Conditional and unconditional categorical regression models with missing covariates

We consider methods for analyzing categorical regression models when some c ovariates (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 pro bability that X is observed does not depend on the value of X itself), we p resent a likelihood approach for the observed data that allows the same nui sance parameters to be eliminated in a conditional analysis as when data ar e complete. An example of a matched case-control study is used to demonstra te our approach.