Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2017

Joint models for longitudinal counts and left-truncated time-to event data with applications to health insurance

  • Identification data

    Identifier: RP:2468
    Handle: https://hdl.handle.net/20.500.11797/RP2468
    Authors:
    Rizopoulos, DimitrisGuillén, MontserratAlemany, RamonPiulachs, Xavier
    Abstract:
    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.

  • Others:

    URV's Author/s: Rizopoulos, Dimitris Guillén, Montserrat Alemany, Ramon Piulachs, Xavier
    Keywords: Joint models, panel count data, left truncation, Bayesian framework, health insurance
    Abstract: 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.
    Journal publication year: 2017
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Joint models, panel count data, left truncation, Bayesian framework, health insurance

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