  Automated variable selection is widely applied in statistical model
development. Algorithms like forward, backward or stepwise selection are
available in statistical software packages like R and SAS. Many researchers
have criticized the use of these algorithms because the models resulting from
automated selection algorithms are not based on theory and tend to be unstable.
Furthermore, simulation studies have shown that they often select incorrect
variables due to random effects which makes these model building strategies
unreliable. In this article, a comprehensive stepwise selection algorithm
tailored to logistic regression is proposed. It uses multiple criteria in
variable selection instead of relying on one single measure only, like a
$p$-value or Akaike's information criterion, which ensures robustness and
soundness of the final outcome. The result of the selection process might not
be unambiguous. It might select multiple models that could be considered as
statistically equivalent. A simulation study demonstrates the superiority of
the proposed variable selection method over available alternatives.
