Indexes To Measure Dependence Between Clinical Diagnostic Tests: A Comparative Study [indices Para Medir Dependencia Entre Pruebas Para Diagnóstico Clínico: Un Estudio Comparativo]

In many practical situations, clinical diagnostic procedures include two or more biological traits whose outcomes are expressed on a continuous scale and are then dichotomized using a cut point. As measurements are performed on the same individual there is a likely correlation between the continuous underlying traits that can go unnoticed when the parameter estimation is done with the resulting binary variables. In this paper, we compare the performance of two different indexes developed to evaluate the dependence between diagnostic clinical tests that assume binary structure in the results with the performance of the binary covariance and two copula dependence parameters.343433450Bohning, D., Patilea, V., A capture-recapture approach for screening using two diagnostic tests with availability of disease status for the positives only (2008) Journal of the American Statistical Association, 103, pp. 212-221Dendukuri, N., Joseph, L., Bayesian approaches to modelling the conditional dependence between multiple diagnostic tests (2001) Biometrics, 57, pp. 158-167Enoe, C., Georgiadis, M.P., Johnson, W.O., Estimation of sensitivity and specificity of two diagnostic tests (2000) Preventive Veterinary Medicine, 45, pp. 61-81Georgiadis, M.P., Johnson, W.O., Gardner, I.A., Correlation adjusted estimation of sensitivity and specificity of two diagnostic tests (2003) Journal of the Royal Statistical Society: Series C (Applied Statistics), 52, pp. 63-76Gumbel, E.J., Bivariate exponential distributions (1960) Journal of the American Statistical Association, 55, pp. 698-707Johnson, M.E., (1987) Multivariate Statistical Simulation, , Wiley and Sons, New YorkJoseph, L., Gyorkos, T.W., Coupal, L., Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard (1995) American Journal of Epidemiology, 141, pp. 263-272Nelsen, R.B., (1999) An Introduction to Copulas, , Springer Verlag, New YorkPark, C.G., Park, T., Shin, D.W., A simple method for generating correlated binary variates (1996) The American Statistician, 50 (4), pp. 306-310Shurtleff, D., Some characteristics related to the incidence of cardiovascular disease and death: 18-year follow-up (1974) An Epidemiological Investigation of Cardiovascular Disease. the Framinham Study, , National Institute of Health, in W. B. Kannel & T. Gordon, Washington, D. CThibodeau, L.A., Evaluating diagnostic tests (1981) Biometrics, 37, pp. 801-804Torrance-Rynard, V.L., Walter, S.D., Effects of dependent errors in the assessment of diagnostic tests performance (1997) Statistics In Medicine, 16, pp. 2157-2175Vacek, P.M., The effect of conditional dependence on the evaluation of diagnostic tests (1985) Biometrics, 41, pp. 959-96

Bayesian Estimation Of Performance Measures Of Cervical Cancer Screening Tests In The Presence Of Covariates And Absence Of A Gold Standard

In this paper we develop a Bayesian analysis to estimate the disease prevalence, the sensitivity and specificity of three cervical cancer screening tests (cervical cytology, visual inspection with acetic acid and Hybrid Capture II) in the presence of a covariate and in the absence of a gold standard. We use Metropolis-Hastings algorithm to obtain the posterior summaries of interest. The estimated prevalence of cervical lesions was 6.4% (a 95% credible interval [95% CI] was 3.9, 9.3). The sensitivity of cervical cytology (with a result of ≥ ASC-US) was 53.6% (95% CI: 42.1, 65.0) compared with 52.9% (95% CI: 43.5, 62.5) for visual inspection with acetic acid and 90.3% (95% CI: 76.2, 98.7) for Hybrid Capture II (with result of >1 relative light units). The specificity of cervical cytology was 97.0% (95% CI: 95.5, 98.4) and the specifi cities for visual inspection with acetic acid and Hybrid Capture II were 93.0% (95% CI: 91.0, 94.7) and 88.7% (95% CI: 85.9, 91.4), respectively. The Bayesian model with covariates suggests that the sensitivity and the specificity of the visual inspection with acetic acid tend to increase as the age of the women increases. The Bayesian method proposed here is an useful alternative to estimate measures of performance of diagnostic tests in the presence of covariates and when a gold standard is not available. An advantage of the method is the fact that the number of parameters to be estimated is not limited by the number of observations, as it happens with several frequentist approaches. However, it is important to point out that the Bayesian analysis requires informative priors in order for the parameters to be identifiable. The method can be easily extended for the analysis of other medical data sets.63346Begg, C.B., Greenes, R.A., Assessment of diagnostic tests when disease verification is subject to selection bias (1983) Biometrics, 39, pp. 207-215Zhou, X., Maximum likelihood estimators of sensitivity and specificity corrected for verification bias (1983) Commun Statis Theory Meth, 22, pp. 3177-3198Hui, S.L., Walter, S.D., Estimating the error rates of diagnostic tests (1980) Biometrics, 36, pp. 167-171Joseph, L., Gyorkos, T.W., Coupal, L., Bayesian estimation of disease prevalence and the parameters of diagnostic tests in the absence of a gold standard (1985) Am J Epidemiol, 141, pp. 263-272Hitt, E., Cancer in the Americas (2003) Lancet Oncol, 4, p. 9Brasil. Ministério da Saúde. Secretaria Nacional de Assistência à Saúde. Instituto Nacional do Câncer. Estimativas da incidência e mortalidade por câncer no Brasil. Rio de Janeiro: INCA2002. Available on website: 〈http://www.inca.org.br/cancer/ epide miologia/estimativa2002/estimativas.html〉Mitchell, M.F., Schottenfeld, D., Tortolero-Luna, G., Cantor, S.B., Richards-Kortum R. Colposcopy for the diagnosis of squamous intraepithelial lesions: A meta-analysis (1998) Obstet Gynecol, 91, pp. 626-631Hopman, E.H., Kenemans, P., Helmerhorst, T.J., Positive predictive rate of colposcopic examination of the cervix uteri: An overview of literature (1998) Obstet Gynecol Surv, 53, pp. 97-106Begg, C.B., Biases in the assessment of diagnostic tests (1987) Stat Med, 6, pp. 411-423Hui, S.L., Zhou, X.H., Evaluation of diagnostic tests without gold standards (1998) Stat Methods Med Res, 7, pp. 354-370Zhou, X.H., Correcting for verification bias in studies of a diagnostic test's accuracy (1998) Stat Methods Med Res, 7, pp. 337-353McCrory, D.C., Matchar, D.B., Bastian, L. et al. 1999. Evaluation of cervical cytology. Evidence report/technology assessment n.5. (Prepared by Duke University under Contract n. 290-97-0014). AHCPR publication n. 99-E010. Rockville: Agency for Health Care Policy and ResearchDendukuri, N., Joseph, L., Bayesian approaches to modelling the conditional dependence between multiple diagnostic tests (2001) Biometrics, 57, pp. 208-217Faraone, S.V., Tsuang, M.T., Measuring diagnostic accuracy in the absence of a "gold standard (1994) Am J Psychiatry, 151, pp. 650-657Qu, Y., Tan, M., Kutner, M.H., Random effects models in latent class analysis for evaluating accuracy of diagnostic tests (1996) Biometrics, 52, pp. 797-810Hadgu, A., Qu, Y., A biomedical application of latent models with random effects (1998) Appl Statist, 47, pp. 603-616Tanner, M., Wong, W., The calculation of posterior distributions by data augmentation (1987) J Am Statist Ass, 82, pp. 528-550Gelfand, A.E., Smith, A.F.M., Sampling Based Approaches to Calculating Marginal Densities (1990) J Am Stat Assoc, 85, pp. 398-409Gelfand, A.E., Gibbs sampling (2000) J Am Stat Assoc, 95, pp. 1300-1304Syrjanen, K., Naud, P., Derchain, S., Comparing PAP smear cytology, aided visual inspection, screening colposcopy, cervicography and HPV testing as optional screening tools in Latin America. Study design and baseline data of the LAMS study (2005) Anticancer Res, 25, pp. 3469-3480Sarian, L.O., Derchain, S.F., Naud, P., Evaluation of visual inspection with acetic acid (VIA), Lugol's iodine (VILI), cervical cytology and HPV testing as cervical screening tools in Latin America. This report refers to partial results from the LAMS (Latin AMerican Screening) study (2005) J Med Screen, 12, pp. 142-149Solomon, D., Davey, D., Kurman, R., The 2001 Bethesda System: Terminology for reporting results of cervical cytology (2002) JAMA, 287, pp. 2114-2119Blumenthal, P., Sanghvi, H., (1997) Atlas for unaided visual inspection of the cervix, , Baltimore and Harare: JHPIEGO Corporation and University of Zimbabwe Medical SchoolNanda, K., McCrory, D.C., Myers, E.R., Accuracy of the Papanicolaou test in screening for and follow-up of cervical cytologic abnormalities: A systematic review (2000) Ann Intern Med, 132, pp. 810-819Belinson, J.L., Pretorius, R.G., Zhang, W.H., Wu, L.Y., Qiao, Y.L., Elson, P., Cervical cancer screening by simple visual inspection after acetic acid (2001) Obstet Gynecol, 98, pp. 441-444Visual inspection with acetic acid for cervical-cancer screening: Test qualities in a primary care setting (1999) Lancet, 353, pp. 869-873. , University of Zimbabwe/JHPIEGO Cervical Cancer ProjectSchiffman, M., Herrero, R., Hildensheim, A., HPV DNA testing in cervical cancer screening: Results from women in a high-risk province of Costa Rica (2000) JAMA, 283, pp. 87-93Wright Jr, T.C., Lynette, D., Kuhn, L., Pollack, A., Lorincz, A., HPV DNA testing of self-collected vaginal samples compared with cytologic screening to detect cervical cancer (2000) JAMA, 283, pp. 81-86Box, G.E.P., Tiao, G.C., (1992) Bayesian Inference in Statistical Analysis, , Reprint edition. New York: Wiley-InterscienceAltman, D.G., Bland, J.M., Diagnostic tests 2: Predictive values (1994) BMJ, 309, p. 102Franco, E.L., Primary screening of cervical cancer with human papillomavirus tests (2003) J Natl Cancer Inst Monogr, 31, pp. 89-96Geweke J. 1992. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. Bayesian Statistics 4Bernardo, J.M.Berger, J.O.Dawid, A.P and.Smith, A.F.M., Eds.Clarendom Press: Oxford, 169-94Carlin, B.P., Louis, T.A., (2000) Bayes and empirical Bayes methods for data analysis, , 2nd ed. London: Chapman and Hall/CRCKoss, L.G., Human papillomavirus testing as a screening tool for cervical cancer (2000) JAMA, 283, p. 2525Shlay, J.C., Dunn, T., Byers, T., Barón, A.E., Douglas, J.M., Prediction of cervical intraepithelial neoplasia grade 2-3 using risk assessment and human papillomavirus testing in women with atypia on Papanicolaou Smears (2000) Obstet Gynecol, 96, pp. 410-416Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde A Bayesian measures of model complexity and fit (with discussion) (2002) J Roy Statist Soc B, 64, pp. 583-640Franco, E.L., Ferenczy, A., Assessing gains in diagnostic utility when human papillomavirus testing is used as an adjunct to papanicolaou smear in the triage of women with cervical cytologic abnormalities (1999) Am J Obstet Gynecol, 181, pp. 382-386Macaskill, P., Walter, S.D., Irwig, L., Franco, E.L., Assessing the gain in diagnostic performance when combining two diagnostic tests (2002) Statis Med, 21, pp. 2527-2546Brenner, H., How independent are multiple "independent" diagnostic classifications? Stat MedEspeland, M.A., Handelman, S.L., Using latent class models to characterize and assess relative error in discrete measurements (1989) Biometrics, 45, pp. 587-599Yang, I., Becker, M.P., Latent variable modeling of diagnostic accuracy (1997) Biometrics, 53, pp. 948-958Ratman, S., Franco, E.L., Ferenczy, A., Human papillomavirus testing for primary screening of cervical cancer precursors (2000) Cancer Epidemiol Biomarkers Prev, 9, pp. 945-951Coste, J., Cochand-Priollet, B., de Cremoux, P., Cross sectional study of conventional cervical smear, monolayer cytology and human papillomavirus DNA testing for cervical cancer screening (2003) BMJ, 326, p. 733Schiffman, M., Hildesheim, A., Herrero, R., Bratti, M., In reply: Human papillomavirus testing as a screening tool for cervical cancer (2000) JAMA, 283, pp. 2525-2526Londhe, M., George, S.S., Seshadri, L., Detection of CIN by naked eye visualization after application of acetic acid (1997) Indian J Cancer, 34, pp. 88-91Sankaranarayanan, R., Wesley, R., Somanathan, T., Visual inspection of the uterine cervix after the application of acetic acid in the detection of cervical carcinoma and its precursors (1998) Cancer, 83, pp. 2150-2156Sankaranarayanan, R., Shyamalayumary, B., Wesley, R., Sreedevi Amma, N., Parkin, D.M., Nair, M.K., Visual inspection with acetic acid in the early detection of cervical cancer and precursors (1999) Int J Cancer, 80, pp. 161-163Denny, L., Kuhn, L., Pollack, A., Wainwright, H., Wright Jr., T.C., Evaluation of alternative methods of cervical cancer screening in resource-poor settings (2000) Cancer, 89, pp. 826-833Denny, L., Kuhn, L., Pollack, A., Wright Jr., T.C., Direct visual inspection for cervical cancer screening: An analysis of factors influencing test performance (2002) Cancer, 94, pp. 1699-1707Parmigiani, G., Measuring uncertainty in complex decision analysis models (2002) Stat Methods Med Res, 11, pp. 513-537Koss, L.G., Human papillomavirus testing as a screening tool for cervical cancer (2000) JAMA, 283, pp. 2525-2526Smith, A.F.M., Roberts, G.O., Bayesian Computation via the Gibbs Sampler and Related MCMC Methods (1993) J R Stat SocB, 55, pp. 3-2

