BayesFBHborrow: An R Package for Bayesian borrowing for time-to-event data from a flexible baseline hazard

Statistics > Methodology [Submitted on 8 Aug 2024 (v1), last revised 16 Jun 2026 (this version, v2)] Title:BayesFBHborrow: An R Package for Bayesian borrowing for time-to-event data from a flexible baseline hazard View PDFAbstract:Statistical methods that leverage external trial information to help accelerate drug development are becoming increasingly popular. Bayesian methods facilitate dynamic borrowing, where the similarity of the response guides how much information is used. We have proposed a semiparametric Bayesian borrowing model for time-to-event data, with smoothing priors that allows the baseline hazard to take any form via an ensemble average \citep{Scott2024}. By accurately modelling the baseline hazard, rather than approximating its form via fixed piecewise intervals, power is improved and bias of the estimated treatment effect reduced when the borrowing assumption of parameter exchangeability holds. A ``lump-and-smear'' borrowing prior makes the model robust to non-exchangeable historical data by increasing the sensitivity of borrowing to the presence of prior-data conflict, reducing the potential for type I error inflation. We present BayesFBHborrow, an R package that implements our semiparametric Bayesian borrowing model with a historical control. We demonstrate how to select the optimal borrowing hyperparameters. The model supports covariate-adjusted borrowing, which can reduce prior-data conflict and improve power when differences in outcomes are attributable to changes in the covariate distribution. As the treatment effect estimator is non-collapsible, the marginal hazard ratio can be estimated via Bayesian G-computation, while still permitting an adjusted analysis to account for control group drift. We illustrate the Bayesian flexible baseline hazard model on a simulated and real dataset with a marginal estimand, for both an unadjusted and adjusted analyses. Submission history From: Darren Scott [view email][v1] Thu, 8 Aug 2024 09:21:26 UTC (2,507 KB) [v2] Tue, 16 Jun 2026 09:48:56 UTC (720 KB) Current browse context: stat.ME References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.