General Regression Models with Missing Values in One of Two Covariates

Abstract

So far we have considered the logistic regression model with two categorical covariates and missing values in one covariate. The considered techniques to handle missing values can also be applied in other regression models, at least if the covariates (and for some methods the outcome variable, too) are categorical. We will now discuss some generalizations to the case of covariates measured on an arbitrary scale, and this discussion will be done within the scope of rather general regression models. We do not consider the special case of Gaussian distributed errors, for which a lot of suggestions have been made. We refer to the excellent review of Little (1992). Note, however, that many of the methods described there depend on the assumption of a joint multivariate normal distribution of all variables.