Semiparametric transformation models for joint analysis of multivariate recurrent and terminal events

Liang Zhu*, Jianguo Sun, Deo Kumar Srivastava, Xingwei Tong, Wendy Leisenring, Hui Zhang, Leslie L. Robison

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Recurrent event data occur in many clinical and observational studies, and in these situations, there may exist a terminal event such as death that is related to the recurrent event of interest. In addition, sometimes more than one type of recurrent events may occur, that is, one may encounter multivariate recurrent event data with some dependent terminal event. For the analysis of such data, one must take into account the dependence among different types of recurrent events and that between the recurrent events and the terminal event. In this paper, we extend a method for univariate recurrent and terminal events and propose a joint modeling approach for regression analysis of the data and establish the finite and asymptotic properties of the resulting estimates of unknown parameters. The method is applied to a set of bivariate recurrent event data arising from a long-term follow-up study of childhood cancer survivors.

Original languageEnglish (US)
Pages (from-to)3010-3023
Number of pages14
JournalStatistics in Medicine
Volume30
Issue number25
DOIs
StatePublished - Nov 10 2011

Keywords

  • Joint modeling
  • Multivariate analysis
  • Regression analysis
  • Survival analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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