The motivation of this paper came from a study which was conducted to examine the effect of laser treatment in delaying the onset of blindness in patients with diabetic retinopathy. The data are competing risks data with two dependent competing causes of failures, and there are ties. In this paper we have used the bivariate Weibull-geometric (BWG) distribution to analyse this data set. It is well known that the Bayesian inference has certain advantages over the classical inference in certain cases. In this paper, first we develop the Bayesian inference of the unknown parameters of the BWG model, under a fairly flexible class of priors and analyse one real data set with ties to show the effectiveness of the model. Further, it is observed that the BWG can be used to analyse dependent competing risk data quite effectively when there are ties. The analysis of the above-mentioned competing risks data set indicates that the BWG is preferred compared to the MOBW in this case.

本文的动机来自一项研究，该研究旨在检查激光治疗在延迟糖尿病视网膜病变患者失明方面的作用。该数据是具有两个相互依赖的故障竞争原因的竞争风险数据，并且存在联系。在本文中，我们使用二元威布尔几何 (BWG) 分布来分析该数据集。众所周知，贝叶斯推理在某些情况下比经典推理具有一定的优势。在本文中，我们首先在相当灵活的先验类别下开发了 BWG 模型未知参数的贝叶斯推理，并分析了一个具有联系的真实数据集以展示模型的有效性。此外，据观察，当存在联系时，BWG 可用于非常有效地分析相关竞争风险数据。