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The Value of Bayesian Methods for Accurate and Efficient Neuropsychological Assessment

Published online by Cambridge University Press:  19 October 2021

Hanne Huygelier Open the ORCID record for Hanne Huygelier [Opens in a new window] ,
Céline R. Gillebert  and
Pieter Moors
Hanne Huygelier*
Affiliation:
Department of Brain & Cognition, KU Leuven, Leuven, Belgium
Céline R. Gillebert
Affiliation:
Department of Brain & Cognition, KU Leuven, Leuven, Belgium
Pieter Moors
Affiliation:
Department of Brain & Cognition, KU Leuven, Leuven, Belgium
*
*Correspondence and reprint requests to: Hanne Huygelier, Tiensestraat 102 box 3711, 3000 Leuven, Belgium. Email: [email protected]
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Abstract

Objective:

Clinical neuropsychology has been slow in adopting novelties in psychometrics, statistics, and technology. Researchers have indicated that the stationary nature of clinical neuropsychology endangers its evidence-based character. In addition to a technological crisis, there may be a statistical crisis affecting clinical neuropsychology. That is, the frequentist null hypothesis significance testing framework remains the dominant approach in clinical practice, despite a recent surge in critique on this framework. While the Bayesian framework has been put forward as a viable alternative in psychology in general, the possibilities it offers to clinical neuropsychology have not received much attention.

Method:

In the current position paper, we discuss and reflect on the value of Bayesian methods for the advancement of evidence-based clinical neuropsychology.

Results:

We aim to familiarize clinical neuropsychologists and neuropsychological researchers to Bayesian methods of inference and provide a clear rationale for why these methods are valuable for clinical neuropsychology.

Conclusion:

We argue that Bayesian methods allow for a more intuitive answer to our diagnostic questions and form a more solid foundation for sequential and adaptive diagnostic testing, representing uncertainty about patients’ observed test scores and cognitive modeling of test results.

Keywords

PsychometricsEvidence-basedAssessmentBayesianBase rateDiagnostic Accuracy
Type
Critical Review
Information
Journal of the International Neuropsychological Society , Volume 28 , Issue 9 , October 2022 , pp. 984 - 995
Copyright
Copyright © INS. Published by Cambridge University Press, 2021

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