Simulation-based assessment of a Bayesian survival model with flexible baseline hazard and time-dependent effects
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909555213991936 |
|---|---|
| author | Timmins, Iain R. Torabi, Fatemeh Jackson, Christopher H. Lambert, Paul C. Sweeting, Michael J. |
| author_facet | Timmins, Iain R. Torabi, Fatemeh Jackson, Christopher H. Lambert, Paul C. Sweeting, Michael J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21388 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simulation-based assessment of a Bayesian survival model with flexible baseline hazard and time-dependent effects Timmins, Iain R. Torabi, Fatemeh Jackson, Christopher H. Lambert, Paul C. Sweeting, Michael J. Methodology Computation 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. |
| title | Simulation-based assessment of a Bayesian survival model with flexible baseline hazard and time-dependent effects |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2503.21388 |