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

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 R² 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.