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

Tópicos

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Resumo

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.

Resumo Limpo

bivari surviv analysi wide applic presenc covari literatur focus studi effect margin distribut howev covari can also affect associ two variabl articl consid latter issu propos nonstandard local linear estim concord probabl function covari clayton copula condit concord probabl simpl onetoon correspond copula paramet differ data structur includ subject independ depend censor depend truncat propos method can use studi covari affect clayton associ paramet without specifi margin regress model asymptot properti propos estim deriv finitesampl perform examin via simul final illustr appli propos method analyz bone marrow transplant data set

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