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Home / Archives for de Oliveira MR

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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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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