Conditional and Unconditional Categorical Regression Models with Missing Covariates
2000
Satten, Glen A. | Carroll, Raymond J.
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 casecontrol study is used to demonstrate our approach.
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