Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition

Affiliations

¹ Center for Economic and Social Research, University of Southern California, VPD, 635 Downey Way, Los Angeles, CA, 90089, USA. emmanich@usc.edu.
² Lenoard Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA. emmanich@usc.edu.
³ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1845 N. Soto St, Los Angeles, CA, 90032, USA.
⁴ Department of Clinical and Health Psychology, University of Florida, 1225 Center Dr, Gainesville, FL, 32603, USA.
⁵ Department of Epidemiology, School of Public Health, Boston University, 715 Albany St, Boston, MA, 02118, USA.
⁶ Department of Biostatistics, Sanders-Brown Center On Aging, Lexington, KY, 40536, USA.
⁷ Department of Neurology, University of California Davis School of Medicine, Sacramento, CA, 95816, USA.
⁸ Department of Psychology, University of Victoria, Victoria, BC, V8W 2Y2, Canada.
⁹ Taub Institute for Research On Alzheimers Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
¹⁰ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, 213 Quarry Road, Palo Alto, CA, 94304, USA.
¹¹ Department of Educational Psychology, University of North Texas, 1300 W. Highland Street Denton, Denton, TX, 76203, USA.
¹² Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21231, USA.

PMID: 39567460
PMCID: PMC12089424 (available on 2026-05-20)
DOI: 10.1007/s11065-024-09655-1

Review

Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition

Emma Nichols et al. Neuropsychol Rev. 2024.

. 2024 Nov 20:10.1007/s11065-024-09655-1.

doi: 10.1007/s11065-024-09655-1. Online ahead of print.

Affiliations

¹ Center for Economic and Social Research, University of Southern California, VPD, 635 Downey Way, Los Angeles, CA, 90089, USA. emmanich@usc.edu.
² Lenoard Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA. emmanich@usc.edu.
³ Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1845 N. Soto St, Los Angeles, CA, 90032, USA.
⁴ Department of Clinical and Health Psychology, University of Florida, 1225 Center Dr, Gainesville, FL, 32603, USA.
⁵ Department of Epidemiology, School of Public Health, Boston University, 715 Albany St, Boston, MA, 02118, USA.
⁶ Department of Biostatistics, Sanders-Brown Center On Aging, Lexington, KY, 40536, USA.
⁷ Department of Neurology, University of California Davis School of Medicine, Sacramento, CA, 95816, USA.
⁸ Department of Psychology, University of Victoria, Victoria, BC, V8W 2Y2, Canada.
⁹ Taub Institute for Research On Alzheimers Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
¹⁰ Department of Neurology and Neurological Sciences, Stanford University School of Medicine, 213 Quarry Road, Palo Alto, CA, 94304, USA.
¹¹ Department of Educational Psychology, University of North Texas, 1300 W. Highland Street Denton, Denton, TX, 76203, USA.
¹² Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21231, USA.

PMID: 39567460
PMCID: PMC12089424 (available on 2026-05-20)
DOI: 10.1007/s11065-024-09655-1

Abstract

Existing studies examining the predictive ability of biomarkers for cognitive outcomes do not account for variance due to measurement error, which could lead to under-estimates of the proportion of variance explained. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 1084) to estimate the proportion of variance explained by Alzheimer's disease (AD) imaging biomarkers in four cognitive outcomes: memory, executive functioning, language, and visuospatial functioning. We compared estimates from standard models that do not account for measurement error, and multilevel models that do account for measurement error. We also examined estimates across diagnostic subgroups (normal, MCI, AD). Estimates of the proportion of variance explained from multilevel models accounting for measurement error were larger (e.g., for language, 9-47% vs. 7-34% under standard modeling), with relatively greater differences between standard and multilevel measurement models for cognitive outcomes that have larger measurement error variance. Heterogeneity across subgroups also emphasized the importance of sample composition. Future studies should evaluate measurement error adjustments when considerable measurement error in cognitive outcomes is suspected.

Keywords: Bias; Biomarkers; Cognition; Dementia; Measurement.

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Conflict of interest statement

Declarations. Competing Interests: The authors declare no competing interests.

References

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Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition

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Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition

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