Objective

Two main approaches to the interpretation of cognitive test performance have been utilized for the characterization of disease: evaluating shared variance across tests, as with measures of severity, and evaluating the unique variance across tests, as with pattern and error analysis. Both methods provide necessary information, but the unique contributions of each are rarely considered. This study compares the 2 approaches on their ability to differentially diagnose with accuracy, while controlling for the influence of other relevant demographic and risk variables.

Method

Archival data requested from the NACC provided clinical diagnostic groups that were paired to 1 another through a genetic matching procedure. For each diagnostic pairing, 2 separate logistic regression models predicting clinical diagnosis were performed and compared on their predictive ability. The shared variance approach was represented through the latent phenotype δ, which served as the lone predictor in 1 set of models. The unique variance approach was represented through raw score values for the 12 neuropsychological test variables comprising δ, which served as the set of predictors in the second group of models.

Results

Examining the unique patterns of neuropsychological test performance across a battery of tests was the superior method of differentiating between competing diagnoses, and it accounted for 16-30% of the variance in diagnostic decision making.

Conclusion

Implications for clinical practice are discussed, including test selection and interpretation. (PsycINFO Database Record