Validation of a Bayesian Diagnostic and Inferential Model for Evidence-Based Neuropsychological Practice

Evidence-based diagnostic methods have clinical and research applications in neuropsychology. A flexible Bayesian model was developed to yield diagnostic posttest probabilities from a single person's neuropsychological score profile by utilizing sample descriptive statistics of the test battery across diagnostic populations of interest.

Three studies examined the model's performance. One simulation examined estimation accuracy of true z-scores. A diagnostic accuracy simulation utilized descriptive statistics from two popular neuropsychological tests, the Wechsler Adult Intelligence Scale-IV (WAIS-IV) and Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The final simulation examined posterior predictive accuracy of scores to those reported in the WAIS manual.

The model produced minimally biased z-score estimates (root mean square errors: .02-.18) with appropriate credible intervals (95% credible interval empirical coverage rates: .94-1.00). The model correctly classified 80.87% of simulated normal, mild cognitive impairment, and Alzheimer's disease cases using a four subtest WAIS-IV and the RBANS compared to accuracies of 60.67-65.60% from alternative methods. The posterior predictions of raw scores closely aligned to percentile estimates published in the WAIS-IV manual.

This model permits estimation of posttest probabilities for various combinations of neuropsychological tests across any number of clinical populations with the principal limitation being the accessibility of applicable reference samples. The model produced minimally biased estimates of true z-scores, high diagnostic classification rates, and accurate predictions of multiple reported percentiles while using only simple descriptive statistics from reference samples. Future nonsimulation research on clinical data is needed to fully explore the utility of such diagnostic prediction models.