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

Journal of Clinical Epidemiology

Volumn 71, Issue , 2016, Pages 58-67

  Serghiou, Stylianos ^a   Patel, Chirag J ^b   Tan, Yan Yu ^a   Koay, Peter ^c,d   Ioannidis, John P A ^e,f  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b HARVARD MEDICAL SCHOOL   (United States)

^c ST JOHN'S HOSPITAL   (United Kingdom)

^d PRINCESS ALEXANDRA EYE PAVILION   (United Kingdom)

^e STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^f STANFORD UNIVERSITY   (United States)

Author keywords

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

Indexed keywords

ARTICLE; EFFECT SIZE; EMBASE; HUMAN; MEDLINE; META ANALYSIS; MULTIVARIATE ANALYSIS; OBSERVATIONAL STUDY; PRIORITY JOURNAL; PTERYGIUM; RISK FACTOR; STATISTICAL SIGNIFICANCE; UNIVARIATE ANALYSIS; WEB OF SCIENCE; METHODOLOGY; STATISTICS AND NUMERICAL DATA; THEORETICAL MODEL;

HUMANS; MODELS, THEORETICAL; OBSERVATIONAL STUDIES AS TOPIC; RESEARCH DESIGN; RISK FACTORS;

EID: 84962953658     PISSN: 08954356     EISSN: 18785921     Source Type: Journal    
DOI: 10.1016/j.jclinepi.2015.09.004     Document Type: Article

References (50)

