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Evidence synthesis in prognosis research

Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analy...

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Detalles Bibliográficos
Autores principales: Debray, Thomas P.A., de Jong, Valentijn M.T., Moons, Karel G.M., Riley, Richard D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6621956/
https://www.ncbi.nlm.nih.gov/pubmed/31338426
http://dx.doi.org/10.1186/s41512-019-0059-4
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author Debray, Thomas P.A.
de Jong, Valentijn M.T.
Moons, Karel G.M.
Riley, Richard D.
author_facet Debray, Thomas P.A.
de Jong, Valentijn M.T.
Moons, Karel G.M.
Riley, Richard D.
author_sort Debray, Thomas P.A.
collection PubMed
description Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods.
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spelling pubmed-66219562019-07-23 Evidence synthesis in prognosis research Debray, Thomas P.A. de Jong, Valentijn M.T. Moons, Karel G.M. Riley, Richard D. Diagn Progn Res Methodology Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods. BioMed Central 2019-07-11 /pmc/articles/PMC6621956/ /pubmed/31338426 http://dx.doi.org/10.1186/s41512-019-0059-4 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology
Debray, Thomas P.A.
de Jong, Valentijn M.T.
Moons, Karel G.M.
Riley, Richard D.
Evidence synthesis in prognosis research
title Evidence synthesis in prognosis research
title_full Evidence synthesis in prognosis research
title_fullStr Evidence synthesis in prognosis research
title_full_unstemmed Evidence synthesis in prognosis research
title_short Evidence synthesis in prognosis research
title_sort evidence synthesis in prognosis research
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6621956/
https://www.ncbi.nlm.nih.gov/pubmed/31338426
http://dx.doi.org/10.1186/s41512-019-0059-4
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