Identifying Maximum-Accuracy Cut-Points for Diagnostic Indexes via ODA

Ariel Linden & Paul R. Yarnold

Linden Consulting Group, LLC & Optimal Data Analysis, LLC

Maximizing the discriminatory accuracy of a diagnostic or screening test is paramount to correctly identifying individuals with vs. without the disease or disease marker. In this paper we demonstrate the use of ODA to identify the optimal cut-point which best discriminates between those with vs. without the disease (or marker) under study, for any diagnostic test. We illustrate this methodology using a dataset composed of a range of repeated biomedical voice measurements from 31 people, 23 with Parkinson’s disease (PD). A logistic regression model was used to estimate the probability that each observation was from a person with vs. without PD as a function of 22 voice measurement variables, entered in the model as main effects only. Five different methods for computing a diagnostic cut-point on estimated probability are compared.

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