A Logodds Criterion for Selection of Diagnostic Tests

We propose a criterion for selection of independent binary diagnostic tests (signs). The criterion maximises the difference between the logodds for having the disease and the logodds for not having the disease. A parallel is drawn between the logodds criterion and the standard minimum error criterion. The error criterion is "progression non-monotone" which means that even for independent binary signs, the best set of two signs might not contain the single best sign. The logodds criterion is progression monotone, therefore the selection procedure consists of simply selecting the individually best features. A data set for scrapie in sheep is used as an illustration.