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.

翻译：针对时间-事件数据的分析，灵活参数模型日益受到关注，然而能够整合先验知识的贝叶斯方法仍未得到充分利用。近期提出的一种灵活贝叶斯参数模型采用M样条对风险函数进行建模。我们通过仿真研究评估了该模型的统计性能，该模型已在survextrap R包中实现。我们的仿真基于两项肿瘤学临床试验，采用真实生存数据生成机制。通过比较一系列灵活模型，改变M样条设定、平滑处理、先验分布及其他计算参数，评估其统计性能。研究证明该模型在真实场景中表现良好，包括对复杂基准风险函数和时变协变量效应的良好拟合。本工作有助于指导模型选择的关键考量，并为软件中应能在广泛应用中表现良好的默认模型设置提供参考依据。