title: Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.
abstract: This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data.
authors.affiliation: Professor, Department of Psychology, Loyola University Chicago, Chicago, Illinois, USA.
authors.forename: Fred B
authors.initials: FB
authors.lastname: Bryant
mesh_terms: D016002:Discriminant Analysis; D000069550:Machine Learning / Q000706:statistics & numerical data; D015203:Reproducibility of Results
keywords: exact probability; generalizability; predictive accuracy; reproducibility