title: Monotone spline-based least squares estimation for panel count data with informative observation times.
abstract: This article discusses the statistical analysis of panel count data when the underlying recurrent event process and observation process may be correlated. For the recurrent event process, we propose a new class of semiparametric mean models that allows for the interaction between the observation history and covariates. For inference on the model parameters, a monotone spline-based least squares estimation approach is developed, and the resulting estimators are consistent and asymptotically normal. In particular, our new approach does not rely on the model specification of the observation process. The proposed inference procedure performs well through simulation studies, and it is illustrated by the analysis of bladder tumor data.
authors.affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, China.
authors.forename: Shirong
authors.initials: S
authors.lastname: Deng
authors.affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, China.
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China.
authors.forename: Xingqiu
authors.initials: X
authors.lastname: Zhao
mesh_terms: D001699:Biometry / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D013997:Time Factors; D001749:Urinary Bladder Neoplasms / Q000628:therapy
keywords: Informative observation process; Least squares estimation; Monotone B-splines; Panel count data; Semiparametric mean models