Premium
Semiparametric Regression Analysis of Panel Count Data: A Practical Review
Author(s) -
Chiou Sy Han,
Huang ChiungYu,
Xu Gongjun,
Yan Jun
Publication year - 2019
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12271
Subject(s) - covariate , count data , event (particle physics) , computer science , semiparametric regression , econometrics , statistics , counting process , regression analysis , regression , mathematics , physics , quantum mechanics , poisson distribution
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 package spef . 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.