A commercial insurance company in the Czech Republic provided data on critical illness insurance. The survival analysis was used to study the influence of the gender of an insured person, the age at which the person entered into an insurance contract, and the region where the insured person lived on the occurrence of an insured event. The main goal of the research was to investigate whether the influence of explanatory variables is estimated differently when two different approaches of analysis are used. The two approaches used were (1) the Cox proportional hazard model that does not assign a specific cause, such as a certain diagnosis, to a critical illness insured event and (2) the competing risks models. Regression models related to these approaches were estimated by R software. The results, which are discussed and compared in the paper, show that insurance companies might benefit from offering policies that consider specific diagnoses as the cause of insured events. They also show that in addition to age, the gender of the client plays a key role in the occurrence of such insured events.

中文翻译：

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Cox比例风险模型和竞争风险模型在大病保险数据中的应用

捷克共和国的一家商业保险公司提供了重大疾病保险的数据。生存分析用于研究被保险人的性别、被保险人签订保险合同的年龄、被保险人居住地区对保险事故发生的影响。该研究的主要目标是调查当使用两种不同的分析方法时，解释变量的影响是否有不同的估计。使用的两种方法是 (1) Cox 比例风险模型，它没有将特定原因（例如某个诊断）分配给重大疾病保险事件，以及 (2) 竞争风险模型。与这些方法相关的回归模型由 R 软件估计。论文中讨论和比较的结果，表明保险公司可能会受益于提供将特定诊断视为保险事件原因的保单。他们还表明，除了年龄之外，客户的性别在此类保险事件的发生中也起着关键作用。