Conditional Regression Analysis for Recurrence Time Data

Shu Hui Chang, Mei Cheng Wang

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Recurrence time data can be regarded as a specific type of correlated survival data in which recurrent event times of a subject are stochastically ordered. Given the ordinal nature of recurrence times, this article focuses on conditional regression analysis. A semiparametric hazards model, including the structural and episode-specific parameters, is proposed for recurrence time data. In this model the order of episodes serves as the stratification variable. Estimation of the structural parameter can be constructed on the basis of all of the observed recurrence times. The structural parameter is estimated by the profile-likelihood approach. Although the structural parameter estimator is asymptotically normal, the episode-specific parameters may or may not be estimated consistently due to the sparseness of data for specific events. Examples are presented to illustrate the performance of the estimators of the structural and episode-specific parameters. An extension of the univariate recurrent events to the bivariate events, which occur repeatedly and sequentially, is discussed with an example.

Original languageEnglish (US)
Pages (from-to)1221-1230
Number of pages10
JournalJournal of the American Statistical Association
Issue number448
StatePublished - Dec 1 1999


  • Correlated survival data
  • Counting processes
  • Martingale
  • Partial likelihood
  • Profile likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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