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Research Article

Analysis of recurrent gap time data using the weighted risk-set method and the modified within-cluster resampling method

  1. Xianghua Luo1,*,
  2. Chiung-Yu Huang2

Article first published online: 20 OCT 2010

DOI: 10.1002/sim.4074

Issue

Statistics in Medicine

Statistics in Medicine

Volume 30, Issue 4, pages 301–311, 20 February 2011

Additional Information

How to Cite

Luo, X. and Huang, C.-Y. (2011), Analysis of recurrent gap time data using the weighted risk-set method and the modified within-cluster resampling method. Statist. Med., 30: 301–311. doi: 10.1002/sim.4074

Author Information

  1. 1

    Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, U.S.A.

  2. 2

    Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, U.S.A.

*Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware Street SE, MMC 303, Minneapolis, MN 55455, U.S.A.

Publication History

  1. Issue published online: 10 JAN 2011
  2. Article first published online: 20 OCT 2010
  3. Manuscript Accepted: 4 AUG 2010
  4. Manuscript Received: 17 NOV 2009

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Keywords:

  • clustered survival data;
  • Cox proportional hazards model;
  • Kaplan–Meier estimator;
  • log-rank test;
  • multiple outputation;
  • multivariate survival times

Abstract

The gap times between recurrent events are often of primary interest in medical and epidemiology studies. The observed gap times cannot be naively treated as clustered survival data in analysis because of the sequential structure of recurrent events. This paper introduces two important building blocks, the averaged counting process and the averaged at-risk process, for the development of the weighted risk-set (WRS) estimation methods. We demonstrate that with the use of these two empirical processes, existing risk-set based methods for univariate survival time data can be easily extended to analyze recurrent gap times. Additionally, we propose a modified within-cluster resampling (MWCR) method that can be easily implemented in standard software. We show that the MWCR estimators are asymptotically equivalent to the WRS estimators. An analysis of hospitalization data from the Danish Psychiatric Central Register is presented to illustrate the proposed methods. Copyright © 2010 John Wiley & Sons, Ltd.

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