Regression analysis of general mixed recurrent event data.
Event history study
Panel binary data
Panel count data
Recurrent event data
Terminal event
Journal
Lifetime data analysis
ISSN: 1572-9249
Titre abrégé: Lifetime Data Anal
Pays: United States
ID NLM: 9516348
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
received:
26
07
2022
accepted:
23
06
2023
medline:
25
9
2023
pubmed:
13
7
2023
entrez:
12
7
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37438585
doi: 10.1007/s10985-023-09604-9
pii: 10.1007/s10985-023-09604-9
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
807-822Subventions
Organisme : NIDCR NIH HHS
ID : R03 DE029238
Pays : United States
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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