Semiparametric regression analysis of panel count data with informative observation times

Xingqiu Zhao, Xingwei Tong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

27 Citations (Scopus)

Abstract

This paper discusses regression analysis of panel count data that arise naturally when recurrent events are considered. For the analysis of panel count data, most of the existing methods have assumed that observation times are completely independent of recurrent events or given covariates, which may not be true in practice. We propose a joint modeling approach that uses an unobserved random variable and a completely unspecified link function to characterize the correlations between the response variable and the observation times. For inference about regression parameters, estimating equation approaches are developed without involving any estimation for latent variables, and the asymptotic properties of the resulting estimators are established. In addition, a technique is provided for assessing the adequacy of the model. The performance of the proposed estimation procedures are evaluated by means of Monte Carlo simulations, and a data set from a bladder tumor study is analyzed as an illustrative example.
Original languageEnglish
Pages (from-to)291-300
Number of pages10
JournalComputational Statistics and Data Analysis
Volume55
Issue number1
DOIs
Publication statusPublished - 1 Jan 2011

Keywords

  • Estimating equation
  • Informative observation times
  • Mean function model
  • Panel count data
  • Regression analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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