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Statistics (i.e., sensitivity, specificity, hit rates, positive and negative predictive values, odds ratios, and likelihood ratios) that best describe a diagnostic test's ability to classify persons as either "impaired" or "normal," but that are not commonly reported in neuropsychological research, are reviewed. These statistics are applied to Mayo Cognitive Factor Scale scores (MCFS; Smith et al., 1994) to demonstrate information that can be acquired about the diagnostic capabilities of cognitive tests as they are commonly used in clinical settings. Multivariate analyses then generated a statistical model that combines MCFS scores and improves on the diagnostic capabilities of the individual MCFS scores. This model enjoys better diagnostic power than individual scores. It establishes that cognitive testing that uses multiple measures is very good at differentiating normal from impaired cognitive states. Information is also provided that helps clinicians quantify a person's risk for cognitive impairment based on specific cognitive test score(s).
Ivnik et al. (Thu,) studied this question.