Evidence map›Paper›PMID 26415577›Full record

SynthesisJournal of clinical epidemiology2016

Field-wide meta-analyses of observational associations can map selective availability of risk factors and the impact of model specifications.

Stylianos Serghiou, Chirag J Patel, Yan Yu Tan, Peter Koay, John P A Ioannidis

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Journal of clinical epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 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

5 authors.

Stylianos SerghiouCollege of Medicine and Veterinary Medicine, The University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, Edinburgh, UK.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, 4th Floor, Boston, MA 02115, USA.
Yan Yu TanCollege of Medicine and Veterinary Medicine, The University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, Edinburgh, UK.
Peter KoayOphthalmology Department, St John's Hospital, Howden South Road, Livingston, West Lothian, EH54 6PP, UK; The Princess Alexandra Eye Pavilion, Chalmers Street, Edinburgh EH3 9HA, UK.
John P A IoannidisStanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 1265 Welch Rd, MSOB X306, Stanford, CA 94305, USA; Department of Health Research and Policy, Stanford University School of Medicine, 150 Governor's Lane, Stanford, CA 94305, USA; Department of Statistics, Stanford University School of Humanities and Sciences, 390 Serra Mall, Stanford, CA 94305, USA; Meta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, 1070 Arastradero Road, Palo Alto, CA 94304, USA. Electronic address: jioannid@stanford.edu.

Funding

NIEHS NIH HHS K99 ES023504NIEHS NIH HHS K99ES023504NIEHS NIH HHS R00 ES023504NIEHS NIH HHS R21 ES025052
6 · The paper itself

Abstract

objectivesInstead of evaluating one risk factor at a time, we illustrate the utility of "field-wide meta-analyses" in considering all available data on all putative risk factors of a disease simultaneously. STUDY DESIGN AND

settingWe identified studies on putative risk factors of pterygium (surfer's eye) in PubMed, EMBASE, and Web of Science. We mapped which factors were considered, reported, and adjusted for in each study. For each putative risk factor, four meta-analyses were done using univariate only, multivariate only, preferentially univariate, or preferentially multivariate estimates.

resultsA total of 2052 records were screened to identify 60 eligible studies reporting on 65 putative risk factors. Only 4 of 60 studies reported both multivariate and univariate regression analyses. None of the 32 studies using multivariate analysis adjusted for the same set of risk factors. Effect sizes from different types of regression analyses led to significantly different summary effect sizes (P-value < 0.001). Observed heterogeneity was very high for both multivariate (median I(2), 76.1%) and univariate (median I(2), 85.8%) estimates. No single study investigated all 11 risk factors that were statistically significant in at least one of our meta-analyses.

conclusionField-wide meta-analyses can map availability of risk factors and trends in modeling, adjustments and reporting, as well as the impact of differences in model specification.

Indexed as

Models, TheoreticalHumansObservational Studies as TopicResearch DesignRisk FactorsBig dataExposome-wide association studyMeta-analysisObservational studyRisk factor epidemiologyStatistical modeling

Identifiers

PMID26415577
PMCPMC5108175

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.