title: Conditional and unconditional categorical regression models with missing covariates.
abstract: We consider methods for analyzing categorical regression models when some covariates (Z) are completely observed but other covariates (X) are missing for some subjects. When data on X are missing at random (i.e., when the probability that X is observed does not depend on the value of X itself), we present a likelihood approach for the observed data that allows the same nuisance parameters to be eliminated in a conditional analysis as when data are complete. An example of a matched case-control study is used to demonstrate our approach.
authors.affiliation: Centers for Disease Control and Prevention, Atlanta, Georgia 30333, USA. gas0@cdc.gov
authors.forename: G A
authors.initials: GA
authors.lastname: Satten
authors.forename: R J
authors.initials: RJ
authors.lastname: Carroll
mesh_terms: D001699:Biometry / Q000379:methods*; D016022:Case-Control Studies; D004812:Epidemiologic Methods; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D011336:Probability; D012044:Regression Analysis