Methodology for the Differential Diagnosis of a Complex Data Set

Data from routine CT scan examinations are employed to illustrate the use of the polychotomous logistic regression model as a statistical diagnostic tool. The assumptions of the model, the interpretation of its parameters, and its capabilities are described in detail. In carrying out the analysis on the CT data, a large, relatively sparse data set, many technical difficulties were encountered. Modifications to the methodology that were necessary to permit its implementation are described, and it is demonstrated that an unbiased analysis of T + 1 diagnostic categories can be implemented by separately performing T individual simple logistic analyses. The limitations of the methodology are discussed. It is hoped that this paper may serve as a basis for the practical implementation of the polychotomous logistic model in similar diagnostic settings.