Semiparametric Regression Analysis of Panel Count Data: A Practical Review. (13th June 2018)

• Record Type: Journal Article
• Authors: Chiou, Sy Han; Huang, Chiung‐Yu; Xu, Gongjun; Yan, Jun
• Is Part Of: International statistical review. Volume 87:Number 1(2019)
• Page Start: 24
• Page End: 43
• Publication Date: 2018-06-13
• Subjects: Counting process -- estimating equation -- frailty -- maximum likelihood -- recurrent event; Statistics -- Periodicals; Statistics -- Bibliography -- Periodicals; Statistics -- Bibliography; Statistics -- Periodicals; Statistique; Statistique -- Périodiques; Statistique -- Bibliographie -- Périodiques; Statistique -- Étude et enseignement -- Périodiques; Statistique -- Étude et enseignement -- Bibliographie -- Périodiques; Ressource Internet (Descripteur de forme); Périodique électronique (Descripteur de forme); 519.2
• DOI: 10.1111/insr.12271
• Languages: English
• ISSNs: 0306-7734

Abstract

Summary: Panel count data arise in many applications when the event history of a recurrent event process is only examined at a sequence of discrete time points. In spite of the recent methodological developments, the availability of their software implementations has been rather limited. Focusing on a practical setting where the effects of some time‐independent covariates on the recurrent events are of primary interest, we review semiparametric regression modelling approaches for panel count data that have been implemented in R packagespef . The methods are grouped into two categories depending on whether the examination times are associated with the recurrent event process after conditioning on covariates. The reviewed methods are illustrated with a subset of the data from a skin cancer clinical trial.