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

• Carroll, Raymond

published proceedings

• Biometrics

author list (cited authors)

• Satten, G. A., & Carroll, R. J.

citation count

• 34

complete list of authors

• Satten, GA||Carroll, RJ

publication date

• June 2000

publisher

• Wiley  Publisher

published in

• Biometrics  Journal

keywords

• Biometry
• Case-Control Studies
• Epidemiologic Methods
• Humans
• Likelihood Functions
• Models, Statistical
• Probability
• Regression Analysis

PubMed Central ID

• 10877293

Digital Object Identifier (DOI)

• 10.1111/j.0006-341x.2000.00384.x

start page

• 384

end page

• 388

volume

• 56

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1111%2Fj.0006-341x.2000.00384.x