首页    期刊浏览 2024年09月20日 星期五
登录注册

文章基本信息

  • 标题:Joint models for longitudinal counts and left-truncated time-to event data with applications to health insurance
  • 本地全文:下载
  • 作者:Xavier Piulachs ; Ramon Alemany ; Montserrat Guillén
  • 期刊名称:SORT-Statistics and Operations Research Transactions
  • 印刷版ISSN:2013-8830
  • 出版年度:2017
  • 卷号:1
  • 期号:2
  • 页码:347-372
  • 语种:English
  • 出版社:SORT- Statistics and Operations Research Transactions
  • 摘要: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.
  • 其他摘要: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.
国家哲学社会科学文献中心版权所有