  To capture the dependences of a disease on several risk factors, a challenge
is to combine model-based estimation with evidence-based arguments. Standard
case-control methods allow estimation of the dependences of a rare disease on
several regressors via logistic regressions. For case-control studies, the
sampling design leads to samples from two different populations and for the set
of regressors in every logistic regression, these samples are then mixed and
taken as given observations. But, it is the differences in independence
structures of regressors for cases and for controls that can improve logistic
regression estimates and guide us to the important feature dependences that are
specific to the diseased. A case-control study on laryngeal cancer is used as
illustration.
