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

Hangjin Jiang (), Wen Su and Xingqiu Zhao
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Hangjin Jiang: ZheJiang University
Wen Su: Haitong International Securities Group
Xingqiu Zhao: The Hong Kong Polytechnic University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2020, vol. 26, issue 1, No 4, 65-84

Abstract: 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.

Keywords: Semiparametric regression; Panel count data; Informative observation times; Informative censoring times; Robust estimation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10985-018-09457-7

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