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Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement
A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta‐analysis is difficult because primary studies often use different methods of measure...
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/PMC4973834/ https://www.ncbi.nlm.nih.gov/pubmed/25924725 http://dx.doi.org/10.1002/sim.6493 |
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author | Riley, Richard D. Elia, Eleni G. Malin, Gemma Hemming, Karla Price, Malcolm P. |
author_facet | Riley, Richard D. Elia, Eleni G. Malin, Gemma Hemming, Karla Price, Malcolm P. |
author_sort | Riley, Richard D. |
collection | PubMed |
description | A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta‐analysis is difficult because primary studies often use different methods of measurement and/or different cut‐points to dichotomise continuous factors into ‘high’ and ‘low’ groups; selective reporting is also common. We illustrate how multivariate random effects meta‐analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut‐points and/or methods of measurement. The models account for within‐study and between‐study correlations, which utilises more information and reduces the impact of unreported cut‐points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut‐point to reduce the number of unknown parameters. The models provide important inferential results for each cut‐point and method of measurement, including the summary prognostic effect, the between‐study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta‐analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut‐points than previously thought. In the second, a multivariate meta‐analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut‐points). © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. |
format | Online Article Text |
id | pubmed-4973834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49738342016-08-17 Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement Riley, Richard D. Elia, Eleni G. Malin, Gemma Hemming, Karla Price, Malcolm P. Stat Med Research Articles A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta‐analysis is difficult because primary studies often use different methods of measurement and/or different cut‐points to dichotomise continuous factors into ‘high’ and ‘low’ groups; selective reporting is also common. We illustrate how multivariate random effects meta‐analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut‐points and/or methods of measurement. The models account for within‐study and between‐study correlations, which utilises more information and reduces the impact of unreported cut‐points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut‐point to reduce the number of unknown parameters. The models provide important inferential results for each cut‐point and method of measurement, including the summary prognostic effect, the between‐study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta‐analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut‐points than previously thought. In the second, a multivariate meta‐analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut‐points). © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. John Wiley and Sons Inc. 2015-04-29 2015-07-30 /pmc/articles/PMC4973834/ /pubmed/25924725 http://dx.doi.org/10.1002/sim.6493 Text en © 2015 The Authors. Statistics in Medicine 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/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Riley, Richard D. Elia, Eleni G. Malin, Gemma Hemming, Karla Price, Malcolm P. Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
title | Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
title_full | Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
title_fullStr | Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
title_full_unstemmed | Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
title_short | Multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
title_sort | multivariate meta‐analysis of prognostic factor studies with multiple cut‐points and/or methods of measurement |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4973834/ https://www.ncbi.nlm.nih.gov/pubmed/25924725 http://dx.doi.org/10.1002/sim.6493 |
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