Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-26T20:03:17.109Z Has data issue: false hasContentIssue false
  • English
  • Français

The utility of regression-based norms in interpreting the minimal assessment of cognitive function in multiple sclerosis (MACFIMS)

Published online by Cambridge University Press:  02 October 2009

BRETT A. PARMENTER ,
S. MARC TESTA ,
DAVID J. SCHRETLEN ,
BIANCA WEINSTOCK-GUTTMAN  and
RALPH H. B. BENEDICT
BRETT A. PARMENTER
Affiliation:
Department of Psychology, Western State Hospital, Tacoma, Washington
S. MARC TESTA
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
DAVID J. SCHRETLEN
Affiliation:
Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
BIANCA WEINSTOCK-GUTTMAN
Affiliation:
Department of Neurology, Jacobs Neurological Institute, State University of New York at Buffalo, School of Medicine and Biomedical Sciences, Buffalo, New York
RALPH H. B. BENEDICT*
Affiliation:
Department of Neurology, Jacobs Neurological Institute, State University of New York at Buffalo, School of Medicine and Biomedical Sciences, Buffalo, New York
*
*Correspondence and reprint requests to: Ralph H. B. Benedict, Ph.D., Department of Neurology, 100 High Street (D-6), Buffalo, New York 14203. E-mail: benedict@buffalo.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

The Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS) is a consensus neuropsychological battery with established reliability and validity. One of the difficulties in implementing the MACFIMS in clinical settings is the reliance on manualized norms from disparate sources. In this study, we derived regression-based norms for the MACFIMS, using a unique data set to control for standard demographic variables (i.e., age, age2, sex, education). Multiple sclerosis (MS) patients (n = 395) and healthy volunteers (n = 100) did not differ in age, level of education, sex, or race. Multiple regression analyses were conducted on the performance of the healthy adults, and the resulting models were used to predict MS performance on the MACFIMS battery. This regression-based approach identified higher rates of impairment than manualized norms for many of the MACFIMS measures. These findings suggest that there are advantages to developing new norms from a single sample using the regression-based approach. We conclude that the regression-based norms presented here provide a valid alternative to identifying cognitive impairment as measured by the MACFIMS. (JINS, 2010, 16, 6–16.)

Keywords

Standard scoresImmunologic diseaseNeuropsychologyNormalizationpsychometricsBrain
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 16 , Issue 1 , January 2010 , pp. 6 - 16
Copyright
Copyright © The International Neuropsychological Society 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Arnett, P.A., Higginson, C.I., Voss, W.D., Bender, W.I., Wurst, J.M., & Tippin, J.M. (1999a). Depression in multiple sclerosis: Relationship to working memory capacity. Neuropsychology, 13(4), 546–56.CrossRefGoogle ScholarPubMed
Arnett, P.A., Higginson, C.I., Voss, W.D., Wright, B., Bender, W.I., Wurst, J.M., & Tippin, J.M. (1999b). Depressed mood in multiple sclerosis: Relationship to capacity-demanding memory and attentional functioning. Neuropsychology, 13(3), 434–446.CrossRefGoogle ScholarPubMed
Arnett, P.A., Smith, M.M., Barwick, F.H., Benedict, R.H.B., & Ahlstrom, B.P. (2008). Oralmotor slowing in multiple sclerosis: Relationship to neuropsychological tasks requiring an oral response. Journal of the International Neuropsychological Society, 14, 454–462.CrossRefGoogle ScholarPubMed
Beatty, W.W., & Monson, N. (1996). Problem solving by patients with multiple sclerosis: Comparison of performance on the Wisconsin and California Card Sorting tests. Journal of the International Neuropsychological Society, 2, 134–140.CrossRefGoogle ScholarPubMed
Beck, A.T., Steer, R.A., & Brown, G.K. (2000). BDI-Fast Screen for Medical Patients: Manual. San Antonio, TX: Psychological Corporation.Google Scholar
Benedict, R.H.B. (1997). Brief Visuospatial Memory Test – Revised: Professional Manual. Odessa, Florida: Psychological Assessment Resources, Inc.Google Scholar
Benedict, R.H.B., Cookfair, D., Gavett, R., Gunther, M., Munschauer, F., Garg, N., & Weinstock-Guttman, B. (2006). Validity of the minimal assessment of cognitive function in multiple sclerosis (MACFIMS). Journal of the International Neuropsychological Society, 12, 549–558.CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Fischer, J.S., Archibald, C.J., Arnett, P.A., Beatty, W.W., Bobholz, J., et al. (2002). Minimal neuropsychological assessment of MS patients: A consensus approach. The Clinical Neuropsychologist, 16(3), 381–397.CrossRefGoogle ScholarPubMed
Benedict, R.H.B., Fishman, I., McClellan, M.M., Bakshi, R., & Weinstock-Guttman, B. (2003). Validity of the Beck Depression Inventory - Fast Screen in multiple sclerosis. Multiple Sclerosis, 9, 393–396.CrossRefGoogle ScholarPubMed
Benton, A.L., Sivan, A.B., Hamsher, K., Varney, N.R., & Spreen, O. (1994). Contributions to Neuropsychological Assessment. Second ed. New York: Oxford University Press.Google Scholar
Crawford, J.R., & Allan, K.M. (1997). Estimating premorbid WAIS-R IQ with demographic variables: Regression equations derived from a UK sample. The Clinical Neuropsychologist, 11(2), 192–197.CrossRefGoogle Scholar
Crawford, J.R., & Howell, D.C. (1998). Regression equations in clinical neuropsychology: An evaluation of statistical methods for comparing predicted and obtained scores. Journal of Clinical and Experimental Neuropsychology, 20(5), 755–762.CrossRefGoogle ScholarPubMed
Delis, D.C., Kramer, J.H., Kaplan, E., & Ober, B.A. (2000). California Verbal Learning Test Manual: Second Edition, Adult Version. San Antonio, TX: Psychological Corporation.Google Scholar
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis-Kaplan Executive Function System. San Antonio, Texas: Psychological Corporation.Google Scholar
Fastenau, P.S. (1998). Validity of regression-based norms: An empirical test of the comprehensive norms with older adults. Journal of Clinical and Experimental Neuropsychology, 20(6), 906–916.CrossRefGoogle ScholarPubMed
Feinstein, A. (2006). Mood disorders in multiple sclerosis and the effects on cognition. Journal of the Neurological Sciences, 245, 63–66.CrossRefGoogle ScholarPubMed
Feinstein, A., Roy, P., Lobaugh, N., Feinstein, K., O’Connor, P., & Black, S. (2004). Structural brain abnormalities in multiple sclerosis patients with major depression. Neurology, 62, 586–590.CrossRefGoogle ScholarPubMed
Fischer, J.S., Foley, F.W., Aikens, J.E., Ericson, G.D., Rao, S.M., & Shindell, S. (1994). What do we really know about cognitive dysfunction, affective disorders, and stress in multiple slcerosis? A practitioner’s guide. Journal of Neurologic Rehabilitation, 8, 151–164.Google Scholar
Heaton, R.K., Avitable, N., Grant, I., & Matthews, C.G. (1999). Further cross validation of regression-based neuropsychological norms with an update for the Boston Naming Test. Journal of Clinical and Experimental Neuropsychology, 21(4), 572–582.CrossRefGoogle Scholar
Heaton, R.K., Ryan, L., Grant, I., & Matthews, C.G. (1996). Demographic influences on neuropsychological test performance. In Grant, I. & Adams, K. (Eds.), Neuropsychological assessment of neuropsychiatric disorders. New York: Oxford University Press.Google Scholar
Kent, R.D., Kent, J.F., & Rosenbek, J.C. (1987). Maximum performance tests of speech production. Journal of Speech and Hearing Disorders, 52, 367–387.CrossRefGoogle ScholarPubMed
Klein, C., Forester, F., & Hartnegg, K. (2007). Regression-based developmental models exemplified for Wisconsin Card Sorting Test parameters: Statistics and software for individual predictions. Journal of Clinical and Experimental Neuropsychology, 29(1), 25–35.CrossRefGoogle ScholarPubMed
Kurtzke, J.F. (1983). Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Annals of Neurology, 13, 227–231.Google Scholar
Leckliter, I.N., & Matarazzo, J.D. (1989). The influence of age, education, IQ, gender, and alcohol abuse on Halstead-Reitan neuropsychological test battery performance. Journal of Clinical Psychology, 45(4), 484–512.3.0.CO;2-L>CrossRefGoogle ScholarPubMed
Lublin, F.D., & Reingold, S.C. (1996). Defining the clinical course of multiple sclerosis: results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. [see comment]. Neurology, 46, 907–911.CrossRefGoogle ScholarPubMed
Mathiowetz, V., Weber, K., Kashman, N., & Volland, G. (1985). Adult norms for the nine hole peg test of finger dexterity. The Occupational Therapy Journal of Research, 5, 24–37.CrossRefGoogle Scholar
McDonald, W.I., Compston, A., Edan, G., Goodkin, D.E., Hartung, H., Lublin, F., McFarland, H.F., Paty, D.W., Polman, C.H., Reingold, S.C., Sandberg-Wollheim, M., Sibley, W.A., Thompson, A., van der Noort, S., Weinshenker, B.Y., & Wolinsky, J.S. (2001). Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology, 50, 121–127.CrossRefGoogle ScholarPubMed
Parmenter, B., Zivadinov, R., Kerenyi, L., Gavett, R., Weinstock-Guttman, B., Dwyer, M., et al. (2007). Validity of the Wisconsin Card Sorting and Delis-Kaplan Executive Function System (DKEFS) sorting tests in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 29, 215–223.CrossRefGoogle ScholarPubMed
Peyser, J.M., Edwards, K.R., Poser, C.M., Filskov, S.B. (1980). Cognitive function in patients with multiple sclerosis Archives of Neurology 37 577–579.CrossRefGoogle ScholarPubMed
Polman, C.H., Reingold, S.C., Edan, G., Filippi, M., Hartung, H.P., Kappos, L., et al. (2005). Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria.” Annals of Neurology, 58, 840–846.CrossRefGoogle Scholar
Rao, S.M. (1991). A manual for the brief repeatable battery of neuropsychological tests in multiple sclerosis. New York: National MS Society.Google Scholar
Rao, S.M., Leo, G.J., Bernardin, L., & Unverzagt, F. (1991). Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology, 41, 685–691.CrossRefGoogle ScholarPubMed
Reitan, R.M., & Wolfson, D. (1995). Influence of age and education on neuropsychological test results. The Clinical Neuropsychologist, 9(2), 151–158.CrossRefGoogle Scholar
Reitan, R.M. & Wolfson, D. (1997). The influence of age and education on neuropsychological performances of persons with mild head injuries. Applied Neuropsychology, 4(1), 16–33.CrossRefGoogle ScholarPubMed
Schretlen, D.J., Cascella, N.G., Meyer, S.M., Kingery, L.R., Testa, S.M., Munro, C.A., Pulver, A.E., Rivkin, P., Rao, V.A., Diaz-Asper, C.M., Dickerson, F.B., Yolken, R.H., & Pearlson, G.D. (2007). Neuropsychological functioning in bipolar disorder and schizophrenia. Biol Psychiatry, 62, 179–186.CrossRefGoogle Scholar
Sherrill-Pattison, S., Donders, J., & Thompson, E. (2000). Influence of demographic variables on neuropsychological test performance after traumatic brain injury. The Clinical Neuropsychologist, 14(4), 496–503.CrossRefGoogle ScholarPubMed
Shuttleworth-Jordan, A.B. (1997). Age and education effects on brain-damaged subjects: “Negative” findings revisited. The Clinical Neuropsychologist, 11(2), 205–209.CrossRefGoogle Scholar
Silverberg, N.D., & Millis, S.R. (2009). Impairment versus deficiency in neuropsychological assessment: Implications for ecological validity. Journal of the International Neuropsychological Society, 15, 94–102.CrossRefGoogle ScholarPubMed
Smith, A. (1982). Symbol digit modalities test: Manual. Los Angeles: Western Psychological Services.Google Scholar
Testa, S.M., Winicki, J. M, Pearlson, G.D., Gordon, B., & Schretlen, D.J. (submitted). Accounting for estimated IQ in neuropsychological test performance with regression-based norms.Google Scholar
Thornton, A., & Raz, N. (1997). Memory impairment in multiple sclerosis: A quantitative review. Neuropsychology, 11(3), 357–366.CrossRefGoogle ScholarPubMed
Van Breukelen, G.J.P., & Vlaeyen, J.W.S. (2005). Norming clinical questionnaires with multiple regression: The Pain Cognition List. Psychological Assessment, 17(3), 336–344.CrossRefGoogle ScholarPubMed
Van der Elst, W., Van Boxtel, M.P.J., Van Breukelen, G.J.P., & Jolles, J. (2005). Rey’s verbal learning test: Normative data for 1855 healthy participants aged 24–81 years and the influence of age, sex, education, and mode of transportation. Journal of the International Neuropsychological Society, 11, 290–302.CrossRefGoogle Scholar
Van der Elst, W., Van Boxtel, M.P.J., Van Breukelen, G.J.P., & Jolles, J. (2006a). The Stroop Color-Word Test: Influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment, 13(1), 62–79.CrossRefGoogle ScholarPubMed
Van der Elst, W., Van Boxtel, M.P.J., Van Breukelen, G.J.P., & Jolles, J. (2006b). The Concept Shifting Test: Adult normative data. Psychological Assessment, 18(4), 424–432.CrossRefGoogle ScholarPubMed
Vanderploeg, R.D., Axelrod, B.N., Sherer, M., Scott, J., & Adams, R.L. (1997). The importance of demographic adjustments on neuropsychological test performance: A response to Reitan and Wolfson (1995). The Clinical Neuropsychologist, 11(2), 211–217.CrossRefGoogle Scholar
Zachary, R.A., & Gorsuch, R.L. (1985). Continuous norming: Implications for the WAIS-R. Journal of Clinical Psychology, 41(1), 86–94.3.0.CO;2-W>CrossRefGoogle ScholarPubMed