Widely used, linear classification functions may be derived on the basis of several different statistical paradigms. Since, regardless of choice, analysis generally yields an equation with unwieldy intercept and attribute coefficients, researchers often construct simpler scoring schemes based on unit weights. Accordingly, for applications involving entirely binary data, we discuss a simple procedure for obtaining unit-weighted (that is, we restrict attribute coefficients to the values of 0, 1 or -1) MultiODA functions that explicitly maximize classification accuracy in the training sample. We illustrate this with an application involving prediction of in-hospital mortality of patients receiving cardiopulmonary resuscitation. In training analysis of 88 patients, unit-weighted MultiODA outperformed prior scoring schemes and logistic regression analysis. Unit-weighted MultiODA also yielded superior hold-out (cross-generalizability) validity for an independent sample of 26 patients.
|Original language||English (US)|
|Number of pages||10|
|Journal||Statistics in Medicine|
|State||Published - Oct 30 1998|
ASJC Scopus subject areas
- Statistics and Probability