Using quantile regression to create baseline norms for neuropsychological tests

Ben Sherwood, Andrew Xiao Hua Zhou, Sandra Weintraub, Lan Wang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Introduction: The 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. Methods: We propose using quantile regression to directly model conditional percentiles of neuropsychological test scores. An online application allows users to easily implement the proposed method. Results: Scores 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. Discussion: Quantile 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.

Original languageEnglish (US)
Pages (from-to)12-18
Number of pages7
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume2
DOIs
StatePublished - 2016

Bibliographical note

Funding Information:
The work of B.S. and L.W. was supported in part by National Science Foundation grant NSF DMS-1308960 . The work of A.X.Z. and S.W. was supported in part by NIH / NIA grant U01AG016976 .

Publisher Copyright:
© 2016 The Authors.

Keywords

  • Alzheimer's disease
  • Cognitive decline
  • Early detection
  • Neuropsychological assessment
  • Quantile regression

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