47 research outputs found

    The Log-logistic Regression Model with a Threshold Stress 1

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    Abstract. In this paper we propose an accelerated lifetime test model with threshold stress under a Log-logistic distribution to express the behavior of lifetimes and a general stress-response relationship. We present a sampling-based inference procedure of the model based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. The methodology is illustrated on an artificial and real lifetime data set

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

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    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. 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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. 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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? 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    Pervasive gaps in Amazonian ecological research

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    Pervasive gaps in Amazonian ecological research

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    Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 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 or ganism 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 ne glected 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 lostinfo:eu-repo/semantics/publishedVersio

    Pervasive gaps in Amazonian ecological research

    Get PDF
    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
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