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- Title
Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models.
- Authors
Arrospide, Arantzazu; Ibarrondo, Oliver; Blasco-Aguado, Rubén; Larrañaga, Igor; Alarid-Escudero, Fernando; Mar, Javier
- Abstract
Purpose: To describe a procedure for incorporating parametric functions into individual-level simulation models to sample time to event when age-specific rates are available but not the individual data. Methods: Using age-specific event rates, regression analysis was used to parametrize parametric survival distributions (Weibull, Gompertz, log-normal, and log-logistic), select the best fit using the R 2 statistic, and apply the corresponding formula to assign random times to events in simulation models. We used stroke rates in the Spanish population to illustrate our procedure. Results: The 3 selected survival functions (Gompertz, Weibull, and log-normal) had a good fit to the data up to 85 y of age. We selected Gompertz distribution as the best-fitting distribution due to its goodness of fit. Conclusions: Our work provides a simple procedure for incorporating parametric risk functions into simulation models without individual-level data. Highlights: We describe the procedure for sampling times to event for individual-level simulation models as a function of age from parametric survival functions when age-specific rates are available but not the individual data We used linear regression to estimate age-specific hazard functions, obtaining estimates of parameter uncertainty. Our approach allows incorporating parameter (second-order) uncertainty in individual-level simulation models needed for probabilistic sensitivity analysis in the absence of individual-level survival data.
- Subjects
STATISTICAL models; PARAMETERS (Statistics); SPANIARDS; MATHEMATICAL statistics; SIMULATION methods in education; SURVIVAL analysis (Biometry); STROKE; REGRESSION analysis
- Publication
Medical Decision Making, 2024, Vol 44, Issue 4, p359
- ISSN
0272-989X
- Publication type
Article
- DOI
10.1177/0272989X241232967