Evidence map›Paper›PMID 34225716›Full record

ArticleBMC medicine2021

Validity of observational evidence on putative risk and protective factors: appraisal of 3744 meta-analyses on 57 topics.

Perrine Janiaud, Arnav Agarwal, Ioanna Tzoulaki, Evropi Theodoratou, Konstantinos K Tsilidis, Evangelos Evangelou, John P A Ioannidis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 6 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

24 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Perrine JaniaudMeta-Research Innovation Center at Stanford (METRICS), Stanford, CA, 94305, USA.
Arnav AgarwalDepartment of Medicine, University of Toronto, 1 King's College Circle #3172, Toronto, ON, M5S 1A8, Canada.
Ioanna TzoulakiDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus, 45110, Ioannina, Greece.
Evropi TheodoratouCentre for Global Health, The University of Edinburgh, Edinburgh, EH8 9AG, UK.
Konstantinos K TsilidisDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus, 45110, Ioannina, Greece.
Evangelos EvangelouDepartment of Hygiene and Epidemiology, University of Ioannina School of Medicine, University Campus, 45110, Ioannina, Greece.
John P A IoannidisMeta-Research Innovation Center at Stanford (METRICS), Stanford, CA, 94305, USA. jioannid@stanford.edu.ORCID 0000-0003-3118-6859

Funding

Cancer Research UK (GB) C31250/A22804Medical Research Council MR/S019669/1
6 · The paper itself

Abstract

backgroundThe validity of observational studies and their meta-analyses is contested. Here, we aimed to appraise thousands of meta-analyses of observational studies using a pre-specified set of quantitative criteria that assess the significance, amount, consistency, and bias of the evidence. We also aimed to compare results from meta-analyses of observational studies against meta-analyses of randomized controlled trials (RCTs) and Mendelian randomization (MR) studies.

methodsWe retrieved from PubMed (last update, November 19, 2020) umbrella reviews including meta-analyses of observational studies assessing putative risk or protective factors, regardless of the nature of the exposure and health outcome. We extracted information on 7 quantitative criteria that reflect the level of statistical support, the amount of data, the consistency across different studies, and hints pointing to potential bias. These criteria were level of statistical significance (pre-categorized according to 10

results3744 associations (in 57 umbrella reviews) assessed by a median number of 7 (interquartile range 4 to 11) observational studies were eligible. Most associations were statistically significant at P < 0.05 (61.1%, 2289/3744). Only 2.6% of associations had P < 10

conclusionsAcknowledging that no gold-standard exists to judge whether an observational association is genuine, statistically significant results are common in observational studies, but they are rarely convincing or corroborated by randomized evidence.

Indexed as

Observational Studies as TopicHumansProtective FactorsMendelian randomizationObservation studiesRandomized clinical trialsUmbrella review

Identifiers

PMID34225716
PMCPMC8259334

What OpenQuestion holds

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Registered trials

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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.