Cumulative Incidence Association Models for Bivariate Competing Risks Data.
Cumulative Incidence Association Models for Bivariate Competing Risks Data.
复制标题
DOI：
10.1111/j.1467-9868.2011.01012.x
复制
发表时间：
2012-03-01
期刊：
影响因子：
--
通讯作者：
Fine JP
中科院分区：
文献类型：
--
作者：
Cheng Y;Fine JP
关联模型，如脆弱性和copula模型，经常用于分析聚类生存数据和评估聚类内的关联。无信息删失的假设通常适用于这些模型，尽管在许多情况下可能不正确。在本文中，我们考虑二元竞争风险数据，并专注于关联模型指定的二元累积发病率函数（CIF），一个非参数可识别的数量。Copula模型提出了相关的双变量CIF到其相应的单变量CIF，类似于独立的右删失数据，并容纳脆弱模型的双变量CIF。两个估计方程来估计的关联参数，允许单变量CIF估计参数或非参数。拟合优度检验正式评估的参数模型。在模拟研究中，这两种估计量在中等样本量下表现良好。痴呆症关联分析说明了该方法的实际用途。
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.
登录
查看更多内容
影响因子：
--
作者：
LEHMANN, EL
通讯作者：
LEHMANN, EL
影响因子：
3.7
作者：
GENEST, C;RIVEST, LP
通讯作者：
RIVEST, LP
DOI：
10.2307/3314660
发表时间：
1986-06-01
影响因子：
0.6
作者：
GENEST, C;MACKAY, RJ
通讯作者：
MACKAY, RJ
影响因子：
3.7
作者：
OAKES, D
通讯作者：
OAKES, D
影响因子：
1.9
作者：
Cheng, Yu;Fine, Jason P.;Kosorok, Michael R.
通讯作者：
Kosorok, Michael R.