Evidence map›Paper›PMID 30062789›Full record

ReviewBiometrical journal. Biometrische Zeitschrift2018

When should meta-analysis avoid making hidden normality assumptions?

Dan Jackson, Ian R White

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biometrical journal. Biometrische Zeitschrift, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 19 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 19 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

67 citing papers in PubMed, 19 syntheses or guidelines pooled it.

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  19. Meta-analysis of published excess relative risk estimates.Radiation and environmental biophysics · 2020
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7 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Dan JacksonStatistical Innovation Group, AstraZeneca, Cambridge, UK.ORCID 0000-0002-4963-8123
Ian R WhiteMRC Clinical Trials Unit at UCL, London, UK.

Funding

DYNAMICS OF TRANSITION TO VENTRICULAR FIBRILLATIONF32HL008014 · NHLBI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI SAXBERG, BO E · 1990 to 1990
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British Heart Foundation RG/08/014British Heart Foundation RG/13/13/30194British Heart Foundation RG13/13/30194British Heart Foundation SP/09/002Medical Research Council G0800270Medical Research Council MC_UU_12023/21Medical Research Council MR/L003120/1
6 · The paper itself

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.

Indexed as

Meta-Analysis as TopicAversive TherapyC-Reactive ProteinHumansNormal DistributionSmoking CessationStatistics as TopicC-Reactive Proteincentral limit theoremdistributional assumptionsnormal approximationrandom effects models

Identifiers

PMID30062789
PMCPMC6282623

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.