  In this paper, the panel count data analysis for recurrent events is
considered. Such analysis is useful for studying tumor or infection recurrences
in both clinical trial and observational studies. A bivariate Gaussian Cox
process model is proposed to jointly model the observation process and the
recurrent event process. Bayesian nonparametric inference is proposed for
simultaneously estimating regression parameters, bivariate frailty effects and
baseline intensity functions. Inference is done through Markov chain Monte
Carlo, with fully developed computational techniques. Predictive inference is
also discussed under the Bayesian setting. The proposed method is shown to be
efficient via simulation studies. A clinical trial dataset on skin cancer
patients is analyzed to illustrate the proposed approach.
