Simulation-based assessment of a Bayesian survival model with flexible baseline hazard and time-dependent effects

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.