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Semiparametric Analysis for Recurrent Event Data with Time‐Dependent Covariates and Informative Censoring
Author(s) -
Huang C.Y.,
Qin J.,
Wang M.C.
Publication year - 2010
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2009.01266.x
Subject(s) - censoring (clinical trials) , covariate , inference , computer science , semiparametric regression , accelerated failure time model , statistics , event data , proportional hazards model , survival analysis , econometrics , semiparametric model , event (particle physics) , regression analysis , mathematics , artificial intelligence , nonparametric statistics , physics , quantum mechanics
Summary Recurrent event data analyses are usually conducted under the assumption that the censoring time is independent of the recurrent event process. In many applications the censoring time can be informative about the underlying recurrent event process, especially in situations where a correlated failure event could potentially terminate the observation of recurrent events. In this article, we consider a semiparametric model of recurrent event data that allows correlations between censoring times and recurrent event process via frailty. This flexible framework incorporates both time‐dependent and time‐independent covariates in the formulation, while leaving the distributions of frailty and censoring times unspecified. We propose a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution. Large sample properties of the regression parameter estimates and the estimated baseline cumulative intensity functions are studied. Numerical studies demonstrate that the proposed methodology performs well for realistic sample sizes. An analysis of hospitalization data for patients in an AIDS cohort study is presented to illustrate the proposed method.