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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.
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)
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