Psychother Psychosom Med Psychol 2013; 63(01): 48-54
DOI: 10.1055/s-0032-1329976
Übersicht
© Georg Thieme Verlag KG Stuttgart · New York

Computer Adaptive Tests in der Medizin

Computer Adaptive Tests in Medicine
Matthias Rose
1   Psychosomatische Medizin und Psychotherapie, Charité-Universitätsmedizin Berlin, Berlin
,
Inka Wahl
2   Institut und Poliklinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Hamburg-Eppendorf und Schön Klinik Hamburg Eilbek, Hamburg
,
Bernd Löwe
3   Universitäre Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Hamburg-Eppendorf und Schön Klinik Hamburg-Eilbek, Hamburg
› Author Affiliations
Further Information

Publication History

eingereicht 17 June 2012

akzeptiert 16 October 2012

Publication Date:
22 January 2013 (online)

Zusammenfassung

Die Methodik zur Messung psychischer Variablen steht in der Medizin allgemein, aber auch in der psychosomatischen Medizin weiter deutlich hinter der der Erfassung biomedizinischer Parameter zurück. Für wichtige Konstrukte existieren meist mehrere Instrumente, deren Ergebnisse nur schwer vergleichbar sind. Viele Fragebögen sind zudem entweder zu lang oder zu ungenau für den Einsatz in der klinischen Routine. Moderne psychometrische Methoden, wie die Entwicklung von Item Banken und Computer Adaptiver Tests (CAT) auf Grundlage der Item Response Theory (IRT), versprechen einige dieser Probleme zu lösen. Simulationsstudien zeigen, dass CATs mit einer geringeren Itemanzahl eine höhere Messpräzision über einen größeren Messbereich erreichen als statische Fragebögen. Untersuchungen mit realen CAT Anwendungen bestätigen diese Befunde, jedoch existieren bislang kaum longitudinale Untersuchungen. Die Skalierung etablierter Fragebögen auf einer gemeinsamen IRT-basierten Metrik stellt eine weitere vielversprechende Option der Nutzung der Item Response Theory dar und einen möglichen Schritt hin zu einer Standardisierung der Messung psychischer Parameter.

Abstract

Measurement of Patient-reported Outcomes (PRO) still lacks behind clinical standards. Most established tools are also either too burdensome or too imprecise to be used in clinical practice. Item Response Theory (IRT) methods and Computer Adaptive Tests (CAT) promise to overcome these shortcomings. Simulation studies have shown that individually tailored CATs can provide more precise and less burdensome measurements over a larger measurement range than static tools. Several studies with real CAT application have supported the psychometric superiority of CATs, but results from longitudinal studies are still scarce. IRT item banks also allow scoring different established tools measuring the same construct on one common metric, which could greatly facilitate the harmonization of PRO-assessments.

