Parametric analysis and model selection for economic evaluation of survival data

Article type: Research Article

Authors: Nemes, Szilárd

Affiliations: AstraZeneca, BioPharmaceuticals R&D, Late-stage Development, Respiratory & Immunology Biometrics, Gothenburg, Sweden | E-mail: [email protected]

Correspondence: [*] Corresponding author: AstraZeneca, BioPharmaceuticals R&D, Late-stage Development, Respiratory & Immunology Biometrics, Gothenburg, Sweden. E-mail: [email protected].

Abstract: Health technology assessments of interventions impacting survival often require extrapolating current data to gain a better understanding of the interventions’ long-term benefits. Both a comprehensive examination of the trial data up to the maximum follow-up period and the fitting of parametric models are required for extrapolation. It is standard practice to visually compare the parametric curves to the Kaplan-Meier survival estimate (or comparison of hazard estimates) and to assess the parametric models using likelihood-based information criteria. In place of these two steps, this work demonstrates how to minimize the squared distance of parametric estimators to the Kaplan-Meier estimate. This is in line with the selection of the model using Mean Squared Error, with the modification that the unknown true survival is replaced by the Kaplan-Meier estimate. We would assure the internal validity of the extrapolated model and its appropriate representation of the data by adhering to this procedure. We use both simulation and real-world data with a scenario where no model that properly fits the data could be found to illustrate how this process can aid in model selection.

Keywords: Parametric survival, model selection, survival extrapolation, FIC, mean squared error

DOI: 10.3233/MAS-241506

Journal: Model Assisted Statistics and Applications, vol. 19, no. 2, pp. 123-131, 2024