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Development of embedded performance validity indicators in the NIH Toolbox Cognitive Battery.

Christopher Abeare1, Laszlo Erdodi1, Isabelle Messa1

  • 1Department of Psychology, University of Windsor.

Psychological Assessment
|October 29, 2020
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Summary
This summary is machine-generated.

New embedded validity indicators (EVIs) for the NIH Toolbox Cognitive Battery (NIHTB-CB) effectively identify noncredible performance. Combining traditional and novel EVIs improves accuracy in detecting invalid test results.

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Area of Science:

  • Neuroscience
  • Psychology
  • Medical Research

Background:

  • Assessing noncredible performance is crucial in mild traumatic brain injury (mTBI) evaluations.
  • The NIH Toolbox Cognitive Battery (NIHTB-CB) is a widely used cognitive assessment tool.
  • Existing methods for detecting invalid test performance require enhancement.

Purpose of the Study:

  • To develop and validate embedded validity indicators (EVIs) for the NIHTB-CB.
  • To assess the effectiveness of novel item-based EVIs compared to traditional EVIs.
  • To improve the accuracy of detecting noncredible performance in cognitive assessments.

Main Methods:

  • Developed traditional and item-based EVIs for the NIHTB-CB.
  • Used the Medical Symptom Validity Test (MSVT) as the criterion standard.
  • Collected data from 98 adults at mTBI specialty clinics in a prospective multicenter study.
  • Utilized multivariate models and signal detection analysis to evaluate EVIs.

Main Results:

  • Individual EVIs showed varied signal detection profiles.
  • Multivariate models demonstrated superior classification accuracy.
  • Combining traditional and item-based EVIs improved sensitivity (0.65–0.70) while maintaining high specificity (0.91–0.95).
  • Failing ≥3 traditional EVIs (conservative cutoff) showed good sensitivity and specificity.

Conclusions:

  • Newly developed EVIs within the NIHTB-CB can effectively discriminate between credible and noncredible performance.
  • Aggregating EVIs into validity composites enhances signal detection.
  • Item-based EVIs offer improved classification accuracy over traditional EVIs, though practical benefits require further study.
  • The developed EVIs require cross-validation before widespread clinical or research application.