 
  • Literatur

  • 1 Weizsäcker Vv. Über Psychosomatische Medizin. In: Weizsäcker Vv. (ed.). Gesammelte Schriften Bd.6. Körpergeschehen und Neurose. Psychosomatische Medizin. Frankfurt/M. Suhrkamp; 1986: 517-521
  • 2 Engel GL. The need for a new medical model: a challenge for biomedicine. Science 1977; 196: 129-136
  • 3 Bantel H, Bahr MJ, Schulze-Osthoff K. An apoptosis biomarker for prediction of nonalcoholic steatohepatitis. Hepatology 2009; 50: 991
  • 4 Golub TR, Slonim DK, Tamayo P et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 15; 286: 531-537
  • 5 Meigs JB. Multiple biomarker prediction of type 2 diabetes. Diabetes Care 2009; 32: 1346-1348
  • 6 Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 2011; 378: 1812-1823
  • 7 Volpe M, Francia P, Tocci G et al. Prediction of long-term survival in chronic heart failure by multiple biomarker assessment: a 15-year prospective follow-up study. Clin Cardiol 2010; 33: 700-707
  • 8 The Cochrane Collaboration Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1 [updated Septeber 2008]. 2008
  • 9 US Food and Drug Administration. Guidance for Industry. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. U.S. Department of Health and Human Services Food and Drug Administration; 2006
  • 10 McHorney CA, Tarlov AR. Individual-patient monitoring in clinical practice: are available health status surveys adequate?. Qual Life Res 1995; 4: 293-307
  • 11 Ware Jr JE, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992; 30: 473-483
  • 12 Wahl I, Meyer B, Löwe B et al. Erfassung der Lebensqualität in der Psychotherapie Forschung. Klinische Diagnostik und Evaluation 2010; 1: 4-21
  • 13 Institute for Quality and Efficiency in Health Care IQWIG. Allgemeine Methoden. Version 3.0 vom 27.05.2008
  • 14 DeMars C. Item Response Theory. New York: Oxford University Press Inc; 2010
  • 15 Embretson SE, Reise SP. Item Response Theory for Psychologists. London: Lawrence Erlbaum Associates; 2000
  • 16 Hambleton R, Swaminathan H. Item Response Theory: Principles and Applications. Boston/Dordrecht/Lancaster: Kluwer Nijhoff Publishing; 2010
  • 17 van der Linden W, Hambleton RK. Handbook of Modern Item Response Theory. New York: Springer; 2010
  • 18 van der Linden WJ, Glas CAW. Computerized Adaptive Testing: Theory and Practice. Dordrecht: Kluwer Academic Publishers; 2000
  • 19 Ellert U, Lampert T, Ravens-Sieberer U. Measuring health-related quality of life with the SF-8. Normal sample of the German population. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2005; 48: 1330-1337
  • 20 Muraki E. A Generalized Partial Credit Model. In: van der Linden WJ, Hambleton RK. (ed.). Handbook of Modern Item Response Theory. Berlin: Springer; 1997: 153-164
  • 21 Reeve BB, Hays RD, Bjorner JB et al. Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care 2007; 45 (Suppl. 01) S22-S31
  • 22 Rose M, Bjorner JB, Becker J et al. Evaluation of a preliminary physical function item bank supported the expected advantages of the Patient-Reported Outcomes Measurement Information System (PROMIS). J Clin Epidemiol 2008; 61: 17-33
  • 23 Bjorner JB, Smith K, Stone C et al. IRTFIT: A Macro for Item Fit and Local Dependence Tests under IRT Models. http://outcomescancer gov/areas/measurement/irtfit_macro_users_guide pdf 2007 Available at: URL: http://outcomes.cancer.gov/areas/measurement/irtfit_macro_users_guide.pdf
  • 24 Ware Jr JE, Kosinski M, Dewey J. How to Score Version Two of the SF-36 Health Survey. Lincoln, RI: QualityMetric Inc; 2000
  • 25 Thissen D, Reeve BB, Bjorner JB et al. Methodological issues for building item banks and computerized adaptive scales. Qual Life Res 2007; 16 (Suppl. 01) 109-119
  • 26 Ware Jr JE, Kosinski M, Bjorner JB et al. Applications of computerized adaptive testing (CAT) to the assessment of headache impact. Qual Life Res 2003; 12: 935-952
  • 27 Bayliss MS, Dewey JE, Dunlap I et al. A study of the feasibility of Internet administration of a computerized health survey: the headache impact test (HIT). Qual Life Res 2003; 12: 953-961
  • 28 Fliege H, Becker J, Walter OB et al. Development of a computer-adaptive test for depression (D-CAT). Qual Life Res 2005; 14: 2277-2291
  • 29 Fliege H, Becker J, Walter OB et al. Evaluation of a computer-adaptive test for the assessment of depression (D-CAT) in clinical application. Int J Methods Psychiatr Res 2009; 18: 23-36
  • 30 Rose M, Bjorner JB, Fischer F et al. Computerized adaptive testing – ready for ambulatory monitoring?. Psychosom Med 2012; 74: 338-348
  • 31 Hart DL, Werneke MW, Wang YC et al. Computerized adaptive test for patients with lumbar spine impairments produced valid and responsive measures of function. Spine 2010; 35: 2157-2164
  • 32 Hart DL, Wang YC, Cook KF et al. A computerized adaptive test for patients with shoulder impairments produced responsive measures of function. Phys Ther 2010; 90: 928-938
  • 33 Hsueh IP, Chen JH, Wang CH et al. Development of a computerized adaptive test for assessing balance function in patients with stroke. Phys Ther 2010; 90: 1336-1344
  • 34 Hart DL, Wang YC, Stratford PW et al. A computerized adaptive test for patients with hip impairments produced valid and responsive measures of function. Arch Phys Med Rehabil 2008; 89: 2129-2139
  • 35 Hart DL, Wang YC, Stratford PW et al. Computerized adaptive test for patients with knee impairments produced valid and responsive measures of function. J Clin Epidemiol 2008; 61: 1113-1124
