Abstract
Assessment of accuracy and reliability of screening tests such as detection of cancer and depression is important in clinical and research studies. Although a diagnostic test may not be accurate, the criteria for the disease of interest are well defined, such as cancer cells for detecting breast cancer using mammography. In many studies, we are also interested in less transparent, multidimensional constructs such as quality of life. The latent and multidimensional nature of the latter construct often requires that we probe into various attributes of the construct such as physical, psychological and social functioning of an individual in the case of quality of life. Not only are the criteria less well defined, indeed in many cases the gold standard simply does not exist, but the multitude of assessments also needs to be synthesized to create a few meaningful scales to facilitate assessment of such latent constructs for clinical and research use. In this chapter, we discuss methods for assessing the accuracy of diagnostic tests and validating measurement scales for latent, multidimensional constructs. In the latter case, we also provide a brief overview of the construction of an instrument for assessing such latent constructs to help readers gain familiarity with this increasingly important tool for modern clinical practice and research. We use real study data to illustrate the methods discussed, along with the software used to facilitate the analysis.
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References
Bach P, Jett J, Pastorino U, Tockman M, Swensen S, Begg C (2007) Computed tomography screening and lung cancer outcomes. J Amer Med Assoc 297(9):953–961
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46
DeLong E, DeLong D, Clarke-Pearson D (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3):837–845
Donner A, Zou G (2002) Testing the equality of dependent intraclass correlation coefficients. J Roy Stat Soc D 51(3):367–379
Dorfman D, Alf E (1969) Maximum-likelihood estimation of parameters of signal-detection theory and determination of confidence intervals—Rating-method data. J Math Psychol 6(3):487–496
Duberstein P, Ma Y, Chapman B, Conwell Y, McGriff J, Coyne J, Franus N, Heisel M, Kaukeinen K, Sörensen S, Tu X, Lyness J (2011) Detection of depression in older adults by family and friends: distinguishing mood disorder signals from the noise of personality and everyday life. Int Psychogeriatr 23(04):634–643
Feldt L, Woodruff D, Salih F (1987) Statistical inference for coefficient alpha. Appl Psychol Meas 11(1):93
Fleiss J, Levin B, Paik M (2003) Statistical methods for rates and proportions, 3rd edn. Wiley, New York
Kowalski J, Tu XM (2008) Modern applied U-statistics. Wiley series in probability and statistics. Wiley-Interscience, Hoboken, NJ
Lin L (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255–268
Lloyd CJ (1998) Using smoothed receiver operating characteristic curves to summarize and compare diagnostic systems. J Amer Stat Assoc 93:1356–1364
Lu N, Gunzler D, Zhang H, Ma Y, He H, Tu X (2012) On robust inference for intraclass correlation coefficients. Technical report, The Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York
Lubetkin E, Jia H, Gold M (2003) Use of the SF-36 in low-income Chinese American primary care patients. Medical Care 41(4):447–457
Ma Y, Alejandro G, Hui Z, Tu X (2010) A u-statistics-based approach for modeling cronbach coefficient alpha within a longitudinal data setting. Stat Med 29(6):659–670
Ma Y, Tang W, Feng CY, Tu XM (2008) Inference for kappas for longitudinal study data: Applications to sexual health research. Biometrics 64(3):781–789
Ma Y, Tang W, Yu Q, Tu X (2010) Modeling concordance correlation coefficient for longitudinal study data. Psychometrika 75(1):99–119
McGraw K, Wong S (1996) Forming inferences about some intraclass correlation coefficients. Psychol Meth 1(1):30–46
Nakas C, Yiannoutsos C (2004) Ordered multiple-class roc analysis with continuous measurements. Stat Med 23(22):3437–3449
Shrout P, Fleiss J (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86(2):420–428
Spitzer RL, Gibbon M, Williams JBW (1994) Structured clinical interview for axis I DSM-IV disorders. Biometrics Research Department, New York State Psychiatric Institute.
Stewart J (2010) Calculus: early transcendentals. Brooks/Cole Publishing Company, Pacific Grove, CA
Tu X (2009) The domain-sampling model for measurement errors. Technical report, The Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York
Tu X, Litvak E, Pagano M (1992) Issues in human immunodeficiency virus (HIV) screening programs. Amer J Epidemiol 136(2):244–255
van Zyl J, Neudecker H, Nel D (2000) On the distribution of the maximum likelihood estimator of cronbach’s alpha. Psychometrika 65(3):271–280
Wan C, Gao L, Li X et al. (2005) Development of the general module for the system of quality of life instruments for patients with chronic disease: Items selection and structure of the general module[j]. Chin Ment Health J 11:444–447
Wan C, Jiang R, Tu XM, Tang W, Pan J, Yang R, Li X, Yang Z, Zhang X (2012) The hypertension scale of the system of Quality of Life Instruments for Chronic Diseases, QLICD-HY: a development and validation study. Int J Nurs Stud. 49(4):465–480
Wan C, Yang Z, Yang Y (2007) Development of the general module of the system of quality of life instruments for patients with chronic disease: Evaluation of the general module. Chinese J Behavior Med Sci 16:559–561
Ware Jr J, Sherbourne C (1992) The MOS 36-item short-form health survey (sf-36): I. conceptual framework and item selection. Med Care 30:473–483
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He, H., Gunzler, D., Ma, Y., Xia, Y. (2012). Assessment of Diagnostic Tests and Instruments. In: Tang, W., Tu, X. (eds) Modern Clinical Trial Analysis. Applied Bioinformatics and Biostatistics in Cancer Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4322-3_3
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DOI: https://doi.org/10.1007/978-1-4614-4322-3_3
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