A Measurement Model of Disease Severity in Absence of a Gold Standard

P. Martus

doi:10.1055/s-0038-1634164

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2001; 40(03): 265-271
DOI: 10.1055/s-0038-1634164

Original Article

Schattauer GmbH

A Measurement Model of Disease Severity in Absence of a Gold Standard

P. Martus

¹Institute of Medical Informatics, Biometry and Epidemiology Free University Berlin Germany

› Institutsangaben

Abstract

Abstract:

In the absence of a gold standard, we propose the use of confirmatory factor analysis for the quantification of agreement between diagnostic measurements and the true disease severity. The essential assumption is conditional independence of diagnostic measurements adjusted for the severity of the disease. However, depending on the number of measurements available, the method works even if some of them are conditionally dependent. We illustrate the method using an example related to glaucoma eye disease.

Keywords:

Diagnosis - Agreement - Factor Analysis - Latent Variables - Glaucoma

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Referenzen

REFERENCES
1 Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Statistician 1983; 32: 307-17.
2 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; i: 307-10.
3 Fleiss JL. Design and Analysis of Clinical Experiments. New York: Wiley; 1986
4 Bland JM, Altman DG. Comparing two methods of clinical measurement: A personal history. Int J Epidemiol 1995; 24 (Suppl. 01) 7-14.
5 Shoukri MM. Agreement, Measurement of:. In: Encyclopedia of Biostatistics. Armitage P, Colton T. eds. New York: Wiley; 1998. 1: 103-17.
6 Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999; 8: 135-60.
7 Dunn G, Roberts C. Modelling method comparison data. Stat Methods Med Res 1999; 8: 161-79.
8 Bollen KA. Structural Equations with latent variables. New York: Wiley; 1989. chapters 2, 47
9 Bartholomew DJ, Knott M. Latent variable models and factor analysis. London: Arnold; 2. ed. 1999. chapters 3, 8.
10 American Academy of Ophthalmology.. Basic and Clinical Science Course 1998-1999. San Francisco: AAO; 1998. Section 10. Glaucoma, chapters II, VII.
11 Flammer J. The concept of visual field indices. Graefe’s Arch Clin Exp Ophthalmol 1985; 224: 389-92.
12 Horn F, Korth M, Martus P. Quick full-field flicker test in glaucoma diagnosis: correlations with perimetry and papillometry. J Glaucoma 1994; 3: 206-13.
13 Horn F, Martus P, Korth M. Comparison of temporal and spatio-temporal contrast-sensitivity tests in normal subjects and glaucoma patients. Ger J Ophthalmol 1995; 4: 97-102.
14 Korth M. et al. The pattern-evoked electro-retinogram (PERG): Age-related alterations and changes in glaucoma. Graefe’s Arch Clin Exp Ophthalmol 1989; 227: 123-30.
15 Korth M. et al. VEP test of the blue-sensitive pathway in glaucoma. Invest Ophthalmol Vis Sci 1994; 35: 2599-610.
16 Jonas JB, Fernandez MC, Naumann GOH. Glaucomatous parapapillary chorioretinal atrophy: Occurrence and correlations. Arch Ophthalmol 1992; 110: 214-22.
17 Martus P. et al. A multivariate statistical model for quantification of glaucomatous damage. Invest Ophthalmol Vis Sci 2000; 41: 1099-110.
18 Fahrmeier L, Hamerle A, Tutz G. eds. Multivariate statistische. Verfahren Berlin: de Gruyter; 1996. 2. ed., chapter 12..
19 Bentler PM, Stein JA. Structural equation models in medical research. Stat Methods Med Res 1992; 1: 159-81.
20 Arbuckle JL. Amos Users’ Guide Version 3.6. Chicago: SmallWaters Corporation; 1997
21 Efron B. Bootstrap methods: Another look at the jackknife. Ann Statist 1979; 7: 1-26.
22 Rosner B. Statistical methods in ophthalmology: An adjustment for the intraclass correlation between eyes. Biometrics 1982; 38: 105-14.
23 Dawid AP, Skene AM. Maximum likelihood estimation of observer error rates using the EM algorithm. Appl Statist 1979; 28: 20-8.
24 Deutschmann C, Guggenmoos-Holzmann I. Probleme bei der Validierung von diagnostischen Tests. Informatik, Biometrie und Epidemiologie in Medizin und Biologie 1994; 25: 128-39.
25 Hui SL, Zhou XH. Evaluation of diagnostic tests without gold standards. Stat Methods Med Res 1998; 7: 354-70.
26 Espeland MA, Handelman SL. Using latent class models to characterize and assess relative error in discrete measurements. Biometrics 1989; 45: 587-99.
27 Qu Y, Tan M, Kutner MH. Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. Biometrics 1996; 52: 797-810.
28 Jöreskog KG, Sörbom D. LISREL 8. Mooresville: Scientific Software Inc.; 1993
29 Bentler PM. EQS Structural Equations Program Manual. Los Angeles: BMDP Statistical Software; 1989
30 Dunn G, Everitt B, Pickles A. Modelling covariances and latent variables with EQS. London: Chapman and Hall; 1993
31 Hatcher L. A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute Inc.; 1998. 3rd printing.