A semiparametric likelihood-based method for regression analysis of mixed panel-count data

Liang Zhu, Ying Zhang, Yimei Li, Jianguo Sun, Leslie L. Robison

Research output: Contribution to journalArticle

Abstract

Panel-count data arise when each study subject is observed only at discrete time points in a recurrent event study, and only the numbers of the event of interest between observation time points are recorded (Sun and Zhao, 2013). However, sometimes the exact number of events between some observation times is unknown and what we know is only whether the event of interest has occurred. In this article, we will refer this type of data to as mixed panel-count data and propose a likelihood-based semiparametric regression method for their analysis by using the nonhomogeneous Poisson process assumption. However, we establish the asymptotic properties of the resulting estimator by employing the empirical process theory and without using the Poisson assumption. Also, we conduct an extensive simulation study, which suggests that the proposed method works well in practice. Finally, the method is applied to a Childhood Cancer Survivor Study that motivated this study.

Original languageEnglish (US)
Pages (from-to)488-497
Number of pages10
JournalBiometrics
Volume74
Issue number2
DOIs
StatePublished - Jun 2018

Keywords

  • Maximum likelihood method
  • Panel-binary data
  • Panel-count data
  • Semiparametric estimation efficiency
  • Semiparametric regression analysis

ASJC Scopus subject areas

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

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