Nicolaie Mioara Alina, Taylor Jeremy M G, Legrand Catherine
Institute of Statistics, Biostatistics and Actuarial Sciences, Catholic University of Louvain, Voie du Roman Pays 20, bte L1.04.01, 1348, Louvain-la-Neuve, Belgium.
School of Public Health, University of Michigan, M4509 SPH II, 1415 Washington Heights, Ann Arbor, MI, 48109-2029, USA.
Lifetime Data Anal. 2019 Jan;25(1):1-25. doi: 10.1007/s10985-018-9417-8. Epub 2018 Jan 31.
In this paper, we extend the vertical modeling approach for the analysis of survival data with competing risks to incorporate a cure fraction in the population, that is, a proportion of the population for which none of the competing events can occur. The proposed method has three components: the proportion of cure, the risk of failure, irrespective of the cause, and the relative risk of a certain cause of failure, given a failure occurred. Covariates may affect each of these components. An appealing aspect of the method is that it is a natural extension to competing risks of the semi-parametric mixture cure model in ordinary survival analysis; thus, causes of failure are assigned only if a failure occurs. This contrasts with the existing mixture cure model for competing risks of Larson and Dinse, which conditions at the onset on the future status presumably attained. Regression parameter estimates are obtained using an EM-algorithm. The performance of the estimators is evaluated in a simulation study. The method is illustrated using a melanoma cancer data set.
在本文中，我们扩展了用于分析具有竞争风险的生存数据的纵向建模方法，以纳入人群中的治愈比例，即竞争事件均不会发生的人群比例。所提出的方法有三个组成部分：治愈比例、无论病因如何的失败风险，以及给定发生失败的情况下某一特定失败原因的相对风险。协变量可能会影响这些组成部分中的每一个。该方法的一个吸引人之处在于，它是普通生存分析中半参数混合治愈模型对竞争风险的自然扩展；因此，只有在发生失败时才指定失败原因。这与现有的Larson和Dinse的竞争风险混合治愈模型形成对比，后者在开始时就假定未来状态的条件。使用期望最大化（EM）算法获得回归参数估计值。在模拟研究中评估估计量的性能。使用黑色素瘤癌症数据集对该方法进行了说明。
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