A proportional hazards model for the subdistribution of a competing risk
A proportional hazards model for the subdistribution of a competing risk
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DOI：
10.2307/2670170
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发表时间：
1999-06-01
影响因子：
3.7
通讯作者：
Gray, RJ
中科院分区：
文献类型：
--
作者：
Fine, JP;Gray, RJ
对于解释协变量，竞争风险数据的标准分析涉及通过比例风险假设对特定原因的风险函数进行建模。不幸的是，特定原因的危险函数不具有关于特定故障类型的生存概率的直接解释。近年来，许多临床医生开始使用累积关联函数，即特定原因的边际失败概率，它直观地吸引人，更容易被非统计学家解释。累积发病率在成本-效果分析中尤其相关，在成本-效果分析中，需要生存概率来确定治疗效用。在此之前，作者已经考虑了在比例危险公式下合并特定原因危险函数的估计的方法。然而，这些方法不允许分析师直接评估协变量对边际概率函数的影响。在这篇文章中，我们提出了一个新的次分布的半参数比例风险模型。利用偏似然原理和加权技术，我们推导出了各种截尾情形下有限维回归参数的估计和推断过程。对于具有一定协变量的个体，我们给出了预测累积发病率的一致一致估计；可信区间和区间可以解析地获得，也可以通过易于实现的模拟技术获得。为了对比这两种方法，我们分析了两种模型下乳腺癌临床试验的数据集。
With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type. In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician. The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility. Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation. However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function. In this article we propose a novel semiparametric proportional hazards model for the subdistribution. Using the partial likelihood principle and weighting techniques, we derive estimation and inference procedures for the finite-dimensional regression parameter under a variety of censoring scenarios. We give a uniformly consistent estimator for the predicted cumulative incidence for an individual with certain covariates; confidence intervals and bands can be obtained analytically or with an easy-to-implement simulation technique. To contrast the two approaches, we analyze a dataset from a breast cancer clinical trial under both models.