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

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.