Nonparametric and semiparametric trend analysis for stratified recurrence times

Mei Cheng Wang, Ying Qing Chen

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Recurrent event data are frequently encountered in longitudinal follow-up studies when the occurrences of multiple events are considered as the major outcomes. Suppose that the recurrent events are of the same type and the variable of interest is the recurrence time between successive events. In many applications, the distributional pattern of recurrence times can be used as an index for the progression of a disease. Such a distributional pattern is important for understanding the natural history of a disease or for confirming long-term treatment effect. In this article, we discuss and define the comparability of recurrence times. Nonparametric and semiparametric methods are developed for testing trend of recurrence time distributions and estimating trend parameters in regression models. The construction of the methods is based on comparable recurrence times from stratified data. A real data example is presented to illustrate the use of methodology.

Original languageEnglish (US)
Pages (from-to)789-794
Number of pages6
Issue number3
StatePublished - 2000


  • Accelerated failure time model
  • Comparable recurrence times
  • Longitudinal studies
  • Recurrent event data
  • Truncation

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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