Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data

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• 作者：Aidong Adam Ding (1)
  Jin-Jian Hsieh (2)
  Weijing Wang (3)
  
  1. Department of Mathematics ; Northeastern University ; Boston ; MA聽 ; 02115 ; USA
  2. Department of Mathematics ; National Chung Cheng University ; Chia-Yi ; Taiwan ; ROC
  3. Institute of Statistics ; National Chiao-Tung University ; Hsin-Chu ; Taiwan ; ROC
  
• 关键词：Multivariate local polynomial regression ; Clayton copula ; Non ; informative missing data ; Dependent censoring ; Dependent truncation
• 刊名：Lifetime Data Analysis
• 出版年：2015
• 出版时间：January 2015
• 年：2015
• 卷：21
• 期：1
• 页码：42-74
• 全文大小：433 KB
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• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics
  Statistics for Life Sciences, Medicine and Health Sciences
  Quality Control, Reliability, Safety and Risk
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1572-9249

文摘

Bivariate survival analysis has wide applications. In the presence of covariates, most literature focuses on studying their effects on the marginal distributions. However covariates can also affect the association between the two variables. In this article we consider the latter issue by proposing a nonstandard local linear estimator for the concordance probability as a function of covariates. Under the Clayton copula, the conditional concordance probability has a simple one-to-one correspondence with the copula parameter for different data structures including those subject to independent or dependent censoring and dependent truncation. The proposed method can be used to study how covariates affect the Clayton association parameter without specifying marginal regression models. Asymptotic properties of the proposed estimators are derived and their finite-sample performances are examined via simulations. Finally, for illustration, we apply the proposed method to analyze a bone marrow transplant data set.