Addressing the estimation of standard errors in fixed effects meta‐analysis

Standard methods for fixed effects meta‐analysis assume that standard errors for study‐specific estimates are known, not estimated. While the impact of this simplifying assumption has been shown in a few special cases, its general impact is not well understood, nor are general‐purpose tools availabl...

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Veröffentlicht in:Statistics in medicine Jg. 37; H. 11; S. 1788 - 1809
Hauptverfasser: Domínguez Islas, Clara, Rice, Kenneth M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Wiley Subscription Services, Inc 20.05.2018
John Wiley and Sons Inc
Schlagworte:
Biostatistics - methods
Common Cold - drug therapy
Computer Simulation
Errors
fixed effects
heterogeneity
Humans
Meta-analysis
Meta-Analysis as Topic
Methods
Models, Statistical
random effects
Systematic Reviews as Topic
Zinc - therapeutic use
random effects
meta-analysis
fixed effects
heterogeneity
ISSN:0277-6715, 1097-0258, 1097-0258
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Abstract Standard methods for fixed effects meta‐analysis assume that standard errors for study‐specific estimates are known, not estimated. While the impact of this simplifying assumption has been shown in a few special cases, its general impact is not well understood, nor are general‐purpose tools available for inference under more realistic assumptions. In this paper, we aim to elucidate the impact of using estimated standard errors in fixed effects meta‐analysis, showing why it does not go away in large samples and quantifying how badly miscalibrated standard inference will be if it is ignored. We also show the important role of a particular measure of heterogeneity in this miscalibration. These developments lead to confidence intervals for fixed effects meta‐analysis with improved performance for both location and scale parameters.
AbstractList Standard methods for fixed effects meta-analysis assume that standard errors for study-specific estimates are known, not estimated. While the impact of this simplifying assumption has been shown in a few special cases, its general impact is not well understood, nor are general-purpose tools available for inference under more realistic assumptions. In this paper, we aim to elucidate the impact of using estimated standard errors in fixed effects meta-analysis, showing why it does not go away in large samples and quantifying how badly miscalibrated standard inference will be if it is ignored. We also show the important role of a particular measure of heterogeneity in this miscalibration. These developments lead to confidence intervals for fixed effects meta-analysis with improved performance for both location and scale parameters.
Standard methods for fixed effects meta-analysis assume that standard errors for study-specific estimates are known, not estimated. While the impact of this simplifying assumption has been shown in a few special cases, its general impact is not well understood, nor are general-purpose tools available for inference under more realistic assumptions. In this paper, we aim to elucidate the impact of using estimated standard errors in fixed effects meta-analysis, showing why it does not go away in large samples and quantifying how badly miscalibrated standard inference will be if it is ignored. We also show the important role of a particular measure of heterogeneity in this miscalibration. These developments lead to confidence intervals for fixed effects meta-analysis with improved performance for both location and scale parameters.Standard methods for fixed effects meta-analysis assume that standard errors for study-specific estimates are known, not estimated. While the impact of this simplifying assumption has been shown in a few special cases, its general impact is not well understood, nor are general-purpose tools available for inference under more realistic assumptions. In this paper, we aim to elucidate the impact of using estimated standard errors in fixed effects meta-analysis, showing why it does not go away in large samples and quantifying how badly miscalibrated standard inference will be if it is ignored. We also show the important role of a particular measure of heterogeneity in this miscalibration. These developments lead to confidence intervals for fixed effects meta-analysis with improved performance for both location and scale parameters.
Author Rice, Kenneth M.
Domínguez Islas, Clara
AuthorAffiliation 1 Fred Hutchinson Cancer Research Center Seattle WA USA
2 MRC Biostatistics Unit School of Clinical Medicine, University of Cambridge Cambridge UK
3 Department of Biostatistics University of Washington Seattle WA USA
AuthorAffiliation_xml – name: 3 Department of Biostatistics University of Washington Seattle WA USA
– name: 1 Fred Hutchinson Cancer Research Center Seattle WA USA
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Author_xml – sequence: 1
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  surname: Domínguez Islas
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  givenname: Kenneth M.
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Issue 11
Keywords random effects
meta-analysis
fixed effects
heterogeneity
Language English
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Snippet Standard methods for fixed effects meta‐analysis assume that standard errors for study‐specific estimates are known, not estimated. While the impact of this...
Standard methods for fixed effects meta-analysis assume that standard errors for study-specific estimates are known, not estimated. While the impact of this...
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StartPage 1788
SubjectTerms Biostatistics - methods
Common Cold - drug therapy
Computer Simulation
Errors
fixed effects
heterogeneity
Humans
Meta-analysis
Meta-Analysis as Topic
Methods
Models, Statistical
random effects
Systematic Reviews as Topic
Zinc - therapeutic use
Title Addressing the estimation of standard errors in fixed effects meta‐analysis
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fsim.7625
https://www.ncbi.nlm.nih.gov/pubmed/29574827
https://www.proquest.com/docview/2035200578
https://www.proquest.com/docview/2018662770
https://pubmed.ncbi.nlm.nih.gov/PMC6001579
Volume 37
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