title: Regression models for recurrent event data: parametric random effects models with measurement error.
abstract: Statistical methodology is presented for the statistical analysis of non-linear measurement error models. Our approach is to provide adjustments for the usual maximum likelihood estimators, their standard errors and associated significance tests in order to account for the presence of measurement error in some of the covariates. We illustrate the technique with a mixed effects Poisson regression model for recurrent event data applied to a randomized clinical trial for the prevention of skin tumours.
authors.affiliation: Statistics Center, Cornell University, Ithaca, NY 14853-3801, USA.
authors.forename: B W
authors.initials: BW
authors.lastname: Turnbull
authors.forename: W
authors.initials: W
authors.lastname: Jiang
authors.forename: L C
authors.initials: LC
authors.lastname: Clark
mesh_terms: D006801:Humans; D016013:Likelihood Functions; D009364:Neoplasm Recurrence, Local / Q000517:prevention & control; D017711:Nonlinear Dynamics; D016012:Poisson Distribution; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012878:Skin Neoplasms / Q000517:prevention & control