Indexes to Measure Dependence between Clinical Diagnostic Tests: A Comparative Study

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)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.343433450Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Bayesian Inference For The Segmented Weibull Distribution

In this paper, we introduce a Bayesian approach for segmented Weibull distributions which could be a good alternative to analyze medical survival data in the presence of censored observations and covariates. With the obtained Bayesian estimated change-points we could get an excellent fit of the proposed model to any data sets. With the proposed methodology, it is also possible to identify survival times intervals where a covariate could have significantly different effects when compared to other lifetime intervals, an important point under a clinical view. The obtained Bayesian estimates are obtained using standard Markov Chain Monte Carlo methods. Some examples with real data sets illustrate the proposed methodology and its potential clinical value

LC–MS-based absolute metabolite quantification:Application to metabolic flux measurement in trypanosomes

Human African trypanosomiasis is a neglected tropical disease caused by the protozoan parasite, Trypanosoma brucei. In the mammalian bloodstream, the trypanosome’s metabolism differs significantly from that of its host. For example, the parasite relies exclusively on glycolysis for energy source. Recently, computational and mathematical models of trypanosome metabolism have been generated to assist in understanding the parasite metabolism with the aim of facilitating drug development. Optimisation of these models requires quantitative information, including metabolite concentrations and/or metabolic fluxes that have been hitherto unavailable on a large scale. Here, we have implemented an LC–MS-based method that allows large scale quantification of metabolite levels by using U-13C-labelled E. coli extracts as internal standards. Known amounts of labelled E. coli extract were added into the parasite samples, as well as calibration standards, and used to obtain calibration curves enabling us to convert intensities into concentrations. This method allowed us to reliably quantify the changes of 43 intracellular metabolites and 32 extracellular metabolites in the medium over time. Based on the absolute quantification, we were able to compute consumption and production fluxes. These quantitative data can now be used to optimise computational models of parasite metabolism

Simple robust parameter estimation for the Birnbaum-Saunders distribution

© 2015, Wang et al. We study the problem of robust estimation for the two-parameter Birnbaum-Saunders distribution. It is well known that the maximum likelihood estimator (MLE) is efficient when the underlying model is true but at the same time it is quite sensitive to data contamination that is often encountered in practice. In this paper, we propose several estimators which have simple closed forms and are also robust to data contamination. We study the breakdown points and asymptotic properties of the proposed estimators. These estimators are then applied to both simulated and real datasets. Numerical results show that the proposed estimators are attractive alternative to the MLE in that they are quite robust to data contamination and also highly efficient when the underlying model is true

Bayesian estimation of performance measures of screening tests in the presence of covariates and absence of a gold standard

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)The sensitivity (S(e)) and the specificity (S(p)) are the two most common measures of the performance of a diagnostic test, where Se is the probability of a diseased individual to be correctly identified by the test while Sp is the probability of a healthy individual to be correctly identified by the same test. A problem appears when all individuals cannot be verified by a gold standard. This occurs when there is not a definitive test for detection of the disease or the verification by a gold standard is an impracticable procedure according to its cost, accessibility or risks. In this paper we develop a Bayesian analysis to estimate the disease prevalence, the sensitivity and specificity of screening tests in the presence of a covariate and in the absence of a gold standard. We use the Metropolis Hastings algorithm to obtain the posterior summaries of interest. We have as motivation for the investigation the LAMS (Latin American Screening) Study, an extensive project designed for comparing screening tools for cervical cancer in Brazil and Argentina. When applied to the analysis of data from LAMS Study, the proposed Bayesian method shows to be a useful alternative to estimate measures of performance of screening 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.2316881Research European Committee of the European Economical Comunity [INCO DEV 4-CT-2001-10013]Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Research European Committee of the European Economical Comunity [INCO DEV 4-CT-2001-10013]FAPESP [99/11264-0]CNPq [300354/01-0