Using Age-Specific Rates for Parametric Survival Function Estimation in Simulation Models

Arantzazu Arrospide, Oliver Ibarrondo, Rubén Blasco-Aguado, Igor Larrañaga, Fernando Alarid-Escudero and Javier Mar
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Arantzazu Arrospide: Ministry of Health of the Basque Government, Vitoria-Gasteiz, Spain
Oliver Ibarrondo: Biodonostia Health Research Institute, Economic Evaluation of Chronic Diseases Research Group, San Sebastián, Spain
Rubén Blasco-Aguado: Basque Center for Applied Mathematics, Bilbao, Spain
Igor Larrañaga: Biodonostia Health Research Institute, Economic Evaluation of Chronic Diseases Research Group, San Sebastián, Spain
Fernando Alarid-Escudero: Department of Health Policy, School of Medicine, and Stanford Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, Stanford, CA, USA
Javier Mar: Biodonostia Health Research Institute, Economic Evaluation of Chronic Diseases Research Group, San Sebastián, Spain

Medical Decision Making, 2024, vol. 44, issue 4, 359-364

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.

Keywords: simulation; survival analyses; uncertainty; natural history; health economics (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:44:y:2024:i:4:p:359-364

DOI: 10.1177/0272989X241232967

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