Accelerated intensity frailty model for recurrent events data

Biometrics. 2014 Sep;70(3):579-87. doi: 10.1111/biom.12163. Epub 2014 Mar 3.


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.

Keywords: Accelerated intensity frailty model; EM algorithm; Kernel smoothing; Nonparametric maximum likelihood estimation; Recurrent events data.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Antineoplastic Agents / therapeutic use
  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Incidence
  • Models, Statistical*
  • Neoplasm Recurrence, Local / epidemiology*
  • Neoplasm Recurrence, Local / prevention & control*
  • Outcome Assessment, Health Care / methods*
  • Prognosis
  • Reproducibility of Results
  • Risk Factors
  • Sensitivity and Specificity
  • Urinary Bladder Neoplasms / drug therapy*
  • Urinary Bladder Neoplasms / epidemiology*


  • Antineoplastic Agents