When there are many nuisance parameters in a logistic regression model
, a popular method for eliminating these nuisance parameters is condit
ional logistic regression. Unfortunately, another common problem in a
logistic regression analysis is missing covariate data. With many nuis
ance parameters to eliminate and missing covariates, many investigator
s exclude any subject with missing covariates and then use conditional
logistic regression, often called a complete-case analysis. In this a
rticle, we derive a modified conditional logistic regression that is a
ppropriate with covariates that are missing at random. Performing a co
nditional logistic regression with only the complete cases is convenie
nt with existing statistical packages, but it may give bias if missing
ness is not completely at random.