Using quantile regression to create baseline norms for neuropsychological tests

Abstract

IntroductionThe Uniform Data Set (UDS) contains neuropsychological test scores and demographic information for participants at Alzheimer's disease centers across the United States funded by the National Institute on Aging. Mean regression analysis of neuropsychological tests has been proposed to detect cognitive decline, but the approach requires stringent assumptions.MethodsWe propose using quantile regression to directly model conditional percentiles of neuropsychological test scores. An online application allows users to easily implement the proposed method.ResultsScores from 13 different neuropsychological tests were analyzed for 5413 cognitively normal participants in the UDS. Quantile and mean regression models were fit using age, gender, and years of education. Differences between the mean and quantile regression estimates were found on the individual measures.DiscussionQuantile regression provides more robust estimates of baseline percentiles for cognitively normal adults. This can then serve as standards against which to detect individual cognitive decline.

Vibration threshold in non-diabetic subjects

“…Regression equations were computed for all points of measurement: big toes, insteps, lateral malleoli, and wrists. Previous studies used quantile regressions to establish a corresponding regression equation [15,16]. To assess the accuracy of the quantile regression model, the standard Koenker and Bassett method as well as bootstrap resampling with performing 2000 replications were conducted to provide standard errors [17].…”

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“…Normative data derived from regression modeling can provide a more fine-grained view of age-related cognitive change. However this approach assumes equal rates of change across performance levels, which is problematic where different trajectories may be expected for those with high versus low performance [1] . As noted by Salthouse [11] , it is unclear whether aging causes a cognitive shift in the entire distribution, or if differences between people increase with age, with only some individuals experiencing cognitive decline, and others remaining stable or improving.…”

“…A detailed report of quantile regression modeling in comparison with linear regression in neuropsychological test scores is presented by Sherwood et al. [1] .…”

“…Normative data standardizes an individual's cognitive performance relative to peers. This helps to identify impairment and can reveal atypical declines [1]. However, in mid‐to‐late life, cognitive decline frequently occurs as a part of the normal aging process [2], affecting general cognitive functions, as well as a range of cognitive domains including fine motor skills, information processing, executive functions, visuospatial ability, and memory [3–5].…”

Polygenic Risk Score of Sporadic late Onset Alzheimer Disease Reveals a Shared Architecture with the Familial and Early Onset Forms

“…We also investigated the effect of PRS on AAO in all sporadic AD cases (including all early--and late--onset cases) in specific quantiles of the AAO. We employed quantile regression analysis to model the association of the PRS to 5--quantiles (quintiles) of AAO [42]. This analysis showed that for all of the quintiles the PRS is significantly associated with AAO (p--values are < 0.05; Supplementary Table 1 panel A).…”