Dynamic and Flexible Survival Models for Extrapolation of Relative Survival: A Case Study and Simulation Study

Benjamin Kearns, Matt D. Stevenson, Kostas Triantafyllopoulos and Andrea Manca
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Benjamin Kearns: The University of Sheffield, Sheffield, UK
Matt D. Stevenson: The University of Sheffield, Sheffield, UK
Kostas Triantafyllopoulos: The University of Sheffield, Sheffield, UK
Andrea Manca: The University of York, York, UK

Medical Decision Making, 2022, vol. 42, issue 7, 945-955

Abstract: Background Extrapolation of survival data is a key task in health technology assessments (HTAs), which may be improved by incorporating general population mortality data via relative survival models. Dynamic survival models are a promising method for extrapolation that may be expanded to dynamic relative survival models (DRSMs), a novel development presented here. There are currently neither examples of dynamic models in HTA nor comparisons of DRSMs with other relative survival models when used for survival extrapolation. Methods An existing appraisal, for which there had been disagreement over the approach to survival extrapolation, was chosen and the health economic model recreated. The sensitivity of estimates of cost-effectiveness to different model choices (standard survival models, DSMs, and DRSMs) and specifications was examined. The appraisal informed a simulation study to evaluate DRSMs with relative survival models based on both standard and spline-based (flexible) models. Results Dynamic models provided insight into the behavior of the trend in the hazard function and how it may vary during the extrapolated phase. DRSMs led to extrapolations with improved plausibility for which model choice may be based on clinical input. In the simulation study, the flexible and dynamic relative survival models performed similarly and provided highly variable extrapolations. Limitations Further experience with these models is required to identify settings when they are most useful, and they provide sufficiently accurate extrapolations. Conclusions Dynamic models provide a flexible and attractive method for extrapolating survival data and facilitate the use of clinical input for model choice. Flexible and dynamic relative survival models make few structural assumptions and can improve extrapolation plausibility, but further research is required into methods for reducing the variability in extrapolations.

Keywords: economic evaluation; forecasting; parametric survival models; relative survival (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:42:y:2022:i:7:p:945-955

DOI: 10.1177/0272989X221107649

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