title: Regression analysis of mixed recurrent-event and panel-count data.
abstract: In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation.
authors.affiliation: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
authors.forename: Liang
authors.initials: L
authors.lastname: Zhu
authors.affiliation: School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.
authors.forename: Xinwei
authors.initials: X
authors.lastname: Tong
authors.affiliation: Department of Statistics, University of Missouri, Columbia, MO 65211, USA and School of Mathematics, Jilin University, Changchun 130012, China sunj@missouri.edu.
authors.forename: Jianguo
authors.initials: J
authors.lastname: Sun
authors.affiliation: Department of Statistics, Tamkang University, Tamsui, Taiwan 25137, China.
authors.forename: Manhua
authors.initials: M
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
authors.forename: Deo Kumar
authors.initials: DK
authors.lastname: Srivastava
authors.affiliation: Department of Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA.
authors.forename: Wendy
authors.initials: W
authors.lastname: Leisenring
authors.affiliation: Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
authors.forename: Leslie L
authors.initials: LL
authors.lastname: Robison
mesh_terms: D006801:Humans; D016013:Likelihood Functions; D012044:Regression Analysis
keywords: Estimating equation-based approach; Maximum likelihood approach; Regression analysis