Joint models for longitudinal counts and left-truncated time-to-event data with applications to health insurance
Aging societies have given rise to important challenges in the field of health insurance. Elderly policyholders need to be provided with fair premiums based on their individual health status, whereas insurance companies want to plan for the potential costs of tackling lifetimes above mean expectations. In this article, we focus on a large cohort of policyholders in Barcelona (Spain), aged 65 years and over. A shared-parameter joint model is proposed to analyse the relationship between annual demand for emergency claims and time until death outcomes, which are subject to left truncation. We compare different functional forms of the association between both processes, and, furthermore, we illustrate how the fitted model provides time-dynamic predictions of survival probabilities. The parameter estimation is performed under the Bayesian framework using Markov chain Monte Carlo methods.
|Keywords||Bayesian framework, Health insurance, Joint models, Left truncation, Panel count data|
|Persistent URL||dx.doi.org/10.2436/20.8080.02.63, hdl.handle.net/1765/103754|
|Note||MSC: 62J99, 62N01, 62P05|
Piulachs, X. (Xavier), Alemany, R. (Ramon), Guillén, M. (Montserrat), & Rizopoulos, D. (2017). Joint models for longitudinal counts and left-truncated time-to-event data with applications to health insurance. SORT, 41(2), 347–372. doi:10.2436/20.8080.02.63