When should meta‐analysis avoid making hidden normality assumptions?
Meta‐analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta‐analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here,...
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| Published in: | Biometrical journal Vol. 60; no. 6; pp. 1040 - 1058 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Germany
Wiley - VCH Verlag GmbH & Co. KGaA
01.11.2018
John Wiley and Sons Inc |
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| ISSN: | 0323-3847, 1521-4036, 1521-4036 |
| Online Access: | Get full text |
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| Abstract | Meta‐analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta‐analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here, we review how the normal distribution is used in meta‐analysis. We discuss when the normal distribution is likely to be adequate and also when it should be avoided. We discuss alternative and more advanced methods that make less use of the normal distribution. We conclude that statistical methods that make fewer normality assumptions should be considered more often in practice. In general, statisticians and applied analysts should understand the assumptions made by their statistical analyses. They should also be able to defend these assumptions. Our hope is that this article will foster a greater appreciation of the extent to which assumptions involving the normal distribution are made in statistical methods for meta‐analysis. We also hope that this article will stimulate further discussion and methodological work. |
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| AbstractList | Meta-analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta-analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here, we review how the normal distribution is used in meta-analysis. We discuss when the normal distribution is likely to be adequate and also when it should be avoided. We discuss alternative and more advanced methods that make less use of the normal distribution. We conclude that statistical methods that make fewer normality assumptions should be considered more often in practice. In general, statisticians and applied analysts should understand the assumptions made by their statistical analyses. They should also be able to defend these assumptions. Our hope is that this article will foster a greater appreciation of the extent to which assumptions involving the normal distribution are made in statistical methods for meta-analysis. We also hope that this article will stimulate further discussion and methodological work. Meta-analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta-analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here, we review how the normal distribution is used in meta-analysis. We discuss when the normal distribution is likely to be adequate and also when it should be avoided. We discuss alternative and more advanced methods that make less use of the normal distribution. We conclude that statistical methods that make fewer normality assumptions should be considered more often in practice. In general, statisticians and applied analysts should understand the assumptions made by their statistical analyses. They should also be able to defend these assumptions. Our hope is that this article will foster a greater appreciation of the extent to which assumptions involving the normal distribution are made in statistical methods for meta-analysis. We also hope that this article will stimulate further discussion and methodological work.Meta-analysis is a widely used statistical technique. The simplicity of the calculations required when performing conventional meta-analyses belies the parametric nature of the assumptions that justify them. In particular, the normal distribution is extensively, and often implicitly, assumed. Here, we review how the normal distribution is used in meta-analysis. We discuss when the normal distribution is likely to be adequate and also when it should be avoided. We discuss alternative and more advanced methods that make less use of the normal distribution. We conclude that statistical methods that make fewer normality assumptions should be considered more often in practice. In general, statisticians and applied analysts should understand the assumptions made by their statistical analyses. They should also be able to defend these assumptions. Our hope is that this article will foster a greater appreciation of the extent to which assumptions involving the normal distribution are made in statistical methods for meta-analysis. We also hope that this article will stimulate further discussion and methodological work. |
| Author | Jackson, Dan White, Ian R. |
| AuthorAffiliation | 1 Statistical Innovation Group AstraZeneca Cambridge UK 2 MRC Clinical Trials Unit at UCL London UK |
| AuthorAffiliation_xml | – name: 1 Statistical Innovation Group AstraZeneca Cambridge UK – name: 2 MRC Clinical Trials Unit at UCL London UK |
| Author_xml | – sequence: 1 givenname: Dan orcidid: 0000-0002-4963-8123 surname: Jackson fullname: Jackson, Dan email: daniel.jackson1@astrazeneca.com organization: Statistical Innovation Group – sequence: 2 givenname: Ian R. surname: White fullname: White, Ian R. organization: MRC Clinical Trials Unit at UCL |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30062789$$D View this record in MEDLINE/PubMed |
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| Copyright | 2018 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim. 2018 The Authors. Biometrical Journal Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. 2018 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim |
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| Keywords | distributional assumptions random effects models central limit theorem normal approximation |
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| SubjectTerms | Aversive Therapy C-Reactive Protein - metabolism Central limit theorem Discussion: When should meta‐analysis avoid making hidden normality assumptions? distributional assumptions Humans Meta-analysis Meta-Analysis as Topic normal approximation Normal Distribution Normality random effects models Review Smoking Cessation Statistical analysis Statistical methods Statistics as Topic - methods |
| Title | When should meta‐analysis avoid making hidden normality assumptions? |
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