title: Binary regression for risks in excess of subject-specific thresholds.
abstract: We describe models for binary valued data to be used to explain the incidence of disease given the level of a known risk factor. Every individual has an unobservable tolerance of the risk. Risk levels below the individual tolerance do not increase the disease incidence above the background, unexposed rate. We estimate parameters from both the tolerance distribution and the risk function for a large group of mice exposed to very low levels of a known carcinogen.
authors: Department of Epidemiology and Public Health, Yale University, New Haven, Connecticut 06520-8034, USA. heping.zhang@yale.edu
authors: H
authors: H
authors: Zhang
authors: D
authors: D
authors: Zelterman
mesh_terms: D015073:2-Acetylaminofluorene / Q000008:administration & dosage / Q000633:toxicity; D000818:Animals; D001699:Biometry; D002273:Carcinogens / Q000008:administration & dosage / Q000633:toxicity; D004361:Drug Tolerance; D005260:Female; D006801:Humans; D008114:Liver Neoplasms, Experimental / Q000139:chemically induced; D051379:Mice; D015233:Models, Statistical; D019055:No-Observed-Adverse-Effect Level; D012044:Regression Analysis; D012306:Risk; D001749:Urinary Bladder Neoplasms / Q000139:chemically induced
chemical_list: D002273:Carcinogens; D015073:2-Acetylaminofluorene