The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Eileen M Stock - One of the best experts on this subject based on the ideXlab platform.
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estimation of disease prevalence True Positive Rate and false Positive Rate of two screening tests when disease verification is applied on only screen Positives a hierarchical model using multi center data
Cancer Epidemiology, 2012Co-Authors: Eileen M Stock, James D Stamey, Rengaswamy Sankaranarayanan, Dean M Young, Richard Muwonge, Marc ArbynAbstract:Abstract Objectives : A model is proposed to estimate and compare cervical cancer screening test properties for third world populations when only subjects with a Positive screen receive the gold standard test. Two fallible screening tests are compared, VIA and VILI. Methods : We extend the model of Berry et al. [1] to the multi-site case in order to pool information across sites and form better estimates for prevalences of cervical cancer, the True Positive Rates (TPRs), and false Positive Rates (FPRs). For 10 centers in five African countries and India involving more than 52,000 women, Bayesian methods were applied when gold standard results for subjects who screened negative on both tests were treated as missing. The Bayesian methods employed suitably correct for the missing screen negative subjects. The study included gold standard verification for all cases, making it possible to validate model-based estimation of accuracy using only outcomes of women with Positive VIA or VILI result (ignoring verification of double negative screening test results) with the observed full data outcomes. Results : Across the sites, estimates for the sensitivity of VIA ranged from 0.792 to 0.917 while for VILI sensitivities ranged from 0.929 to 0.977. False Positive estimates ranged from 0.056 to 0.256 for VIA and 0.085 to 0.269 for VILI. The pooled estimates for the TPR of VIA and VILI are 0.871 and 0.968, respectively, compared to the full data values of 0.816 and 0.918. Similarly, the pooled estimates for the FPR of VIA and VILI are 0.134 and 0.146, respectively, compared to the full data values of 0.144 and 0.146. Globally, we found VILI had a statistically significant higher sensitivity but no statistical difference for the false Positive Rates could be determined. Conclusion : Hierarchical Bayesian methods provide a straight forward approach to estimate screening test properties, prevalences, and to perform comparisons for screening studies where screen negative subjects do not receive the gold standard test. The hierarchical model with random effects used to analyze the sites simultaneously resulted in improved estimates compared to the single-site analyses with improved TPR estimates and nearly identical FPR estimates to the full data outcomes. Furthermore, higher TPRs but similar FPRs were observed for VILI compared to VIA.
Marc Arbyn - One of the best experts on this subject based on the ideXlab platform.
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estimation of disease prevalence True Positive Rate and false Positive Rate of two screening tests when disease verification is applied on only screen Positives a hierarchical model using multi center data
Cancer Epidemiology, 2012Co-Authors: Eileen M Stock, James D Stamey, Rengaswamy Sankaranarayanan, Dean M Young, Richard Muwonge, Marc ArbynAbstract:Abstract Objectives : A model is proposed to estimate and compare cervical cancer screening test properties for third world populations when only subjects with a Positive screen receive the gold standard test. Two fallible screening tests are compared, VIA and VILI. Methods : We extend the model of Berry et al. [1] to the multi-site case in order to pool information across sites and form better estimates for prevalences of cervical cancer, the True Positive Rates (TPRs), and false Positive Rates (FPRs). For 10 centers in five African countries and India involving more than 52,000 women, Bayesian methods were applied when gold standard results for subjects who screened negative on both tests were treated as missing. The Bayesian methods employed suitably correct for the missing screen negative subjects. The study included gold standard verification for all cases, making it possible to validate model-based estimation of accuracy using only outcomes of women with Positive VIA or VILI result (ignoring verification of double negative screening test results) with the observed full data outcomes. Results : Across the sites, estimates for the sensitivity of VIA ranged from 0.792 to 0.917 while for VILI sensitivities ranged from 0.929 to 0.977. False Positive estimates ranged from 0.056 to 0.256 for VIA and 0.085 to 0.269 for VILI. The pooled estimates for the TPR of VIA and VILI are 0.871 and 0.968, respectively, compared to the full data values of 0.816 and 0.918. Similarly, the pooled estimates for the FPR of VIA and VILI are 0.134 and 0.146, respectively, compared to the full data values of 0.144 and 0.146. Globally, we found VILI had a statistically significant higher sensitivity but no statistical difference for the false Positive Rates could be determined. Conclusion : Hierarchical Bayesian methods provide a straight forward approach to estimate screening test properties, prevalences, and to perform comparisons for screening studies where screen negative subjects do not receive the gold standard test. The hierarchical model with random effects used to analyze the sites simultaneously resulted in improved estimates compared to the single-site analyses with improved TPR estimates and nearly identical FPR estimates to the full data outcomes. Furthermore, higher TPRs but similar FPRs were observed for VILI compared to VIA.
Andrea Pagnani - One of the best experts on this subject based on the ideXlab platform.
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inter protein sequence co evolution predicts known physical interactions in bacterial ribosomes and the trp operon
PLOS ONE, 2016Co-Authors: Christoph Feinauer, Hendrik Szurmant, Martin Weigt, Andrea PagnaniAbstract:Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a True-Positive Rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a True Positive Rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.
Martin Weigt - One of the best experts on this subject based on the ideXlab platform.
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inter protein sequence co evolution predicts known physical interactions in bacterial ribosomes and the trp operon
PLOS ONE, 2016Co-Authors: Christoph Feinauer, Hendrik Szurmant, Martin Weigt, Andrea PagnaniAbstract:Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a True-Positive Rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a True Positive Rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.
Sangoh Lee - One of the best experts on this subject based on the ideXlab platform.
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improvement of blood culture contamination Rate blood volume and True Positive Rate after introducing a dedicated phlebotomy team
European Journal of Clinical Microbiology & Infectious Diseases, 2019Co-Authors: Moonsuk Bae, Hae In Kim, Joung Ha Park, Byunghan Ryu, Jeonghyun Chang, Heungsup Sung, Jiwon Jung, Min Jae Kim, Sunghan Kim, Sangoh LeeAbstract:The introduction of dedicated phlebotomy teams certified for blood collection has been reported to be highly cost-effective by reducing contamination Rates. However, data on their effects on blood volume and True Positive Rate are limited. Therefore, we investigated the effect of replacing interns with a phlebotomy team on blood culture results. We performed a 24-month retrospective, quasi-experimental study before and after the introduction of a phlebotomy team dedicated to collecting blood cultures in a 2700-bed tertiary-care hospital. The microbiology laboratory database was used to identify adult patients with Positive blood culture results. During the study period, there were no changes in blood collection method, blood culture tubes, and the application of antiseptic measures. Blood volume was measured by the BACTEC™ FX system based on red blood cell metabolism. A total of 162,207 blood cultures from 23,563 patients were analyzed, comprising 78,673 blood cultures during the intern period and 83,534 during the phlebotomy team period. Blood volume increased from a mean of 2.1 ml in the intern period to a mean of 5.6 ml in the phlebotomy team period (p < 0.001). Introduction of the phlebotomy team also reduced contamination Rate (0.27% vs. 0.45%, p < 0.001) and led to a higher True Positive Rate (5.87% vs. 5.01%, p < 0.05). The increased True Positive Rate associated with the phlebotomy team involved both gram-Positive and gram-negative bacteria. The introduction of a dedicated phlebotomy team can increase blood volumes, reduce blood culture contamination Rate, and increase True Positive Rate.
