Advertisement
Research Article| Volume 27, ISSUE 9, P990-1006, May 01, 1990

Optimizing REM latency as a diagnostic test for depression using receiver operating characteristic analysis and information theory

  • Eugene Somoza
    Correspondence
    Address reprint requests to Eugene Somoza, M.D., Ph.D., Director, Psychiatric Evaluation Center 116A, Veterans Administration Medical Center, 3200 Vine Street, Cincinnati, Ohio 45220, USA.
    Footnotes
    Affiliations
    Psyciatry Service, Veterans Administration Medical Center, Cincinnati, USA

    Division of Neuroscience, Psychiatry Department, University of Cincinnati Medical Center, Cincinnati, Ohio, USA

    Millcreek Psychiatric Center for Children, Paddock Road, Cincinnati, USA

    Department of Psychiatry, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
    Search for articles by this author
  • Douglas Mossman
    Footnotes
    Affiliations
    Psyciatry Service, Veterans Administration Medical Center, Cincinnati, USA

    Division of Neuroscience, Psychiatry Department, University of Cincinnati Medical Center, Cincinnati, Ohio, USA

    Millcreek Psychiatric Center for Children, Paddock Road, Cincinnati, USA

    Department of Psychiatry, University of Cincinnati Medical Center, Cincinnati, Ohio, USA
    Search for articles by this author
  • Author Footnotes
    1 The authors thank Mr. Luis Soutullo-Esperson for his excellent work in the production of the computer-generated figures.
DOI:https://doi.org/10.1016/0006-3223(90)90036-2
Optimizing REM latency as a diagnostic test for depression using receiver operating characteristic analysis and information theory
Previous ArticleEye movements in acute, chronic, and remitted schizophrenics
Next ArticleHormonal circadian rhythms in eating disorders
      This paper is only available as a PDF. To read, Please Download here.

      Abstract

      New diagnostic techniques must be evaluated for their intrinsic accuracy and for their applicability to particular patient groups in specific clinical settings. Using receiver operating characteristics (ROC) analysis and concepts from information theory, we have developed a new mathematical and graphical method that can evaluate, compare, and optimize the performance of diagnostic tests for any value of disorder prevalence. Our analytic method is appropriate to any test that sorts disordered from nondisordered subjects using a continuous or nonbinary diagnostic variable; its characterization of the fundamental properties of such tests thus has important implications for the evaluation and optimization of diagnostic modalities used by clinicians in all medical specialties. We demonstrate our method using published data from five studies that used sleep architecture as a “biological marker” for depression. Our analysis confirms that REM latency is comparable to the dexamethasone suppression test in its ability to discriminate depressed from control subjects. For each of the five studies, we show how optimal REM latency cut-off times may be selected so that diagnostic information yield is maximized, and we compare the ability of each study to detect depressed subjects in populations where the prevalence of affective disorder can be specified.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Biological Psychiatry
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Register: Create an account
      Institutional Access: Sign in to ScienceDirect

      References

        • Akiskal H.S.
        • Lemmi H.
        • Yerevanian B.
        • King D.
        • Belluomini J.
        The utility of the REM latency test in psychiatric diagnosis: A study of 81 depressed outpatients.
        Psychiatry Res. 1982; 7: 101-110
        • American Psychiatric Association
        Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association, Washington1980
        • Ansseau M.
        • Kupfer D.J.
        • Reynolds III, C.F.
        Internight variability of REM latency in major depression: Implications for the use of REM latency as a biological correlate.
        Biol Psychiatry. 1985; 20: 489-505
        • Ansseau M.
        • Kupfer D.J.
        • Reynolds III, C.F.
        • Coble P.A.
        “Paradoxical” shortening of REM latency during the first night in major depressive disorder: Clinical and polysemnographic correlates.
        Biol Psychiatry. 1985; 20: 135-145
        • Berger M.
        • Doerr P.
        • Lund R.
        • Bronish T.
        • von Zerssen D.
        Neuroendocrinological and neurophysiological studies in major depressive disorders: Are there biological markers for the endogenous subtype?.
        Biol Psychiatry. 1982; 17: 1217-1242
        • Carroll B.J.
        • Feinberg M.
        • Greden J.P.
        • et al.
        A specific laboratory test for the diagnosis of melancholia: Standardization, validation and clinical utility.
        Arch Gen Psychiatry. 1981; 38: 15-22
        • Diamond G.A.
        • Hirsh M.
        • Forrester J.S.
        • et al.
        Application of information theory to clinical diagnostic testing.
        Circulation. 1981; 63: 915-921
        • Feinberg M.
        • Gillin J.C.
        • Carroll B.J.
        • Greden J.F.
        • Zis A.P.
        EEG studies of sleep in the diagnosis of depression.
        Biol Psychiatry. 1982; 17: 305-316
        • Gillin J.C.
        • Borbely A.A.
        Sleep: A neurobiological window on affective disorders.
        Trends Neurosci. 1985; 8: 537-542
        • Gillin J.C.
        • Duncan W.C.
        • Murphy D.L.
        • et al.
        Age-related changes in sleep in depressed and normal subjects.
        Psychiatry Res. 1981; 4: 73-78
        • Hanley J.A.
        • McNeil B.J.
        The meaning and use of the area under a receiver operating characteristic (ROC) curve.
        Radiology. 1982; 143: 29-36
        • Hanley J.A.
        • McNeil B.J.
        A method for comparing areas under receiver operating characteristic curves derived from the same cases.
        Radiology. 1983; 148: 839
        • Khinchin A.I.
        Mathematical Foundations of Information Theory.
        Dover Publications, New York1957
        • Kraemer H.C.
        The methodological and statistical evaluation of medical tests: The dexamethasone suppression test in psychiatry.
        Psychoneuroendocrinology. 1987; 12: 411-427
        • Kraemer H.C.
        Assessment of 2 × 2 associations: Generalization of signal detection methodology.
        Am Statistician. 1988; 42: 27-49
        • Kupfer D.J.
        REM latency, a psychobiological marker for primary depressive disease.
        Biol Psychiatry. 1976; 11: 159-174
        • Kupfer D.J.
        • Reynolds III, C.J.
        • Ulrich R.F.
        • Shaw D.H.
        • Coble P.A.
        EEG sleep, depression, and aging.
        Neurobiol Aging. 1982; 3: 351-380
        • McCracken J.T.
        • Rubin R.T.
        • Poland R.E.
        Neuroendocrine aspects of primary endogenous depression. VI. Receiver operating characteristic analysis of the cortisol suppression index versus the dexamethasone suppression test in patients and matched controls.
        Psychiatry Res. 1988; 26: 69-78
        • McNeil B.J.
        • Hanley J.A.
        Statistical approaches to the analysis of receiver operating characteristic (ROC) curves.
        Med Decis Making. 1984; 4: 137-150
        • McNeil B.J.
        • Keeler E.
        • Adelstein S.J.
        Primer on certain elements of medical decision making.
        N Engl J Med. 1975; 293: 211-215
        • McNicol D.
        A Primer of Signal Detection Theory.
        Australasian Publishing Company, London1972
        • Mendlewicz J.
        • Kerhofs M.
        • Hoffmann G.
        • Linkowski P.
        Dexamethasone suppression test and REM sleep in patients with major depressive disorder.
        Br J Psychiatry. 1984; 145: 383-388
        • Metz C.E.
        Basic principles of ROC analysis.
        Semin Nucl Med. 1978; 8: 283-298
        • Metz C.E.
        ROC methodology in radiologic imaging.
        Invest Radiol. 1986; 21: 720-733

      25. Metz CE (in press): Some practical issues of experimental design and data analysis in radiological ROC studies. Invest Radiol.

        • Metz C.E.
        • Goodenough D.J.
        • Rossmann K.
        Evaluation of receiver operating characteristic curve data in terms of information theory with applications in radiography.
        Radiology. 1973; 109: 297-304
        • Metz C.E.
        • Kronman H.B.
        Statistical significance tests for binormal ROC curves.
        J Math Psych. 1980; 22: 218-243
        • Mossman D.
        • Somoza E.
        Assessing improvements in the dexamethasone suppression test using receiver operating characteristic analysis.
        Biol Psychiatry. 1989; 25: 159-173
        • Mossman D.
        • Somoza E.
        Maximizing diagnostic information from the dexamethasone suppression test: An approach to criterion selection using receiver operating characteristic analysis.
        Arch Gen Psychiatry. 1989; 45: 653-660
        • Murphy J.M.
        • Berwick D.M.
        • Weinstein M.C.
        • Borus J.F.
        • Budman S.H.
        • Klerman G.L.
        Performance of screening and diagnostic tests: Application of receiver operating characteristic analysis.
        Arch Gen Psychiatry. 1987; 44: 550-555
        • Pierce J.R.
        An Introduction To Information Theory: Symbols, Signals and Noise.
        Dover Publications, New York1980
        • Rush A.J.
        • Erman M.K.
        • Giles D.E.
        • et al.
        Polysomnographic findings in recently drug-free and clinically remitted depressed patients.
        Arch Gen Psychiatry. 1986; 43: 878-884
        • Rush A.J.
        • Giles D.E.
        • Roffwarg H.P.
        • Parker C.R.
        Sleep EEG and dexamethasone suppression test findings in outpatients with unipolar major depressive disorders.
        Biol Psychiatry. 1982; 17: 327-341
        • Shannon C.E.
        • Weaver W.
        The Mathematical Theory of Communication.
        ed 2. University of Illinois Press, Chicago1963
        • Sondik E.J.
        Clinical evaluation of test strategies.
        Clin Lab Med. 1982; 2: 821-833
        • Swets J.A.
        ROC analysis applied to the evaluation of medical imaging techniques.
        Invest Radiol. 1979; 14: 109-121
        • Swets J.A.
        Forms of empirical ROCs in discrimination and diagnostic tasks: Implication for theory and measurement performance.
        Psychol Bull. 1986; 99: 181-198
        • Swets J.A.
        Indices of discrimination or diagnostic accuracy: Their ROCs and implied models.
        Psychol Bull. 1986; 99: 100-117
        • Swets J.A.
        Measuring the accuracy of diagnostic systems.
        Science. 1988; 240: 1285-1293
        • Swets J.A.
        • Pickett R.M.
        Evaluation of Diagnostic Techniques: Methods from Signal Detection Theory.
        Academic Press, New York1982
        • Weinstein M.C.
        • Stason W.B.
        Hypertension, a Policy Perspective.
        Harvard University Press, Cambridge1976
        • Zimmerman M.
        • Coryell W.
        • Pfohl B.
        • Stangl B.
        The validity of four definitions of endogenous depression. II. Clinical, demographic, familial, and psychosocial correlates.
        Arch Gen Psychiatry. 1986; 43: 234-244

      Article info

      Publication history

      Received in revised form: March 29, 1989
      Received: January 19, 1989

      Identification

      DOI: https://doi.org/10.1016/0006-3223(90)90036-2

      Copyright

      © 1990 Published by Elsevier Inc.

      ScienceDirect

      Access this article on ScienceDirect
      We use cookies to help provide and enhance our service and tailor content. To update your cookie settings, please visit the for this site.
      Copyright © 2023 Elsevier Inc. except certain content provided by third parties. The content on this site is intended for healthcare professionals.


      RELX