title: Conditional regression analysis of the exposure-disease odds ratio using known probability-of-exposure values.
abstract: Conditional inference methods are proposed for the odds ratio between binary exposure and disease variables when only the probability of exposure is known for each study subject. We develop a conditional likelihood approach that removes nuisance parameters and permits inferences to be made about important parameters in log odds ratio regression models. We also discuss a heuristic procedure based on estimating the (unknown) number of truly exposed individuals; this procedure provides a simple framework for interpreting our likelihood-based statistics, and leads to a Mantel-Haenszel-type estimator and a goodness-of-fit test. As an example of the use of this methodology, we present an analysis of some genetic data of Swift et al. (1976, Cancer Research 36, 209-215).
authors.affiliation: Division of HIV/AIDS, Centers for Disease Control, Atlanta, Georgia 30333.
authors.forename: G A
authors.initials: GA
authors.lastname: Satten
authors.forename: L L
authors.initials: LL
authors.lastname: Kupper
mesh_terms: D000328:Adult; D000368:Aged; D004812:Epidemiologic Methods; D030342:Genetic Diseases, Inborn / Q000453:epidemiology; D006801:Humans; D008433:Mathematics; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D016017:Odds Ratio; D011336:Probability; D012044:Regression Analysis