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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.16835 |
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| _version_ | 1866908375059529728 |
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| 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 | Background: Assessment of long-term survival for health technology assessment often necessitates extrapolation beyond the duration of a clinical trial. Without robust methods and external data, extrapolations are unreliable. Flexible Bayesian survival models that incorporate longer-term data sources, including registry data and population mortality, have been proposed as an alternative to using standard parametric models with trial data alone.
Methods: The accuracy and uncertainty of extrapolations from the survextrap Bayesian survival model and R package were evaluated. In case studies and simulations, we assessed the accuracy of estimates with and without long-term data, under different assumptions about the long-term hazard rate and how it differs between datasets, and about treatment effects.
Results: The survextrap model gives accurate extrapolations of long-term survival when long-term data on the patients of interest are included. Even using moderately biased external data gives improvements over using the short-term trial data alone. Furthermore, the model gives accurate extrapolations of differences in survival between treatment groups, provided that a reasonably accurate assumption is made about how the treatment effect will change over time. If no long-term data are available, then the model can quantify structural uncertainty about potential future changes in hazard rates.
Conclusions: This analysis shows that Bayesian modelling can give accurate and reliable survival extrapolations by making the most of all available trial and real-world data. This work improves confidence in the use of a powerful tool for evidence-based healthcare decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A simulation and case study to evaluate the extrapolation performance of flexible Bayesian survival models when incorporating real-world data Timmins, Iain R. Torabi, Fatemeh Jackson, Christopher H. Lambert, Paul C. Sweeting, Michael J. Methodology Background: Assessment of long-term survival for health technology assessment often necessitates extrapolation beyond the duration of a clinical trial. Without robust methods and external data, extrapolations are unreliable. Flexible Bayesian survival models that incorporate longer-term data sources, including registry data and population mortality, have been proposed as an alternative to using standard parametric models with trial data alone. Methods: The accuracy and uncertainty of extrapolations from the survextrap Bayesian survival model and R package were evaluated. In case studies and simulations, we assessed the accuracy of estimates with and without long-term data, under different assumptions about the long-term hazard rate and how it differs between datasets, and about treatment effects. Results: The survextrap model gives accurate extrapolations of long-term survival when long-term data on the patients of interest are included. Even using moderately biased external data gives improvements over using the short-term trial data alone. Furthermore, the model gives accurate extrapolations of differences in survival between treatment groups, provided that a reasonably accurate assumption is made about how the treatment effect will change over time. If no long-term data are available, then the model can quantify structural uncertainty about potential future changes in hazard rates. Conclusions: This analysis shows that Bayesian modelling can give accurate and reliable survival extrapolations by making the most of all available trial and real-world data. This work improves confidence in the use of a powerful tool for evidence-based healthcare decision-making. |
| title | A simulation and case study to evaluate the extrapolation performance of flexible Bayesian survival models when incorporating real-world data |
| topic | Methodology |
| url | https://arxiv.org/abs/2505.16835 |