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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost