Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.
Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21287-7218, USA.
S Marc
SM
Testa
Jessica M
JM
Winicki
Godfrey D
GD
Pearlson
Barry
B
Gordon
David J
DJ
Schretlen
D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D006801:Humans; D007360:Intelligence / Q000502:physiology*; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000295:instrumentation; D008875:Middle Aged; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D011932:Reading; D012737:Sex Factors; D014145:Trail Making Test; D055815:Young Adult