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Modelling of lung cancer survival data for critical illness insurances

机译:重大疾病保险的肺癌生存数据建模

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Modelling of critical illness survival data, being primary developed in the context of, e.g. health insurance contracts, also plays an important role in the currently analysed problems related to secondary insurance market. The aim of this contribution is two-fold. In the first part we describe how to construct a multiple state model for critical illness insurances, which takes into account that a probability of death for a dread disease sufferer depends on the duration of the disease and the survival probabilities are related to the disease stage. Then, in the second part, we focus on modelling of the probabilistic structure of the analysed model for a particular case of dread disease. Based on the actual data for the Lower Silesian Voivodship in Poland, we estimate the transition probabilities for the derived model in case of the risk of lung cancer. For this purpose we use the methodology developed for the construction of multi-state life tables, such as binomial, Poisson and ordinal logistic regression models. The obtained results can be directly used to build the multiple increment-decrement tables, which are useful to valuation not only critical illness insurances and life insurances with accelerated death benefits option but also to viatical settlement contracts and health-related expenses.
机译:关键疾病生存数据的建模主要是在例如健康保险合同在当前分析的与二级保险市场有关的问题中也起着重要作用。该贡献的目的是双重的。在第一部分中,我们描述了如何构建重大疾病保险的多状态模型,该模型考虑到了可怕疾病患者的死亡可能性取决于疾病的持续时间,而生存概率与疾病阶段有关。然后,在第二部分中,我们将重点放在针对特定可怕疾病案例的分析模型的概率结构建模上。基于波兰下西里西亚省的实际数据,我们估计了发生肺癌风险时衍生模型的转移概率。为此,我们使用开发用于构建多状态生命表的方法,例如二项式,泊松和序数逻辑回归模型。获得的结果可直接用于构建多个增减表,这些表不仅可用于评估具有加速身故赔偿金选择权的重大疾病保险和人寿保险,而且还可用于评估过关协议和与健康相关的费用。

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