Regression analysis of general mixed recurrent event data

Ryan Sun (), Dayu Sun, Liang Zhu and Jianguo Sun
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Ryan Sun: University of Texas MD Anderson Cancer Center
Dayu Sun: Emory University Rollins School of Public Health
Liang Zhu: Eisai US
Jianguo Sun: University of Missouri

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 4, No 6, 807-822

Abstract: Abstract In modern biomedical datasets, it is common for recurrent outcomes data to be collected in an incomplete manner. More specifically, information on recurrent events is routinely recorded as a mixture of recurrent event data, panel count data, and panel binary data; we refer to this structure as general mixed recurrent event data. Although the aforementioned data types are individually well-studied, there does not appear to exist an established approach for regression analysis of the three component combination. Often, ad-hoc measures such as imputation or discarding of data are used to homogenize records prior to the analysis, but such measures lead to obvious concerns regarding robustness, loss of efficiency, and other issues. This work proposes a maximum likelihood regression estimation procedure for the combination of general mixed recurrent event data and establishes the asymptotic properties of the proposed estimators. In addition, we generalize the approach to allow for the existence of terminal events, a common complicating feature in recurrent event analysis. Numerical studies and application to the Childhood Cancer Survivor Study suggest that the proposed procedures work well in practical situations.

Keywords: Event history study; Panel binary data; Panel count data; Recurrent event data; Terminal event (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10985-023-09604-9

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