Semiparametric Regression Models for RepeatedEvents with Random E ects and Measurement

Statistical methodology is presented for the regression analysis of multiple events in the presence of random eeects and measurement error. Omitted covariates are modeled as random eeects. Our approach to parameter estimation and signiicance testing is to start with a naive model of semi-parametric 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.

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