Rolling Extrapolation of Censored Survival Data and Its Applications to Lifetime Outcome Estimation

Estimation of lifetime outcomes is a fundamental problem in biostatistics, epidemiology, and health economic evaluation. In many cohort studies, however, follow-up durations are limited and survival data are heavily censored, making direct estimation of lifetime survival, life expectancy, and cumulative disease burden impossible. Conventional approaches typically rely on parametric survival models, but long-term extrapolations are often highly sensitive to model misspecification and may produce substantial bias.

In this talk, I will present a novel statistical framework, termed the Rolling Extrapolation Algorithm (REA), for extrapolating censored survival data beyond the observed follow-up period. The method incorporates external population information through a matched reference cohort and models relative survival between the study and reference populations. A key observation is that the logit transformation of relative survival often exhibits approximate linearity under broad classes of excess hazard models. Rather than performing a single long-term extrapolation, REA fits a restricted cubic spline model and iteratively predicts one step ahead, updating the fitted model in a rolling fashion until a lifetime horizon is reached.

Simulation studies demonstrate that REA substantially improves extrapolation accuracy compared with conventional one-shot spline and parametric approaches under a variety of hazard patterns. The resulting lifetime survival estimates can be combined with longitudinal quality-of-life, disability, healthcare expenditure, and productivity data to estimate life expectancy, years of life lost, disability-adjusted life years, and lifetime economic burden. Applications will be illustrated using nationwide cohort studies in Taiwan, including analyses of long-term PM2.5 exposure and healthy lifestyle factors.

The talk will focus on the statistical principles underlying REA, its theoretical motivation, empirical performance, practical implementation, and remaining methodological challenges in survival extrapolation and lifetime outcome estimation.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.