TY - JOUR
T1 - Regression analysis of general mixed recurrent event data
AU - Sun, Ryan
AU - Sun, Dayu
AU - Zhu, Liang
AU - Sun, Jianguo
N1 - Funding Information:
The authors thank the Editor-in-Chief, Dr. Mei-Ling Ting Lee, the Associate Editor, and two referees for their careful reviews and many insightful comments that have significantly improved the paper. The authors also gratefully acknowledge the support of NIH grant R03-DE029238.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/10
Y1 - 2023/10
N2 - 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.
AB - 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.
KW - Event history study
KW - Panel binary data
KW - Panel count data
KW - Recurrent event data
KW - Terminal event
UR - http://www.scopus.com/inward/record.url?scp=85164523947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85164523947&partnerID=8YFLogxK
U2 - 10.1007/s10985-023-09604-9
DO - 10.1007/s10985-023-09604-9
M3 - Article
C2 - 37438585
AN - SCOPUS:85164523947
SN - 1380-7870
VL - 29
SP - 807
EP - 822
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 4
ER -