  • 36 Elhan AH, Oztuna D, Kutlay S et al. An initial application of computerized adaptive testing (CAT) for measuring disability in patients with low back pain. BMC Musculoskelet Disord 2008; 9: 166
  • 37 Jette AM, Haley SM, Ni P et al. Creating a computer adaptive test version of the late-life function and disability instrument. J Gerontol A Biol Sci Med Sci 2008; 63: 1246-1256
  • 38 Haley SM, Siebens H, Coster WJ et al. Computerized adaptive testing for follow-up after discharge from inpatient rehabilitation: I. Activity outcomes. Arch Phys Med Rehabil 2006; 87: 1033-1042
  • 39 Gibbons RD, Weiss DJ, Kupfer DJ et al. Using computerized adaptive testing to reduce the burden of mental health assessment. Psychiatr Serv 2008; 59: 361-368
  • 40 Becker J, Fliege H, Kocalevent RD et al. Functioning and validity of a Computerized Adaptive Test to measure anxiety (A-CAT). Depress Anxiety 2008; 25: E182-E194
  • 41 Walter OB, Becker J, Fliege H et al. Developmental Steps for a Computer Adaptive Test for Anxiety (A-CAT). Diagnostica 2005; 51: 88-100
  • 42 Walter OB, Becker J, Bjorner JB et al. Development and evaluation of a computer adaptive test for ‘Anxiety’ (A-CAT). Qual Life Res 2007; 16 (Suppl. 01) 143-155
  • 43 Kocalevent RD, Rose M, Becker J et al. An evaluation of patient-reported outcomes found computerized adaptive testing was efficient in assessing stress perception. J Clin Epidemiol 2009; 62 (278–287) 287
  • 44 Anatchkova MD, Saris-Baglama RN, Kosinski M et al. Development and preliminary testing of a computerized adaptive assessment of chronic pain. J Pain 2009; 10: 932-943
  • 45 Schwartz C, Welch G, Santiago-Kelley P et al. Computerized adaptive testing of diabetes impact: a feasibility study of Hispanics and non-Hispanics in an active clinic population. Qual Life Res 2006; 15: 1503-1518
  • 46 Turner-Bowker DM, Saris-Baglama RN, Anatchkova M et al. A Computerized Asthma Outcomes Measure Is Feasible for Disease Management. Am J Pharm Benefits 2010; 2: 119-124
  • 47 Forkmann T, Boecker M, Wirtz M et al. Validation of the Rasch-based Depression Screening in a large scale German general population sample. Health Qual Life Outcomes 2010; 8: 105
  • 48 Chien TW, Wu HM, Wang WC et al. Reduction in patient burdens with graphical computerized adaptive testing on the ADL scale: tool development and simulation. Health Qual Life Outcomes 2009; 7: 39
  • 49 Haley SM, Raczek AE, Coster WJ et al. Assessing mobility in children using a computer adaptive testing version of the pediatric evaluation of disability inventory. Arch Phys Med Rehabil 2005; 86: 932-939
  • 50 Haley SM, Ni P, Hambleton RK et al. Computer adaptive testing improved accuracy and precision of scores over random item selection in a physical functioning item bank. J Clin Epidemiol 2006; 59: 1174-1182
  • 51 Haley SM, Fragala-Pinkham M, Ni P. Sensitivity of a computer adaptive assessment for measuring functional mobility changes in children enrolled in a community fitness programme. Clin Rehabil 2006; 20: 616-622
  • 52 Haley SM, Gandek B, Siebens H et al. Computerized adaptive testing for follow-up after discharge from inpatient rehabilitation: II. Participa­tion outcomes. Arch Phys Med Rehabil 2008; 89: 275-283
  • 53 Hart DL, Mioduski JE, Stratford PW. Simulated computerized adaptive tests for measuring functional status were efficient with good discriminant validity in patients with hip, knee, or foot/ankle impairments. J Clin Epidemiol 2005; 58: 629-638
  • 54 Hart DL, Cook KF, Mioduski JE et al. Simulated computerized adaptive test for patients with shoulder impairments was efficient and produced valid measures of function. J Clin Epidemiol 2006; 59: 290-298
  • 55 Hart DL, Mioduski JE, Werneke MW et al. Simulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function. J Clin Epidemiol 2006; 59: 947-956
  • 56 Hart DL, Wang YC, Stratford PW et al. Computerized adaptive test for patients with foot or ankle impairments produced valid and responsive measures of function. Qual Life Res 2008; 17: 1081-1091
  • 57 Choi SW, Reise SP, Pilkonis PA et al. Efficiency of static and computer adaptive short forms compared to full-length measures of depressive symptoms. Qual Life Res 2010; 19: 125-136
  • 58 Cella D, Yount S, Rothrock N et al. The Patient-Reported Outcomes Measurement Information System (PROMIS): progress of an NIH Roadmap cooperative group during its first two years. Med Care 2007; 45 (Suppl. 01) S3-S11
  • 59 Cella D, Riley W, Stone A et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clin Epidemiol 2010; 63: 1179-1194
  • 60 Rose M, Wahl I, Crusius J et al. Psychological comorbidity. A challenge in acute care. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2011; 54: 83-89
  • 61 FDA . Guidance for Industry. Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims. 2009
  • 62 Rose M, Bezjak A. Logistics of collecting patient-reported outcomes (PROs) in clinical practice: an overview and practical examples. Qual Life Res 2009; 18: 125-136
  • 63 Rose M, Hess V, Horhold M et al. Mobile computer-assisted psychometric diagnosis. Economic advantages and results on test stability. Psychother Psychosom Med Psychol 1999; 49: 202-207
  • 64 Fries JF, Spitz PW, Young DY. The dimensions of health outcomes: the health assessment questionnaire, disability and pain scales. J Rheumatol 1982; 9: 789-793