標題: Local linear estimation of concordance probability with application to covariate effects models on association for bivariate failure-time data
作者: Ding, Aidong Adam
Hsieh, Jin-Jian
Wang, Weijing
統計學研究所
Institute of Statistics
關鍵字: Multivariate local polynomial regression;Clayton copula;Non-informative missing data;Dependent censoring;Dependent truncation
公開日期: 1-一月-2015
摘要: 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.
URI: http://dx.doi.org/10.1007/s10985-013-9286-0
http://hdl.handle.net/11536/124244
ISSN: 1380-7870
DOI: 10.1007/s10985-013-9286-0
期刊: LIFETIME DATA ANALYSIS
Volume: 21
起始頁: 42
結束頁: 74
顯示於類別:期刊論文