An Additive-Multiplicative Mean Model for Panel Count Data with Dependent Observation and Dropout Processes

• Activity Number: 592 - New Developments in Experiment Design and Statistical Modeling
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Yang Li* and Guanglei Yu and Liang Zhu and Hui Zhao and (Tony) Jianguo Sun and Leslie Robison
• Companies: UNC-Charlotte and Eli Lilly and Company and University of Texas Health Science Center at Houston and Central China Normal University and University of Missouri and St. Jude Children's Research Hospital
• Keywords: additive-multiplicative mean model; arti cial censoring; dependent dropout process; dependent observation process; estimating equations; panel count data

Abstract

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In particular, the proportional rates model and the accelerated failure time model are employed to describe covariate effects on the observation and dropout processes, respectively. For estimation of regression parameters, some estimating equation-based procedures are developed and asymptotic properties of the proposed estimates are established. In addition, a resampling approach is proposed for estimating covariance matrix of the proposed estimates and a model checking procedure is provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations and it is applied to a motivating set of real data.