The impact of conventional versus robust norming on cognitive characterization and clinical classification of MCI and dementia

Abstract

We examined the impact of conventional versus robust normative approaches on cognitive characterization and clinical classification of MCI versus dementia. The sample included participants from the National Alzheimer's Coordinating Center Uniform Data Set. Separate demographically adjusted z‐scores for cognitive tests were derived from conventional (n = 4273) and robust (n = 602) normative groups. To assess the impact of deriving scores from a conventional versus robust normative group on cognitive characterization, we examined likelihood of having a low score on each neuropsychological test. Next, we created receiver operating characteristic (ROC) curves for the ability of normed scores derived from each normative group to differentiate between MCI (n = 3570) and dementia (n = 1564). We examined the impact of choice of normative group on classification accuracy by comparing sensitivity and specificity values and areas under the curves (AUC). Compared with using a conventional normative group, using a robust normative group resulted in a higher likelihood of low cognitive scores for individuals classified with MCI and dementia. Comparison of the classification accuracy for distinguishing MCI from dementia did not suggest a statistically significant advantage for either normative approach (Z = −0.29, p = .77; AUC = 0.86 for conventional and AUC = 0.86 for robust). In summary, these results indicate that using a robust normative group increases the likelihood of characterizing cognitive performance as low. However, there is not a clear advantage of using a robust over a conventional normative group when differentiating between MCI and dementia.

Reference Data for Attentional, Executive, Linguistic, and Visual Processing Tests Obtained from Cognitively Healthy Individuals with Normal Alzheimer’s Disease Cerebrospinal Fluid Biomarker Levels

“…Equations used to calculate z-scores accounting for relevant sociodemographic factors are presented in Table 4. Normative tables with the calculations developed and raw scores equivalence to percentiles are available as Supplementary Tables (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

“…These procedures involve selecting individuals who consistently demonstrate healthy cognitive performance over longitudinal follow-ups, while excluding those who later develop MCI or dementia. Such robust normative procedures aimed to increase the sensitivity of norms for the detection of MCI [5,8]. However, robust normative procedures are not limited to clinical stability over longitudinal follow-up, nor to the detection of MCI.…”

“…Since conventional neuropsychological norms might lack enough sensitivity for detecting subtle cognitive decline in preclinical stages, robust normative procedures typically account for the reliable performance of clinically stable populations (including only individuals who maintain a status of healthy cognitive performance on longitudinal follow-up and excluding those that latter on develop MCI/dementia). Robust normative approaches based on longitudinal assessment demonstrated an increase in the probability of labeling cognitive performance as below average in comparison to conventional norms [5][6][7], by establishing higher thresholds for what is considered normal performance or decline according to these robust procedures [8,9].…”