Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study.

ABSTRACT: Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike's information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods' suitability for selecting a parametric mode

SUBMITTER: Gallacher D 

PROVIDER: S-EPMC7780268 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature