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The Value of Bayes’ Theorem for Interpreting Abnormal Test Scores in Cognitively Healthy and Clinical Samples

Published online by Cambridge University Press:  18 March 2015

Brandon E. Gavett
Brandon E. Gavett*
Affiliation:
Department of Psychology, University of Colorado, Colorado Springs, Colorado
*
Correspondence and reprint requests to: Brandon E. Gavett, UCCS Department of Psychology, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918. E-mail: bgavett@uccs.edu
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Abstract

The base rates of abnormal test scores in cognitively normal samples have been a focus of recent research. The goal of the current study is to illustrate how Bayes’ theorem uses these base rates—along with the same base rates in cognitively impaired samples and prevalence rates of cognitive impairment—to yield probability values that are more useful for making judgments about the absence or presence of cognitive impairment. Correlation matrices, means, and standard deviations were obtained from the Wechsler Memory Scale –4th Edition (WMS-IV) Technical and Interpretive Manual and used in Monte Carlo simulations to estimate the base rates of abnormal test scores in the standardization and special groups (mixed clinical) samples. Bayes’ theorem was applied to these estimates to identify probabilities of normal cognition based on the number of abnormal test scores observed. Abnormal scores were common in the standardization sample (65.4% scoring below a scaled score of 7 on at least one subtest) and more common in the mixed clinical sample (85.6% scoring below a scaled score of 7 on at least one subtest). Probabilities varied according to the number of abnormal test scores, base rates of normal cognition, and cutoff scores. The results suggest that interpretation of base rates obtained from cognitively healthy samples must also account for data from cognitively impaired samples. Bayes’ theorem can help neuropsychologists answer questions about the probability that an individual examinee is cognitively healthy based on the number of abnormal test scores observed. (JINS, 2015, 21, 1–9)

Keywords

Cognition disordersNeuropsychological testsProbabilityComputer simulationDiagnosisMemory
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 3 , March 2015 , pp. 249 - 257
Copyright
Copyright © The International Neuropsychological Society 2015 

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References

Binder, L.M., Iverson, G.L., & Brooks, B.L. (2009). To err is human: “Abnormal” neuropsychological scores and variability are common in healthy adults. Archives of Clinical Neuropsychology, 24(1), 31–46.Google Scholar
Brooks, B.L. (2010). Seeing the forest for the trees: Prevalence of low scores on the Wechsler Intelligence Scale for Children, Fourth Edition (WISC-IV). Psychological Assessment, 22(3), 650–656.Google Scholar
Brooks, B.L. (2011). A study of low scores in Canadian children and adolescents on the Wechsler Intelligence Scale For Children, Fourth Edition (WISC-IV). Child Neuropsychology, 17(3), 281–289.Google Scholar
Brooks, B.L., & Iverson, G.L. (2010). Comparing actual to estimated base rates of “abnormal” scores on neuropsychological test batteries: Implications for interpretation. Archives of Clinical Neuropsychology, 25(1), 14–21.CrossRefGoogle ScholarPubMed
Brooks, B.L., Holdnack, J.A., & Iverson, G.L. (2011). Advanced clinical interpretation of the WAIS-IV and WMS-IV: Prevalence of low scores varies by level of intelligence and years of education. Assessment, 18(2), 156–167.Google Scholar
Brooks, B.L., Iverson, G.L., & Holdnack, J.A. (2013). Understanding and using multivariate base rates with the WAIS-IV/WMS-IV. In J.A. Holdnack, L. Drozdick, L.G. Weiss & G.L. Iverson (Eds.), WAIS-IV, WMS-IV, and ACS: Advanced clinical interpretation (pp. 75–102). London: Elsevier.Google Scholar
Brooks, B.L., Iverson, G.L., & White, T. (2009). Advanced interpretation of the Neuropsychological Assessment Battery with older adults: Base rate analyses, discrepancy scores, and interpreting change. Archives of Clinical Neuropsychology, 24(7), 647–657.CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., Feldman, H.H., & Holdnack, J.A. (2009). Minimizing misdiagnosis: Psychometric criteria for possible or probable memory impairment. Dementia and Geriatric Cognitive Disorders, 27(5), 439–450.Google Scholar
Brooks, B.L., Iverson, G.L., Holdnack, J.A., & Feldman, H.H. (2008). Potential for misclassification of mild cognitive impairment: A study of memory scores on the Wechsler Memory Scale-III in healthy older adults. Journal of the International Neuropsychological Society, 14(3), 463–478.CrossRefGoogle ScholarPubMed
Brooks, B.L., Iverson, G.L., & White, T. (2007). Substantial risk of “accidental MCI” in healthy older adults: Base rates of low memory scores in neuropsychological assessment. Journal of the International Neuropsychological Society, 13, 490–500.Google Scholar
Brooks, B.L., Iverson, G.L., Koushik, N.S., Mazur-Mosiewicz, A., Horton, A.M., & Reynolds, C.R. (2013). Prevalence of low scores in children and adolescents on the test of verbal conceptualization and fluency. Applied Neuropsychology: Child, 2(1), 70–77.Google Scholar
Brooks, B.L., Iverson, G.L., Lanting, S.C., Horton, A.M., & Reynolds, C.R. (2012). Improving test interpretation for detecting executive dysfunction in adults and older adults: Prevalence of low scores on the test of verbal conceptualization and fluency. Applied Neuropsychology: Adult, 19(1), 61–70. doi:10.1080/09084282.2012.651951 Google Scholar
Brooks, B.L., Iverson, G.L., Sherman, E.M.S., & Holdnack, J.A. (2009). Healthy children and adolescents obtain some low scores across a battery of memory tests. Journal of the International Neuropsychological Society, 15(4), 613–617.Google Scholar
Brooks, B.L., Sherman, E.M.S., & Iverson, G.L. (2010). Healthy children get low scores too: Prevalence of low scores on the NEPSY-II in preschoolers, children, and adolescents. Archives of Clinical Neuropsychology, 25(3), 182–190.Google Scholar
Brooks, B.L., Strauss, E., Sherman, E.M.S., Iverson, G.L., & Slick, D.J. (2009). Developments in neuropsychological assessment: Refining psychometric and clinical interpretive methods. Canadian Psychology, 50(3), 196–209.Google Scholar
Crawford, J.R., Garthwaite, P.H., & Gault, C.B. (2007). Estimating the percentage of the population with abnormally low scores (or abnormally large score differences) on standardized neuropsychological test batteries: A generic method with applications. Neuropsychology, 21(4), 419–430.Google Scholar
Decker, S.L., Schneider, W.J., & Hale, J.B. (2012). Estimating base rates of impairment in neuropsychological test batteries: A comparison of quantitative models. Archives of Clinical Neuropsychology, 27(1), 69–84.Google Scholar
Detterbeck, F.C., Mazzone, P.J., Naidich, D.P., & Bach, P.B. (2013). Screening for lung cancer: Diagnosis and management of lung cancer, 3rd ed: American college of chest physicians evidence-based clinical practice guidelines. Chest, 143, e78S–e92S.Google Scholar
Friendly, M. (2002). Corrgrams: Exploratory displays for correlation matrices. The American Statistician, 56(4), 316–324.Google Scholar
Gifford, K.A., Liu, D., Lu, Z., Tripodis, Y., Cantwell, N.G., Palmisano, J., & Jefferson, A.L. (2014). The source of cognitive complaints predicts diagnostic conversion differentially among nondemented older adults. Alzheimer’s & Dementia, 10(3), 319–327.CrossRefGoogle ScholarPubMed
Gouvier, W.D. (2001). Are you sure you’re really telling the truth? Neurorehabilitation, 16, 215–219.CrossRefGoogle ScholarPubMed
Gunner, J.H., Miele, A.S., Lynch, J.K., & McCaffrey, R.J. (2012). Performance of non-neurological older adults on the Wisconsin Card Sorting Test and the Stroop Color-Word Test: Normal variability or cognitive impairment? Archives of Clinical Neuropsychology, 27(4), 398–405.Google Scholar
Katz, A.R., Effler, P.V., Ohye, R.G., Brouillet, B., Lee, M.V.C., & Whiticar, P.M. (2004). False-positive gonorrhea test results with a nucleic acid amplification test: The impact of low prevalence on positive predictive value. Clinical Infectious Diseases, 38, 814–819.Google Scholar
Lonie, J.A., Herrmann, L.L., Donaghey, C.L., Ebmeier, K.P., Lonie, J.A., Herrmann, L.L., & Ebmeier, K.P. (2008). Clinical referral patterns and cognitive profile in mild cognitive impairment. The British Journal of Psychiatry, 192, 59–64.Google Scholar
Nugent, W.R. (2004). The role of prevalence rates, sensitivity, and specificity in assessment accuracy: Rolling the dice in social work process. Journal of Social Service Research, 31(2), 51–75.Google Scholar
O’Bryant, S.E., & Lucas, J.A. (2006). Estimating the predictive value of the Test of Memory Malingering: An illustrative example for clinicians. The Clinical Neuropsychologist, 20(3), 533–540.Google Scholar
Palmer, B.W., Boone, K.B., Lesser, I.M., & Wohl, M.A. (1998). Base rates of “impaired” neuropsychological test performance among healthy older adults. Archives of Clinical Neuropsychology, 13(6), 503–511.Google Scholar
R Core Team. (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from http://www.r-project.org/ Google Scholar
Rabin, L.A., Barr, W.B., & Burton, L.A. (2005). Assessment practices of clinical neuropsychologists in the United States and Canada: A survey of INS, NAN, and APA Division 40 members. Archives of Clinical Neuropsychology, 20(1), 33–65.CrossRefGoogle Scholar
Schretlen, D.J., Munro, C.A., Anthony, J.C., & Pearlson, G.D. (2003). Examining the range of normal intraindividual variability in neuropsychological test performance. Journal of the International Neuropsychological Society, 9(6), 864–870.Google Scholar
Schretlen, D.J., Testa, S.M., & Pearlson, G.D. (2010). The Calibrated Neuropsychological Normative System. Lutz, FL: Psychological Assessment Resources.Google Scholar
Schretlen, D.J., Testa, S.M., Winicki, J.M., Pearlson, G.D., & Gordon, B. (2008). Frequency and bases of abnormal performance by healthy adults on neuropsychological testing. Journal of the International Neuropsychological Society, 14(3), 436–445.CrossRefGoogle ScholarPubMed
Stern, R.A., & White, T. (2003). Neuropsychological assessment battery. Lutz, FL: Psychological Assessment Resources.Google Scholar
U.S. Preventive Services Task Force. (2009). Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Annals of Internal Medicine, 151, 716–726.Google Scholar
Wechsler, D. (2009). WMS-IV Technical and Interpretive Manual. San Antonio, TX: Pearson Assessment.Google Scholar