Empirical likelihood inference for the panel count data with informative observation process

Faysal Satter, Yichuan Zhao () and Ni Li
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Faysal Satter: Data Analytics and Computational Intelligence, Lowe’s Companies, Inc.
Yichuan Zhao: Georgia State University
Ni Li: Hainan Normal University

Statistical Papers, 2024, vol. 65, issue 5, No 15, 3039-3061

Abstract: Abstract Panel count data refer to interval-censored recurrent event data. Each study subject can only be observed at discrete time points, leading to knowledge about the total number of events occurring between observations. The observation times can be also different among subjects and carry important information about the underlying recurrent process. In this paper, an empirical likelihood (EL) method for panel count data with informative observation times is proposed. Based on the influence function, we formulate an empirical likelihood ratio for the vector of regression coefficients, and the Wilks’ theorem is established. Simulation studies are carried out to compare the performance of empirical likelihood with normal approximation methods. Finally, the EL method is compared with existing approaches, utilizing an illustrative example drawn from a bladder cancer study.

Keywords: Panel count data; Dependent observation; Empirical likelihood; Semiparametric transformation model; Wilks’ Theorem (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00362-023-01506-0

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