Meta-analyses in systematic reviews of randomized controlled trials in perinatal medicine: Comparison of fixed and random effects models.

Villar, José; Mackey, Maria Eugenia; Carroli, Guillermo; Donner, Allan

doi:10.1002/sim.1096

Cited by 96Meta-analysis and systematic reviewsStatistical Methods in Clinical TrialsDelphi Technique in Research

Meta-analyses in systematic reviews of randomized controlled trials in perinatal medicine: Comparison of fixed and random effects models.

Villar, José, Mackey, Maria Eugenia, Carroli, Guillermo, Donner, Allan·2001·Statistics in Medicine

DOI 10.1002/sim.1096

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Abstract

There is a need for empirical work comparing the random effects model with the fixed effects model in the calculation of a pooled relative risk in the meta-analysis in systematic reviews of randomized controlled trials. Such comparisons are particularly important when trial results are heterogeneous. We considered 84 independent meta-analyses in which each trial included a set of different women/newborns. These meta-analyses were included in systematic reviews published in the Cochrane Library's pregnancy and childbirth module. Twenty-one of these 84 meta-analyses demonstrated statistical heterogeneity at p<0.10. The random effects model estimates showed wider confidence intervals, particularly in those meta-analyses showing heterogeneity in the trial results. The summary relative risk for the random effects model tended to show a larger protective treatment effect than the fixed effects model in the heterogeneous meta-analyses. In this set of meta-analyses, statistical evaluation of publication bias cannot be shown to account for heterogeneity. Our empirical conclusion is that there may be opposing effects if the random effects model is used in the meta-analysis of clinical trials showing heterogeneity in the results: stronger treatment effects reflected in the summary relative risk, but wider confidence intervals about this summary measure.

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A fixed-effect analysis tends to give narrower confidence intervals than a random-effects analysis.
“This is why a fixed-effect analysis tends to give narrower confidence intervals (i.e., provide greater precision) than a random-effects analysis [110],[196],[199].”
From The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration (2009) · cites this paper as [196]

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Authors: Villar, J., Mackey, M. E., Carroli, G., & Donner, A.
Affiliations: World Health Organization (CH)
Centro Rosarino de Estudios Perinatales (AR)
Western University (CA)
Corresponding author: Villar, José
ORCID: Villar, José
Venue: Statistics in Medicine
Publisher: Wiley
Published: 2001-11-26
Volume: 20
Issue: 23
Pages: 3635-3647
ISSN: 0277-6715 (ISSN-L)
1097-0258
DOI: 10.1002/sim.1096
OpenAlex: W2120330366
Cited by: 96
References: 38
Citations by year: 2026: 2 · 2025: 2 · 2024: 1 · 2023: 4 · 2022: 1 · 2021: 4 · 2019: 4 · 2018: 7 · 2017: 2 · 2016: 5 · 2015: 6 · 2014: 3 · 2013: 1 · 2012: 5
FWCI: 0.82
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Statistics in Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Social Sciences › Sociology and Political Science › Delphi Technique in Research
Keywords: fixed effects model, random effects models, meta-analysis, perinatal medicine, randomized controlled trials, heterogeneity, pooled relative risk, confidence intervals, publication bias, pregnancy and childbirth, Cochrane Library
MeSH terms: Female, Humans, Infant, Newborn, Neonatology, Pregnancy, Meta-Analysis as Topic, Models, Statistical, Confidence Intervals, Chi-Square Distribution, Randomized Controlled Trials as Topic, Publication Bias

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Meta-analyses in systematic reviews of randomized controlled trials in perinatal medicine: Comparison of fixed and random effects models.

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