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Home / Archives for Subtil A

Bayesian Latent Class Models in malaria diagnosis

  • Authors: de Oliveira MR, Do Rosário V, Gonçalves L, Lee PW, Shaio MF, Subtil A
  • Journal: PLoS One
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/22844405

The main focus of this study is to illustrate the importance of the statistical analysis in the evaluation of the accuracy of malaria diagnostic tests, without admitting a reference test, exploring a dataset (n=3317) collected in São Tomé and Príncipe.
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Conditional dependence diagnostic in the latent class model: A simulation study

  • Authors: de Oliveira MR, Gonçalves L, Subtil A
  • Journal: Statistics & Probability Letters
  • Link: https://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=4&SID=T2VvZFAKYvQv3Eo9tzK&page=1&doc=7

The classical latent class model assumes the hypothesis of conditional independence. We explore tools commonly used to validate this hypothesis (correlation residual plot, log-odds ratio check plot, and known goodness of fit tests) to make practitioners aware of these tools’ shortcomings in correctly identifying local dependence.
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ROC curve estimation: an overview

  • Authors: de Zea Bermudez P, Gonçalves L, Oliveira MR, Subtil A
  • Publication Year: 2014
  • Journal: REVSTAT – Statistical Journal
  • Link: https://www.ine.pt/revstat/pdf/rs140101.pdf

This work overviews some developments on the estimation of the Receiver Operating Characteristic (ROC) curve. Estimation methods in this area are constantly being developed, adjusted and extended, and it is thus impossible to cover all topics and areas of application in a single paper.
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About GHTM

GHTM is a R&D Unit that brings together researchers with a track record in Tropical Medicine and International & Global Health. It aims at strengthening Portugal's role as a leading partner in the development and implementation of a global health research agenda. Our evidence-based interventions contribute to the promotion of equity in health and to improve the health of populations.

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