title: Modeling zero-inflated count data when exposure varies: With an application to tumor counts.
abstract: This paper is concerned with the analysis of zero-inflated count data when time of exposure varies. It proposes a modified zero-inflated count data model where the probability of an extra zero is derived from an underlying duration model with Weibull hazard rate. The new model is compared to the standard Poisson model with logit zero inflation in an application to the effect of treatment with thiotepa on the number of new bladder tumors.
authors.affiliation: Department of Economics, University of Zurich, CH-8032 Zurich, Switzerland.
authors.forename: Gregori
authors.initials: G
authors.lastname: Baetschmann
authors.forename: Rainer
authors.initials: R
authors.lastname: Winkelmann
mesh_terms: D001699:Biometry / Q000379:methods*; D005500:Follow-Up Studies; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D016032:Randomized Controlled Trials as Topic; D012008:Recurrence; D013269:Stochastic Processes; D013852:Thiotepa / Q000627:therapeutic use; D013997:Time Factors; D047368:Tumor Burden; D001749:Urinary Bladder Neoplasms / Q000188:drug therapy / Q000473:pathology*
chemical_list: D013852:Thiotepa
keywords: Complementary log-log link; Exposure; Extra zeros; Poisson regression