parast et al. (2024) . Addresses key limitations of the frequentist method, including the lack of causal interpretability and the inability to adjust for covariates in the estimation process.

BSET

BSET (Bayesian Surrogate Evaluation Test) is an R package for assessing the validity of surrogate markers in clinical trials. It provides hypothesis testing tools to evaluate whether a surrogate can reliably estimate the causal effect of a treatment on a primary outcome. The package implements the imputation-based Bayesian methodology of Carlotti and Parast (2026), extending the frequentist rank-based approach of Parast et al. (2024). BSET addresses key limitations of the frequentist method, including the lack of causal interpretability and the inability to adjust for covariates in the estimation process.

The package supports Bayesian testing both with and without baseline covariates. Additionally, it includes comprehensive simulation suites to replicate studies from both papers, enabling performance comparisons between Bayesian and frequentist approaches across diverse clinical scenarios.

For a detailed walkthrough and examples of the imputation-based methodology, please visit the BSET Tutorial.

Citation

If you use this package, please cite:

Carlotti, P. and Parast, L. (2026). A Bayesian Critique of Rank-Based Methods for Surrogate Marker Evaluation. arXiv preprint arXiv:2603.14381.

Replication

All code and instructions needed to replicate the results presented in the paper can be found in the replication/ folder.