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J Clin Epidemiol. 2016 Mar;71:58-67. doi: 10.1016/j.jclinepi.2015.09.004. Epub 2015 Sep 28.

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

Author information

1
College of Medicine and Veterinary Medicine, The University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, Edinburgh, UK.
2
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, 4th Floor, Boston, MA 02115, USA.
3
Ophthalmology 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.
4
Stanford 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.

Abstract

OBJECTIVES:

Instead 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 SETTING:

We 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.

RESULTS:

A 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.

CONCLUSION:

Field-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.

KEYWORDS:

Big data; Exposome-wide association study; Meta-analysis; Observational study; Risk factor epidemiology; Statistical modeling

PMID:
26415577
PMCID:
PMC5108175
DOI:
10.1016/j.jclinepi.2015.09.004
[Indexed for MEDLINE]
Free PMC Article

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