2023/02/01 by Philippe Lambert, Philippe, Lambert, Kreyenfeld Michaela +1
Decision Sciences · Health Professions · Social Sciences · #FOS: Computer and information sciences #Global Health Care Issues #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2302.00331
openalex publication_date 2023/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Extended cure survival models enable to separate covariates that affect the probability of an event (or `long-term' survival) from those only affecting the event timing (or `short-term' survival). We propose to generalize the bounded cumulative hazard model to handle additive terms for time-varying (exogenous) covariates jointly impacting long- and short-term survival. The selection of the penalty parameters is a challenge in that framework. A fast algorithm based on Laplace approximations in Bayesian P-spline models is proposed. The methodology is motivated by fertility studies where women's characteristics such as the employment status and the income (to cite a few) can vary in a non-trivial and frequent way during the individual follow-up. The method is furthermore illustrated by drawing on register data from the German Pension Fund which enabled us to study how women's time-varying earnings relate to first birth transitions.