Ariel Linden & Paul R. Yarnold
Linden Consulting Group, LLC & Optimal Data Analysis, LLC
Diagnostic screening tests are used to predict an individual’s graduated disease status which is measured on an ordered scale assessing disease progression (severity of illness). Maximizing the predictive accuracy of the diagnostic or screening test is paramount to correctly identifying an individual’s actual score along the ordered continuum. The present study compares two approaches for mapping a statistical model to a diagnostic index in order to make accurate outcome predictions for individuals. The application involves a dataset composed of multiple biomedical voice measurements for 42 individuals with early-stage Parkinson’s disease, who completed a six-month trial of a device for remote symptom progression telemonitoring. For 16 voice measures, each treated as a main effect, ordinary least-squares regression is used to predict baseline motor impairment component score. ODA is used to maximize accuracy of the regression model when it is mapped to the diagnostic index, and results are compared with accuracy achieved by the novometric solution.