Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study
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Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study Liang Zhu1 · Sangbum Choi2 · Yimei Li3 Leslie L. Robison6
· Xuelin Huang4 · Jianguo Sun5 ·
Received: 26 June 2019 / Accepted: 22 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In long-term follow-up studies on recurrent events, the observation patterns may not be consistent over time. During some observation periods, subjects may be monitored continuously so that each event occurence time is known. While during the other observation periods, subjects may be monitored discretely so that only the number of events in each period is known. This results in mixed recurrent-event and panel-count data. In these data, there is dependence among within-subject events. Furthermore, if the data are collected from multiple centers, then there is another level of dependence among within-center subjects. Literature exists for clustered recurrent-event data, but not for clustered mixed recurrent-event and panel-count data. Ignoring the cluster effect may lead to less efficient analysis. In this paper, we present a marginal modeling approach to take into account the cluster effect and provide asymptotic distributions of the resulting regression parameters. Our simulation study demonstrates that this approach works well for practical situations. It was applied to a study comparing the hospitalization rates between childhood cancer survivors and healthy controls, with
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10985020-09500-6) contains supplementary material, which is available to authorized users.
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Yimei Li [email protected]
1
Biostatistics and Epidemiology Research and Design, Division of Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
2
Department of Statistics, Korea University, Seoul 02841, South Korea
3
Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
4
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
5
Department of Statistics, University of Missouri, Columbia, MO 65211, USA
6
Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
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L. Zhu et al.
data collected from 26 medical institutions across North America during more than 20 years of follow-up. Keywords Cluster effect · Marginal model · Recurrent events · Regression analysis
1 Introduction Recurrent event or event history studies occur in many fields. In recurrent event studies, the event of interest may occur more than once per subject. Examples include tumors, hospitalizations, infections, epilepsies, and acute myocardial infarctions. Two types of data concerning recurrent events have been extensively discussed in the literature. One is recurrent-event data which arise when study subjects are monitored continuously and the times of
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