Escarela Gabriel, Carrière Jacques F
Departamento de Matemáticas, Universidad Autónoma Metropolitana, Unidad Iztapalapa, México DF, Mexico.
Stat Methods Med Res. 2003 Aug;12(4):333-49. doi: 10.1191/0962280203sm335ra.
We propose a fully parametric model for the analysis of competing risks data where the types of failure may not be independent. We show how the dependence between the cause-specific survival times can be modelled with a copula function. Features include: identifiability of the problem; accessible understanding of the dependence structures; and flexibility in choosing marginal survival functions. The model is constructed in such a way that it allows us to adjust for concomitant variables and for a dependence parameter to assess the effects of these on each marginal survival model and on the relationship between the causes of death. The methods are applied to a prostate cancer data set. We find that, with the copula model, more accurate inferences are obtained than with the use of a simpler model such as the independent competing risks approach.
我们提出了一种用于分析竞争风险数据的完全参数模型，其中失效类型可能并非相互独立。我们展示了如何使用一个Copula函数对特定病因生存时间之间的依赖性进行建模。其特点包括：问题的可识别性；对依赖结构的易于理解；以及在选择边际生存函数方面的灵活性。该模型的构建方式使我们能够对伴随变量和一个依赖参数进行调整，以评估它们对每个边际生存模型以及死亡原因之间关系的影响。这些方法应用于一个前列腺癌数据集。我们发现，与使用诸如独立竞争风险方法等更简单的模型相比，使用Copula模型能获得更准确的推断。
