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Exploring consequences of simulation design for apparent performance of methods of meta-analysis
Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of random-effects meta-analysis of log-odds-ratios, we investigate how choices in generating data affect such conclusions. The choices we study in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8411476/ https://www.ncbi.nlm.nih.gov/pubmed/34110941 http://dx.doi.org/10.1177/09622802211013065 |
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author | Kulinskaya, Elena Hoaglin, David C. Bakbergenuly, Ilyas |
author_facet | Kulinskaya, Elena Hoaglin, David C. Bakbergenuly, Ilyas |
author_sort | Kulinskaya, Elena |
collection | PubMed |
description | Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of random-effects meta-analysis of log-odds-ratios, we investigate how choices in generating data affect such conclusions. The choices we study include the overall log-odds-ratio, the distribution of probabilities in the control arm, and the distribution of study-level sample sizes. We retain the customary normal distribution of study-level effects. To examine the impact of the components of simulations, we assess the performance of the best available inverse–variance–weighted two-stage method, a two-stage method with constant sample-size-based weights, and two generalized linear mixed models. The results show no important differences between fixed and random sample sizes. In contrast, we found differences among data-generation models in estimation of heterogeneity variance and overall log-odds-ratio. This sensitivity to design poses challenges for use of simulation in choosing methods of meta-analysis. |
format | Online Article Text |
id | pubmed-8411476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-84114762021-09-03 Exploring consequences of simulation design for apparent performance of methods of meta-analysis Kulinskaya, Elena Hoaglin, David C. Bakbergenuly, Ilyas Stat Methods Med Res Articles Contemporary statistical publications rely on simulation to evaluate performance of new methods and compare them with established methods. In the context of random-effects meta-analysis of log-odds-ratios, we investigate how choices in generating data affect such conclusions. The choices we study include the overall log-odds-ratio, the distribution of probabilities in the control arm, and the distribution of study-level sample sizes. We retain the customary normal distribution of study-level effects. To examine the impact of the components of simulations, we assess the performance of the best available inverse–variance–weighted two-stage method, a two-stage method with constant sample-size-based weights, and two generalized linear mixed models. The results show no important differences between fixed and random sample sizes. In contrast, we found differences among data-generation models in estimation of heterogeneity variance and overall log-odds-ratio. This sensitivity to design poses challenges for use of simulation in choosing methods of meta-analysis. SAGE Publications 2021-06-10 2021-07 /pmc/articles/PMC8411476/ /pubmed/34110941 http://dx.doi.org/10.1177/09622802211013065 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Articles Kulinskaya, Elena Hoaglin, David C. Bakbergenuly, Ilyas Exploring consequences of simulation design for apparent performance of methods of meta-analysis |
title | Exploring consequences of simulation design for apparent performance
of methods of meta-analysis |
title_full | Exploring consequences of simulation design for apparent performance
of methods of meta-analysis |
title_fullStr | Exploring consequences of simulation design for apparent performance
of methods of meta-analysis |
title_full_unstemmed | Exploring consequences of simulation design for apparent performance
of methods of meta-analysis |
title_short | Exploring consequences of simulation design for apparent performance
of methods of meta-analysis |
title_sort | exploring consequences of simulation design for apparent performance
of methods of meta-analysis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8411476/ https://www.ncbi.nlm.nih.gov/pubmed/34110941 http://dx.doi.org/10.1177/09622802211013065 |
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