BayesFBHborrow: An R Package for Bayesian borrowing for time-to-event data from a flexible baseline hazard
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BayesFBHborrow：基于灵活基线风险的生存数据贝叶斯信息借用 R 包
- 研究问题
- 分析临床试验生存结局时，如何根据当前与历史对照的相似程度借用信息，同时灵活刻画基线风险并限制数据冲突的影响？
- 方法与思路
- 作者实现半参数贝叶斯模型，以平滑先验和集成平均刻画基线风险，结合特殊借用先验、协变量调整与贝叶斯 G-computation。
- 材料陈述的贡献
- 摘要称，该 R 包支持借用超参数选择及边际风险比估计；参数可交换时可改善功效与偏差，冲突敏感先验可减轻错误借用。
- 阅读关注点
- 应核对历史与当前参数的可交换性、借用先验和超参数选择规则，并区分条件风险比、边际风险比及协变量调整的作用。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出具体先验、超参数准则或改善幅度，效果取决于借用条件。
原文摘要
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. 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.