  The odds ratio measure is used in health and social surveys where the odds of
a certain event is to be compared between two populations. It is defined using
logistic regression, and requires that data from surveys are accompanied by
their weights. A nonparametric estimation method that incorporates survey
weights and auxiliary information may improve the precision of the odds ratio
estimator. It consists in $B$-spline calibration which can handle the nonlinear
structure of the parameter. The variance is estimated through linearization.
Implementation is possible through standard survey softwares. The gain in
precision depends on the data as shown on two examples.
