In this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

中文翻译：

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协变量对加速失效时间模型中交替重复事件的影响

在本文中，我们对交替发生的复发事件进行建模，并研究协变量对每个生存时间的影响。这是通过加速故障时间模型完成的，我们使用滞后事件时间来捕获对周期和两个事件的依赖关系。然而，由于两个回归模型的误差可能是相关的，我们假设一个二元误差分布。由于大多数事件时间分布不容易扩展到二元形式，我们求助于 copula 函数来从边缘建立二元分布。然后使用最大似然法和所研究的估计量的特性来估计模型参数。呼吸系统疾病的数据用于说明该技术。还进行了模拟研究以检查一致性。