LOGISTIC-REGRESSION WITH INCOMPLETELY OBSERVED CATEGORICAL COVARIATESINVESTIGATING THE SENSITIVITY AGAINST VIOLATION OF THE MISSING AT RANDOM ASSUMPTION

Missing values in the covariates are a widespread complication in the statistical inference of regression models. The maximum likelihood pri nciple requires specification of the distribution of the covariates, a t least in part. For categorical covariates, log-linear models can be used. Additionally, the missing at random assumption is necessary, whi ch excludes a dependence of the occurrence of missing values on the un observed covariate values. This assumption is often highly questionabl e. We present a framework to specify alternative missing value mechani sms such that maximum likelihood estimation of the regression paramete rs under a specified alternative is possible. This allows investigatio n of the sensitivity of a single estimate against violations of the mi ssing at random assumption. The possible results of a sensitivity anal ysis are illustrated by artificial examples. The practical application is demonstrated by the analysis of two case-control studies.