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Goodness of fit tools for dose–response meta‐analysis of binary outcomes
Goodness of fit evaluation should be a natural step in assessing and reporting dose–response meta‐analyses from aggregated data of binary outcomes. However, little attention has been given to this topic in the epidemiological literature, and goodness of fit is rarely, if ever, assessed in practice....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484373/ https://www.ncbi.nlm.nih.gov/pubmed/26679736 http://dx.doi.org/10.1002/jrsm.1194 |
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author | Discacciati, Andrea Crippa, Alessio Orsini, Nicola |
author_facet | Discacciati, Andrea Crippa, Alessio Orsini, Nicola |
author_sort | Discacciati, Andrea |
collection | PubMed |
description | Goodness of fit evaluation should be a natural step in assessing and reporting dose–response meta‐analyses from aggregated data of binary outcomes. However, little attention has been given to this topic in the epidemiological literature, and goodness of fit is rarely, if ever, assessed in practice. We briefly review the two‐stage and one‐stage methods used to carry out dose–response meta‐analyses. We then illustrate and discuss three tools specifically aimed at testing, quantifying, and graphically evaluating the goodness of fit of dose–response meta‐analyses. These tools are the deviance, the coefficient of determination, and the decorrelated residuals‐versus‐exposure plot. Data from two published meta‐analyses are used to show how these three tools can improve the practice of quantitative synthesis of aggregated dose–response data. In fact, evaluating the degree of agreement between model predictions and empirical data can help the identification of dose–response patterns, the investigation of sources of heterogeneity, and the assessment of whether the pooled dose–response relation adequately summarizes the published results. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. |
format | Online Article Text |
id | pubmed-5484373 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54843732017-07-10 Goodness of fit tools for dose–response meta‐analysis of binary outcomes Discacciati, Andrea Crippa, Alessio Orsini, Nicola Res Synth Methods Original Articles Goodness of fit evaluation should be a natural step in assessing and reporting dose–response meta‐analyses from aggregated data of binary outcomes. However, little attention has been given to this topic in the epidemiological literature, and goodness of fit is rarely, if ever, assessed in practice. We briefly review the two‐stage and one‐stage methods used to carry out dose–response meta‐analyses. We then illustrate and discuss three tools specifically aimed at testing, quantifying, and graphically evaluating the goodness of fit of dose–response meta‐analyses. These tools are the deviance, the coefficient of determination, and the decorrelated residuals‐versus‐exposure plot. Data from two published meta‐analyses are used to show how these three tools can improve the practice of quantitative synthesis of aggregated dose–response data. In fact, evaluating the degree of agreement between model predictions and empirical data can help the identification of dose–response patterns, the investigation of sources of heterogeneity, and the assessment of whether the pooled dose–response relation adequately summarizes the published results. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. John Wiley and Sons Inc. 2015-12-17 2017-06 /pmc/articles/PMC5484373/ /pubmed/26679736 http://dx.doi.org/10.1002/jrsm.1194 Text en © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Discacciati, Andrea Crippa, Alessio Orsini, Nicola Goodness of fit tools for dose–response meta‐analysis of binary outcomes |
title | Goodness of fit tools for dose–response meta‐analysis of binary outcomes |
title_full | Goodness of fit tools for dose–response meta‐analysis of binary outcomes |
title_fullStr | Goodness of fit tools for dose–response meta‐analysis of binary outcomes |
title_full_unstemmed | Goodness of fit tools for dose–response meta‐analysis of binary outcomes |
title_short | Goodness of fit tools for dose–response meta‐analysis of binary outcomes |
title_sort | goodness of fit tools for dose–response meta‐analysis of binary outcomes |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484373/ https://www.ncbi.nlm.nih.gov/pubmed/26679736 http://dx.doi.org/10.1002/jrsm.1194 |
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