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

Arantzazu Arrospide1,2,3, Oliver Ibarrondo2,3,4, Rubén Blasco-Aguado5

  • 1Ministry of Health of the Basque Government, Vitoria-Gasteiz, Spain.

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Summary

This study presents a method to simulate event times using age-specific rates for individual-level models. The Gompertz distribution best fit the data, enabling accurate event time sampling without individual records.

Keywords:
health economicsnatural historysimulationsurvival analysesuncertainty

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Economics

Background:

  • Individual-level simulation models are crucial for health economic evaluations.
  • These models often require individual patient data, which may not always be available.
  • Age-specific event rates are frequently accessible, but direct individual data is lacking.

Purpose of the Study:

  • To describe a procedure for incorporating parametric functions into individual-level simulation models.
  • To sample time-to-event data when only age-specific rates are available.
  • To facilitate simulation modeling in the absence of individual-level data.

Main Methods:

  • Parametric survival distributions (Weibull, Gompertz, log-normal, log-logistic) were parametrized using age-specific event rates via regression analysis.
  • The best-fitting distribution was selected using the R-squared statistic.
  • The chosen parametric function was applied to assign random times to events in simulation models, using Spanish stroke rates as an example.

Main Results:

  • The Gompertz, Weibull, and log-normal distributions demonstrated a good fit to the data up to 85 years of age.
  • The Gompertz distribution was identified as the best-fitting distribution based on its goodness of fit.
  • The procedure successfully incorporated parametric risk functions into simulation models.

Conclusions:

  • A straightforward procedure is provided for integrating parametric risk functions into individual-level simulation models.
  • This method enables the simulation of time-to-event data using readily available age-specific rates.
  • The approach allows for the inclusion of parameter uncertainty in simulations, crucial for probabilistic sensitivity analysis.