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A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations

The log response ratio, lnRR, is the most frequently used effect size statistic for meta‐analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of va...

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Autores principales: Nakagawa, Shinichi, Noble, Daniel W. A., Lagisz, Malgorzata, Spake, Rebecca, Viechtbauer, Wolfgang, Senior, Alistair M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108319/
https://www.ncbi.nlm.nih.gov/pubmed/36573275
http://dx.doi.org/10.1111/ele.14144
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author Nakagawa, Shinichi
Noble, Daniel W. A.
Lagisz, Malgorzata
Spake, Rebecca
Viechtbauer, Wolfgang
Senior, Alistair M.
author_facet Nakagawa, Shinichi
Noble, Daniel W. A.
Lagisz, Malgorzata
Spake, Rebecca
Viechtbauer, Wolfgang
Senior, Alistair M.
author_sort Nakagawa, Shinichi
collection PubMed
description The log response ratio, lnRR, is the most frequently used effect size statistic for meta‐analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study‐specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta‐analyses of lnRR, regardless of ‘missingness’.
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spelling pubmed-101083192023-04-18 A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations Nakagawa, Shinichi Noble, Daniel W. A. Lagisz, Malgorzata Spake, Rebecca Viechtbauer, Wolfgang Senior, Alistair M. Ecol Lett Method The log response ratio, lnRR, is the most frequently used effect size statistic for meta‐analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study‐specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta‐analyses of lnRR, regardless of ‘missingness’. John Wiley and Sons Inc. 2022-12-26 2023-02 /pmc/articles/PMC10108319/ /pubmed/36573275 http://dx.doi.org/10.1111/ele.14144 Text en © 2022 The Authors. Ecology Letters published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Nakagawa, Shinichi
Noble, Daniel W. A.
Lagisz, Malgorzata
Spake, Rebecca
Viechtbauer, Wolfgang
Senior, Alistair M.
A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
title A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
title_full A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
title_fullStr A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
title_full_unstemmed A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
title_short A robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
title_sort robust and readily implementable method for the meta‐analysis of response ratios with and without missing standard deviations
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108319/
https://www.ncbi.nlm.nih.gov/pubmed/36573275
http://dx.doi.org/10.1111/ele.14144
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