1. Evaluation of Flexible Parametric Relative Survival Approaches for Enforcing Long-Term Constraints When Extrapolating All-Cause Survival.
- Author
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Lee, Sangyu, Lambert, Paul C., Sweeting, Michael J., Latimer, Nicholas R., and Rutherford, Mark J.
- Subjects
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TECHNOLOGY assessment , *SURVIVAL analysis (Biometry) , *RANDOMIZED controlled trials , *PARAMETER estimation , *DEATH rate , *EXTRAPOLATION , *NATURAL selection - Abstract
Parametric models are used to estimate the lifetime benefit of an intervention beyond the range of trial follow-up. Recent recommendations have suggested more flexible survival approaches and the use of external data when extrapolating. Both of these can be realized by using flexible parametric relative survival modeling. The overall aim of this article is to introduce and contrast various approaches for applying constraints on the long-term disease-related (excess) mortality including cure models and evaluate the consequent implications for extrapolation. We describe flexible parametric relative survival modeling approaches. We then introduce various options for constraining the long-term excess mortality and compare the performance of each method in simulated data. These methods include fitting a standard flexible parametric relative survival model, enforcing statistical cure, and forcing the long-term excess mortality to converge to a constant. We simulate various scenarios, including where statistical cure is reasonable and where the long-term excess mortality persists. The compared approaches showed similar survival fits within the follow-up period. However, when extrapolating the all-cause survival beyond trial follow-up, there is variation depending on the assumption made about the long-term excess mortality. Altering the time point from which the excess mortality is constrained enables further flexibility. The various constraints can lead to applying explicit assumptions when extrapolating, which could lead to more plausible survival extrapolations. The inclusion of general population mortality directly into the model-building process, which is possible for all considered approaches, should be adopted more widely in survival extrapolation in health technology assessment. • Extrapolation of all-cause survival from randomized controlled trial data is often required in the context of economic evaluation of novel interventions. External information can be used to guide long-term survival trends when extrapolating all-cause survival. This article investigates relative survival modeling approaches to incorporate general population mortality rates into long-term extrapolation. In doing so, we expand on existing approaches by clearly detailing how to apply various constraints on the long-term disease-specific mortality in this modeling framework. • Flexible parametric survival models can apply various extrapolation approaches in the framework. We compare models by imposing constraints on parameter estimation and applying those constraints at specific points of follow-up, including beyond the range of the data. We compare these methods in scenarios where cure is simulated to be reasonable and in cases where it is not. The result shows that different assumptions on the excess hazards can result in different extrapolations of all-cause survival. The various constraints can lead to more plausible survival extrapolation depending on the disease or treatment characteristics. • The approaches for all-cause survival extrapolation outlined offer a suite of possibilities to clearly describe an approach to extrapolation that automatically incorporates general population mortality rates while making explicit choices on how long disease-related mortality will affect the cohort. The choice of approach can be dictated by the clinical context in the decision-making process. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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