Regression analysis of general mixed recurrent event data.

Autor: Sun R; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA. rsun3@mdanderson.org., Sun D; Department of Biostatistics, Emory University Rollins School of Public Health, Atlanta, GA, USA., Zhu L; Eisai US, Woodcliff Lake, NJ, USA., Sun J; Department of Statistics, University of Missouri, Columbia, MO, USA.
Jazyk: angličtina
Zdroj: Lifetime data analysis [Lifetime Data Anal] 2023 Oct; Vol. 29 (4), pp. 807-822. Date of Electronic Publication: 2023 Jul 12.
DOI: 10.1007/s10985-023-09604-9
Abstrakt: 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.
(© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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