Estimation Methods

Methods to estimate the regression parameters of a logistic model in the presence of missing values in the covariates can be divided into two classes. The first class contains ad-hoc-methods, which try to manipulate the missing values in a simple manner in order to obtain an artificially completed table without missing values. The widespread use of these methods relates mainly to the fact that we can use standard statistical software for the analysis of the completed table. But a major drawback of these methods are their poor statistical properties. For many missing value mechanisms, even if they satisfy the MAR assumption, the methods may yield inconsistent estimates or they use the information of the incomplete observations in a very inefficient manner. Moreover the validity of variance estimates and confidence intervals is in doubt, because the process of manipulating the table is neglected. For the second class of methods consistency is implied by the estimation principle and estimates of asymptotic variance are available. The drawback of these methods is the increased effort for the implementation, because standard statistical software cannot be used directly.