A class of mixed models for recurrent event data

Liuquan Sun, Xingqiu Zhao, Jie Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)

Abstract

In this article, we propose a class of mixed models for recurrent event data. The new models include the proportional rates model and Box-Cox transformation rates models as special cases, and allow the effects of covariates on the rate functions of counting processes to be proportional or convergent. For inference on the model parameters, estimating equation approaches are developed. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed procedure is evaluated through simulation studies. A real example with data taken from a clinic study on chronic granulomatous disease (CGD) is also illustrated for the use of the proposed methodology.
Original languageEnglish
Pages (from-to)578-590
Number of pages13
JournalCanadian Journal of Statistics
Volume39
Issue number4
DOIs
Publication statusPublished - 1 Dec 2011

Keywords

  • Counting process
  • Marginal rate model
  • Mixed model
  • Partial-score function
  • Proportional and Convergent effects

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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