|Title||Accelerated intensity frailty model for recurrent events data.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Liu, Bo, Wenbin Lu, and Jiajia Zhang|
|Date Published||2014 Sep|
|Keywords||Antineoplastic Agents, Computer Simulation, Data Interpretation, Statistical, Humans, Incidence, Models, Statistical, Neoplasm Recurrence, Local, Outcome Assessment, Health Care, Prognosis, Reproducibility of Results, Risk Factors, Sensitivity and Specificity, Urinary Bladder Neoplasms|
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.
|Original Publication||Accelerated intensity frailty model for recurrent events data.|
|PubMed Central ID||PMC4153804|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA140632 / CA / NCI NIH HHS / United States
Accelerated intensity frailty model for recurrent events data.