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Accelerated intensity frailty model for recurrent events data
#MMPMID24588756
Liu B
; Lu W
; Zhang J
Biometrics
2014[Sep]; 70
(3
): 579-87
PMID24588756
show ga
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.
|*Models, Statistical
[MESH]
|Antineoplastic Agents/therapeutic use
[MESH]
|Computer Simulation
[MESH]
|Data Interpretation, Statistical
[MESH]
|Humans
[MESH]
|Incidence
[MESH]
|Neoplasm Recurrence, Local/*epidemiology/*prevention & control
[MESH]
free
free
free
