  In this work logistic regression when both the response and the predictor
variables may be missing is considered. Several existing approaches are
reviewed, including complete case analysis, inverse probability weighting,
multiple imputation and maximum likelihood. The methods are compared in a
simulation study, which serves to evaluate the bias, the variance and the mean
squared error of the estimators for the regression coefficients. In the
simulations, the maximum likelihood methodology is the one that presents the
best results, followed by multiple imputation with five imputations, which is
the second best. The methods are applied to a case study on the obesity for
schoolchildren in the municipality of Viana do Castelo, North Portugal, where a
logistic regression model is used to predict the International Obesity Task
Force (IOTF) indicator from physical examinations and the past values of the
obesity status. All the variables in the case study are potentially missing,
with gender as the only exception. The results provided by the several methods
are in well agreement, indicating the relevance of the past values of IOTF and
physical scores for the prediction of obesity. Practical recommendations are
given.
