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Regression Analysis of Panel Count Data with Dependent Observation Times
Author(s) -
Sun Jianguo,
Tong Xingwei,
He Xin
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00808.x
Subject(s) - statistics , count data , inference , regression analysis , conditional independence , econometrics , regression , panel data , independence (probability theory) , statistical inference , mathematics , event (particle physics) , computer science , artificial intelligence , physics , quantum mechanics , poisson distribution
Summary Panel count data often occur in long‐term studies that concern occurrence rate of a recurrent event. Methods have been proposed for regression analysis of panel count data, but most of the existing research focuses on situations where observation times are independent of longitudinal response variables and therefore rely on conditional inference procedures given the observation times. This article considers a different situation where the independence assumption may not hold. That is, the observation times and the response variable may be correlated. For inference, estimating equation approaches are proposed for estimation of regression parameters and both large and finite sample properties of the proposed estimates are established. An illustrative example from a cancer study is provided.