Title: Simulation-based assessment of a Bayesian survival model with flexible baseline hazard and time-dependent effects Show Chinese title

Title: 基于仿真的具有灵活基线危险函数和时变效应的贝叶斯生存模型评估

Abstract: There is increasing interest in flexible parametric models for the analysis of time-to-event data, yet Bayesian approaches that offer incorporation of prior knowledge remain underused. A flexible Bayesian parametric model has recently been proposed that uses M-splines to model the hazard function. We conducted a simulation study to assess the statistical performance of this model, which is implemented in the survextrap R package. Our simulation uses data generating mechanisms of realistic survival data based on two oncology clinical trials. Statistical performance is compared across a range of flexible models, varying the M-spline specification, smoothing procedure, priors, and other computational settings. We demonstrate good performance across realistic scenarios, including good fit of complex baseline hazard functions and time-dependent covariate effects. This work helps inform key considerations to guide model selection, as well as identifying appropriate default model settings in the software that should perform well in a broad range of applications. Show Chinese abstract

Abstract: 对时间至事件数据的分析中，灵活的参数模型越来越受到关注，但能够结合先验知识的贝叶斯方法仍使用不足。最近提出了一种灵活的贝叶斯参数模型，该模型使用M样条来建模风险函数。我们进行了一项模拟研究，以评估该模型的统计性能，该模型在survextrap R包中实现。我们的模拟基于两个肿瘤学临床试验的真实生存数据生成机制。在一系列灵活模型中比较了统计性能，包括改变M样条规范、平滑过程、先验和其他计算设置。我们在现实场景中展示了良好的性能，包括复杂基线风险函数和时变协变量效应的良好拟合。这项工作有助于确定关键考虑因素，以指导模型选择，并在软件中识别适当的默认模型设置，这些设置应在广泛的应用中表现良好。