Department of Statistics, ITAM, Mexico City, Mexico.
Lifetime Data Anal. 2022 Apr;28(2):319-334. doi: 10.1007/s10985-022-09551-x. Epub 2022 Mar 17.
In the study of life tables the random variable of interest is usually assumed discrete since mortality rates are studied for integer ages. In dynamic life tables a time domain is included to account for the evolution effect of the hazard rates in time. In this article we follow a survival analysis approach and use a nonparametric description of the hazard rates. We construct a discrete time stochastic processes that reflects dependence across age as well as in time. This process is used as a bayesian nonparametric prior distribution for the hazard rates for the study of evolutionary life tables. Prior properties of the process are studied and posterior distributions are derived. We present a simulation study, with the inclusion of right censored observations, as well as a real data analysis to show the performance of our model.
在生命表研究中，由于研究的死亡率是整数年龄，因此感兴趣的随机变量通常被假设为离散的。在动态生命表中，包括一个时间域，以说明随时间变化的风险率的演变效应。在本文中，我们采用生存分析方法，并使用风险率的非参数描述。我们构建了一个离散时间随机过程，该过程反映了年龄之间以及随时间的依赖性。该过程被用作研究进化生命表的风险率的贝叶斯非参数先验分布。研究了该过程的先验性质，并推导了后验分布。我们进行了模拟研究，包括右删失观测值，并进行了实际数据分析，以展示我们模型的性能。
