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Conditional and Unconditional Categorical Regression Models with Missing Covariates
Author(s) -
Satten Glen A.,
Carroll Raymond J.
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00384.x
Subject(s) - covariate , categorical variable , missing data , statistics , mathematics , regression analysis , regression , econometrics
Summary. 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.