Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition.
Emma Nichols1,2, Vahan Aslanyan3, Tamare V Adrien4
1Center for Economic and Social Research, University of Southern California, VPD, 635 Downey Way, Los Angeles, CA, 90089, USA. emmanich@usc.edu.
Neuropsychology Review
|November 20, 2024
Summary
Accounting for measurement error in cognitive tests improves estimates of how well Alzheimer's disease (AD) biomarkers predict cognitive decline. This is crucial for understanding disease progression and developing effective treatments.
Area of Science:
- Neuroscience
- Biomarkers
- Cognitive Science
Background:
- Existing studies on biomarker predictive ability for cognitive outcomes often overlook measurement error variance.
- This oversight can lead to underestimation of the true proportion of variance explained by biomarkers.
Purpose of the Study:
- To estimate the variance explained by Alzheimer's disease (AD) imaging biomarkers in cognitive outcomes.
- To compare standard models with multilevel models that account for measurement error.
- To examine these estimates across different diagnostic subgroups (normal, MCI, AD).
Main Methods:
- Utilized data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N=1084).
- Employed standard statistical models and multilevel models to assess variance explained.
- Analyzed cognitive outcomes including memory, executive functioning, language, and visuospatial functioning.
Main Results:
- Multilevel models accounting for measurement error yielded larger estimates of variance explained compared to standard models.
- For instance, language outcomes showed 9-47% variance explained in multilevel models versus 7-34% in standard models.
- Differences were more pronounced for cognitive outcomes with higher measurement error variance.
Conclusions:
- Measurement error adjustments are vital for accurate estimation of biomarker predictive power in cognitive outcomes.
- Sample composition significantly influences results, highlighting the importance of subgroup analysis.
- Future research should incorporate measurement error adjustments, especially when cognitive outcome measurement error is substantial.
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