Premium
Accelerated intensity frailty model for recurrent events data
Author(s) -
Liu Bo,
Lu Wenbin,
Zhang Jiajia
Publication year - 2014
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/biom.12163
Subject(s) - estimator , statistics , variance (accounting) , smoothing , kernel smoother , computer science , kernel (algebra) , intensity (physics) , regression , mathematics , algorithm , kernel method , artificial intelligence , physics , accounting , combinatorics , quantum mechanics , radial basis function kernel , support vector machine , business
Summary In this article we propose an accelerated intensity frailty (AIF) model for recurrent events data and derive a test for the variance of frailty. In addition, we develop a kernel‐smoothing‐based EM algorithm for estimating regression coefficients and the baseline intensity function. The variance of the resulting estimator for regression parameters is obtained by a numerical differentiation method. Simulation studies are conducted to evaluate the finite sample performance of the proposed estimator under practical settings and demonstrate the efficiency gain over the Gehan rank estimator based on the AFT model for counting process (Lin et al., 1998). Our method is further illustrated with an application to a bladder tumor recurrence data.