Conditional and unconditional categorical regression models with missing covariates.

G A Satten; R J Carroll

doi:10.1111/j.0006-341x.2000.00384.x

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Conditional and unconditional categorical regression models with missing covariates.

G A Satten·R J Carroll

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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.

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Biometry Case-Control Studies Epidemiologic Methods Humans Likelihood Functions Models, Statistical Probability Regression Analysis Journal Article Research Support, U.S. Gov't, P.H.S.

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G A Satten, R J Carroll. 2000. Conditional and unconditional categorical regression models with missing covariates.. https://doi.org/10.1111/j.0006-341x.2000.00384.x

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