Semiparametric transformation models for multivariate panel count data with dependent observation process

• Published in: Canadian journal of statistics Vol. 39; no. 3; pp. 458 - 474
• Main Authors: Park, Do-hwan, Sun, Jianguo, Kim, Kyungmann, Li, Ni
• Format: Journal Article
• Language: English
• Published: 01.09.2011
• Subjects: Cancer; Estimation; Panel data; Regression analysis; Simulation; Skin
• ISSN: 0319-5724
• DOI: 10.1002/cjs

This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial.