Nonparametric modeling of the gap time in recurrent event data |
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Authors: | Pang Du |
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Institution: | (1) Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA |
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Abstract: | Recurrent event data arise in many biomedical and engineering studies when failure events can occur repeatedly over time for
each study subject. In this article, we are interested in nonparametric estimation of the hazard function for gap time. A
penalized likelihood model is proposed to estimate the hazard as a function of both gap time and covariate. Method for smoothing
parameter selection is developed from subject-wise cross-validation. Confidence intervals for the hazard function are derived
using the Bayes model of the penalized likelihood. An eigenvalue analysis establishes the asymptotic convergence rates of
the relevant estimates. Empirical studies are performed to evaluate various aspects of the method. The proposed technique
is demonstrated through an application to the well-known bladder tumor cancer data. |
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Keywords: | Penalized likelihood Recurrent event Gap time hazard function Asymptotic convergence rate Bayesian confidence interval |
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