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Using quantile regression to create baseline norms for neuropsychological tests
Ben Sherwood1, Andrew Xiao-Hua Zhou2, Sandra Weintraub3
1Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
Alzheimer'S & Dementia (Amsterdam, Netherlands)
|May 31, 2016
Summary
Quantile regression offers a more robust method for establishing cognitive baselines in adults using the Uniform Data Set (UDS). This approach aids in detecting individual cognitive decline more effectively than traditional mean regression.
Area of Science:
- Neuroscience
- Biostatistics
- Gerontology
Background:
- The Uniform Data Set (UDS) collects neuropsychological test scores and demographic data from Alzheimer's disease centers.
- Mean regression is a proposed method for detecting cognitive decline but has strict assumptions.
Purpose of the Study:
- To propose and evaluate quantile regression for modeling neuropsychological test scores.
- To provide a user-friendly online application for implementing quantile regression.
Main Methods:
- Utilized quantile regression to model conditional percentiles of neuropsychological test scores.
- Analyzed data from 5413 cognitively normal participants in the UDS.
- Compared quantile regression with mean regression using age, gender, and education as predictors.
Main Results:
- Quantile regression directly models conditional percentiles of test scores.
- Significant differences were observed between mean and quantile regression estimates for individual measures.
- The study analyzed scores from 13 different neuropsychological tests.
Conclusions:
- Quantile regression yields more robust estimates of baseline cognitive percentiles in normal adults.
- These robust estimates can serve as improved standards for identifying individual cognitive decline.
Keywords:
Alzheimer's diseaseCognitive declineEarly detectionNeuropsychological assessmentQuantile regressionMore Related Videos
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