Dos pruebas para diagnóstico clínico: Uso de funciones copula en la estimación de la prevalencia y los parámetros de desempeño de las pruebas

In this paper, we introduce a Bayesian analysis to estimate the prevalence and performance test parameters of two diagnostic tests. We concentrated our interest in studies where the individuals with negative outcomes in both tests are not verified by a gold standard. Given that the screening tests are applied in the same individual we assume dependence between test results. Generally, to capture the possible existing dependence between test outcomes, it is assumed a binary covariance structure, but in this paper, as an alternative for this modeling, we consider the use of copula function structures. The posterior summaries of interest are obtained using standard MCMC (Markov Chain Monte Carlo) methods. We compare the results obtained with our approach with those obtained using binary covariance and assuming independence. We considerate two published medical data sets to illustrate the approach

Dos pruebas para diagnóstico clínico: Uso de funciones copula en la estimación de la prevalencia y los parámetros de desempeño de las pruebas

In this paper, we introduce a Bayesian analysis to estimate the prevalence and performance test parameters of two diagnostic tests. We concentrated our interest in studies where the individuals with negative outcomes in both tests are not verified by a gold standard. Given that the screening tests are applied in the same individual we assume dependence between test results. Generally, to capture the possible existing dependence between test outcomes, it is assumed a binary covariance structure, but in this paper, as an alternative for this modeling, we consider the use of copula function structures. The posterior summaries of interest are obtained using standard MCMC (Markov Chain Monte Carlo) methods. We compare the results obtained with our approach with those obtained using binary covariance and assuming independence. We considerate two published medical data sets to illustrate the approach

A Bayesian latent variable approach to functional principal components analysis with binary and count data

Probabilistic PCA, Logistic PCA, Factor analysis, Exponential family, Variational algorithm, Working observations, Splines,