Robust estimation for panel count data with informative observation times and censoring times

Hangjin Jiang, Wen Su, Xingqiu Zhao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.

Original languageEnglish
Pages (from-to)65-84
Number of pages20
JournalLifetime Data Analysis
Volume26
Issue number1
DOIs
Publication statusPublished - Jan 2020

Keywords

  • Informative censoring times
  • Informative observation times
  • Panel count data
  • Robust estimation
  • Semiparametric regression

ASJC Scopus subject areas

  • Applied Mathematics

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