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Battery length as a moderator of classification accuracy for cognitive impairment: a Monte Carlo simulation using actuarial and Bayesian methods.

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Τίτλος: Battery length as a moderator of classification accuracy for cognitive impairment: a Monte Carlo simulation using actuarial and Bayesian methods.
Συγγραφείς: Davis A; Department of Behavioral Health, Baptist Medical Center, Jacksonville, FL.
Πηγή: Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists [Arch Clin Neuropsychol] 2026 Sep 09; Vol. 41 (7).
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Oxford University Press Country of Publication: United States NLM ID: 9004255 Publication Model: Print Cited Medium: Internet ISSN: 1873-5843 (Electronic) Linking ISSN: 08876177 NLM ISO Abbreviation: Arch Clin Neuropsychol Subsets: MEDLINE
Imprint Name(s): Publication: Oxford : Oxford University Press
Original Publication: New York : Pergamon Press, c1986-
Ιατρικοί όροι (MeSH): Neuropsychological Tests*/standards , Neuropsychological Tests*/statistics & numerical data , Cognitive Dysfunction*/diagnosis , Cognitive Dysfunction*/classification , Monte Carlo Method*, Humans ; Bayes Theorem ; Psychometrics ; Sensitivity and Specificity
Περίληψη: Objective: This Monte Carlo simulation study examined whether neuropsychological test battery length impacts cognitive impairment classification accuracy using actuarial and Bayesian interpretive frameworks. Three hypotheses were tested: (a) longer batteries yield higher false-positive rates due to cumulative probability effects; (b) longer batteries show superior classification accuracy; and (c) abbreviated batteries have higher false-negative rates.
Method: Four simulated batteries of varying length, domain coverage, and redundancy were applied to 10,000 virtual patient profiles. Sensitivity, specificity, predictive values, classification accuracy, and Bayesian post-test probabilities were evaluated across multiple prevalence conditions alongside base rates of low scores and resource demands.
Results: Hypothesis 1 was supported: longer batteries produced higher base rates of low scores in healthy controls. However, applying actuarial and Bayesian frameworks prevented this from translating into inflated false-positive rates. Hypotheses 2 and 3 were not supported: abbreviated batteries maintained classification accuracy, discrimination (AUC), false-negative rates, and predictive values comparable to longer batteries. Adding tests yielded diminishing returns, reaching a psychometric plateau at approximately eight tests.
Conclusions: Battery length does not meaningfully alter binary impairment classification when actuarial and Bayesian rules are applied. Brief core batteries maximize efficiency for global detection, while comprehensive batteries remain best suited for detailed cognitive profiling and differential diagnosis.
(© The Author(s) 2026. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Contributed Indexing: Keywords: Bayesian method; Monte Carlo simulation; actuarial method; base rates; classification accuracy; comparing test batteries; neuropsychological assessment
Entry Date(s): Date Created: 20260929 Date Completed: 20260929 Latest Revision: 20260929
Update Code: 20260929
DOI: 10.1093/arclin/acag078
PMID: 42806366
Βάση Δεδομένων: MEDLINE
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  Data: Objective: This Monte Carlo simulation study examined whether neuropsychological test battery length impacts cognitive impairment classification accuracy using actuarial and Bayesian interpretive frameworks. Three hypotheses were tested: (a) longer batteries yield higher false-positive rates due to cumulative probability effects; (b) longer batteries show superior classification accuracy; and (c) abbreviated batteries have higher false-negative rates.<br />Method: Four simulated batteries of varying length, domain coverage, and redundancy were applied to 10,000 virtual patient profiles. Sensitivity, specificity, predictive values, classification accuracy, and Bayesian post-test probabilities were evaluated across multiple prevalence conditions alongside base rates of low scores and resource demands.<br />Results: Hypothesis 1 was supported: longer batteries produced higher base rates of low scores in healthy controls. However, applying actuarial and Bayesian frameworks prevented this from translating into inflated false-positive rates. Hypotheses 2 and 3 were not supported: abbreviated batteries maintained classification accuracy, discrimination (AUC), false-negative rates, and predictive values comparable to longer batteries. Adding tests yielded diminishing returns, reaching a psychometric plateau at approximately eight tests.<br />Conclusions: Battery length does not meaningfully alter binary impairment classification when actuarial and Bayesian rules are applied. Brief core batteries maximize efficiency for global detection, while comprehensive batteries remain best suited for detailed cognitive profiling and differential diagnosis.<br /> (© The Author(s) 2026. Published by Oxford University Press. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
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        Value: 10.1093/arclin/acag078
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        Text: English
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        Type: general
      – SubjectFull: Bayes Theorem
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      – SubjectFull: Psychometrics
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      – SubjectFull: Cognitive Dysfunction diagnosis
        Type: general
      – SubjectFull: Cognitive Dysfunction classification
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      – TitleFull: Battery length as a moderator of classification accuracy for cognitive impairment: a Monte Carlo simulation using actuarial and Bayesian methods.
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              Text: 2026 Sep 09
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              Y: 2026
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