Cheng Yu, Fine Jason P
Department of Statistics and Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA.
J R Stat Soc Series B Stat Methodol. 2012 Mar 1;74(2):183-202. doi: 10.1111/j.1467-9868.2011.01012.x.
Association models, like frailty and copula models, are frequently used to analyze clustered survival data and evaluate within-cluster associations. The assumption of noninformative censoring is commonly applied to these models, though it may not be true in many situations. In this paper, we consider bivariate competing risk data and focus on association models specified for the bivariate cumulative incidence function (CIF), a nonparametrically identifiable quantity. Copula models are proposed which relate the bivariate CIF to its corresponding univariate CIFs, similarly to independently right censored data, and accommodate frailty models for the bivariate CIF. Two estimating equations are developed to estimate the association parameter, permitting the univariate CIFs to be estimated either parametrically or nonparametrically. Goodness-of-fit tests are presented for formally evaluating the parametric models. Both estimators perform well with moderate sample sizes in simulation studies. The practical use of the methodology is illustrated in an analysis of dementia associations.
关联模型，如脆弱模型和copula模型，经常用于分析聚类生存数据并评估聚类内的关联性。非信息删失的假设通常应用于这些模型，尽管在许多情况下这可能并不成立。在本文中，我们考虑双变量竞争风险数据，并关注为双变量累积发病率函数（CIF）指定的关联模型，CIF是一个非参数可识别的量。我们提出了copula模型，它将双变量CIF与其相应的单变量CIF联系起来，类似于独立右删失数据，并为双变量CIF纳入脆弱模型。我们开发了两个估计方程来估计关联参数，允许单变量CIF以参数或非参数方式进行估计。我们提出了拟合优度检验，用于正式评估参数模型。在模拟研究中，这两种估计方法在中等样本量下都表现良好。通过对痴呆症关联性的分析说明了该方法的实际应用。
