Point Process Regression Models for Multiple Events with Random Eeects and Measurement Error

Statistical methodology is presented for the analysis of multiple events with random eeects and measurement error. We model multiple events in a general space using a random measure, and deene point process regression models with residual random eeects. Our approach to parameter estimation and signiicance testing is to start with a simple naive model of Poisson process regression, and then to adjust for random eeects and any possible covariate measurement error. We illustrate the techniques with data from a randomized clinical trial for the prevention of recurrent skin tumors.