From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing
Abstract
One of the main advantages of the Bayesian approach to statistical inference is the flexibility in incorporating information from various sources, from expert opinion to historical data. Whereas the literature on Bayesian dynamic borrowing is rich, practical guidance for how to implement such methods is comparatively sparse. We thoroughly review the most common dynamic borrowing approaches for external data, including how to incorporate multiple historical data sets. We then show how these techniques can be used in practice, using publicly available software, to perform important statistical tasks such as model selection, average treatment effect estimation and prior sensitivity analysis. The aim is to make analysts more confident in their use of Bayesian methods to incorporate external data. All code is made publicly available in a companion GitHub repository (https://github.com/EzequielEBS/hdbayes-tutorial).
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BibTeX
@misc{chen2026from,
title = {{From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing}},
author = {Xinxin Chen and Ezequiel Braga and Joseph G. Ibrahim and Luiz Max Carvalho and Ethan M. Alt},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13976},
eprint = {2609.13976},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13976},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From priors to performance: enhancing statistical efficiency with Bayesian dynamic borrowing
AU - Xinxin Chen
AU - Ezequiel Braga
AU - Joseph G. Ibrahim
AU - Luiz Max Carvalho
AU - Ethan M. Alt
PY - 2026
DA - 2026/09/12
PB - arXiv
DO - 10.48550/arxiv.2609.13976
UR - https://arxiv.org/abs/2609.13976
AB - One of the main advantages of the Bayesian approach to statistical inference is the flexibility in incorporating information from various sources, from expert opinion to historical data. Whereas the literature on Bayesian dynamic borrowing is rich, practical guidance for how to implement such methods is comparatively sparse. We thoroughly review the most common dynamic borrowing approaches for external data, including how to incorporate multiple historical data sets. We then show how these techniques can be used in practice, using publicly available software, to perform important statistical tasks such as model selection, average treatment effect estimation and prior sensitivity analysis. The aim is to make analysts more confident in their use of Bayesian methods to incorporate external data. All code is made publicly available in a companion GitHub repository (https://github.com/EzequielEBS/hdbayes-tutorial).
ER -