1. [1] Jones, D.R., Meta-analysis of observational epidemiological studies: a review. J R Soc Med 85 (1992), 165–168.
2. [2] Friedenreich, C.M., Methods for pooled analyses of epidemiologic studies. Epidemiology 4 (1993), 295–302.
3. [3] Blettner, M., Sauerbrei, W., Schlehofer, B., Scheuchenpflug, T., Friedenreich, C., Traditional reviews, meta-analyses and pooled analyses in epidemiology. Int J Epidemiol 28 (1999), 1–9.
4. [4] Egger, M., Schneider, M., Davey Smith, G., Spurious precision? Meta-analysis of observational studies. BMJ 316 (1998), 140–144.
5. [5] Blettner, M., Schlehofer, B., Sauerbrei, W., Limitations of meta-analysis from published data in epidemiological research. Soz Praventivmed 42 (1997), 95–104.
6. [6] Bennett, D.A., Review of analytical methods for prospective cohort studies using time to event data: single studies and implications for meta-analysis. Stat Methods Med Res 12 (2003), 297–319.
7. [7] Madigan, D., Ryan, P.B., Schuemie, M., Does design matter? Systematic evaluation of the impact of analytical choices on effect estimates in observational studies. Ther Adv Drug Saf 4 (2013), 53–62.
8. [8] Madigan, D., Stang, P.E., Berlin, J.A., Schuemie, M., Overhage, J.M., Suchard, M.A., et al. A systematic statistical approach to evaluating evidence from observational studies. Annu Rev Stat Appl 1 (2014), 11–39.
9. [9] Groenwold, R.H.H., Hoes, A.W., Hak, E., Confounding in publications of observational intervention studies. Eur J Epidemiol 22 (2007), 413–415.
10. [10] Peters, J., Mengersen, K., Selective reporting of adjusted estimates in observational epidemiology studies: reasons and implications for meta-analyses. Eval Health Prof 31 (2008), 370–389.
11. [11] Schneeweiss, S., Rassen, J.A., Glynn, R.J., Avorn, J., Mogun, H., Brookhart, M.A., High-dimensional propensity score adjustment in studies of treatment effects using health care claims data. Epidemiology 20 (2009), 512–522.
12. [12] Boffetta, P., McLaughlin, J.K., La Vecchia, C., Tarone, R.E., Lipworth, L., Blot, W.J., False-positive results in cancer epidemiology: a plea for epistemological modesty. J Natl Cancer Inst 100 (2008), 988–995.
13. [13] Fiedler, K., Voodoo correlations are everywhere—not only in neuroscience. Perspect Psychol Sci 6 (2011), 163–171.
14. [14] Ioannidis, J.P.A., Commentary: adjusting for bias: a user's guide to performing plastic surgery on meta-analyses of observational studies. Int J Epidemiol 40 (2011), 777–779.
15. [15] Feinstein, A.R., Meta-analysis: statistical alchemy for the 21st century. J Clin Epidemiol 48 (1995), 71–79.
16. [16] Ioannidis, J.P.A., Trikalinos, T.A., An exploratory test for an excess of significant findings. Clin Trials 4 (2007), 245–253.
17. [17] Ioannidis, J.P.A., Clarifications on the application and interpretation of the test for excess significance and its extensions. J Math Psychol 57 (2013), 184–187.
18. [18] Wilson D.B. Practical meta-analysis effect size calculator. Available at: http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php. Accessed April 29, 2015.
19. [19] Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. 1st ed. Wiley: Chichester, UK, 2009.
20. [20] Mackenzie, F.D., Hirst, L.W., Battistutta, D., Green, A., Risk analysis in the development of pterygia. Ophthalmology 99 (1992), 1056–1061.
21. [21] Zhang, J., Yu, K.F., What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 280 (1998), 1690–1691.
22. [22] Maloof, A.J., Ho, A., Coroneo, M.T., Influence of corneal shape on limbal light focusing. Invest Ophthalmol Vis Sci 35 (1994), 2592–2598.
23. [23] Detorakis, E.T., Spandidos, D.A., Pathogenetic mechanisms and treatment options for ophthalmic pterygium: trends and perspectives (review). Int J Mol Med 23 (2009), 439–447.
24. [24] RStudio. RStudio: Integrated development environment for R. ​RStudio Team. RStudio, Inc.; Boston, MA, 2015, Available at: http://www.rstudio.com/.
25. [25] Pohlert T. The pairwise multiple comparison of mean ranks package (PMCMR). R. package, 2014.
26. [26] Viechtbauer, W., Conducting meta-analyses in R with the metafor package. J Stat Softw 36 (2010), 1–48.
27. [27] Kerkenezov, N., A pterygium survey of the far north coast of New South Wales. Trans Ophthalmol Soc Aust 16 (1956), 110–119.
28. [28] Ibechukwu, B.I., Astigmatism and visual impairment in pterygium: affected eyes in Jos, Nigeria. East Afr Med J 67 (1990), 912–917.
29. [29] Detels, R., Dhir, S.P., Pterygium: a geographical study. Arch Ophthalmol 78 (1967), 485–491.
30. [30] Xie, X.W., Xu, L., Wang, Y.X., Jonas, J.B., Body height and ocular diseases. The Beijing eye study. Graefes Arch Clin Exp Ophthalmol 247 (2009), 1651–1657.
31. [31] Hernández-Díaz, S., Varas-Lorenzo, C., García Rodríguez, L.A., Non-steroidal antiinflammatory drugs and the risk of acute myocardial infarction. Basic Clin Pharmacol Toxicol 98 (2006), 266–274.
32. [32] Loke, Y.K., Kwok, C.S., Singh, S., Comparative cardiovascular effects of thiazolidinediones: systematic review and meta-analysis of observational studies. BMJ, 342, 2011, d1309.
33. [33] Glaeser EL. Researcher incentives and empirical methods, ​National Bureau of Economic Research (NBER) Technical Working Paper Series 329, 2006. Available at: ​ http://www.nber.org/papers/t0329.
34. [34] Elm von, E., Altman, D.G., Egger, M., Pocock, S.J., Gøtzsche, P.C., Vandenbroucke, J.P., et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Int J Surg 12 (2014), 1495–1499.
35. [35] Vandenbroucke, J.P., Elm von, E., Altman, D.G., Gøtzsche, P.C., Mulrow, C.D., Pocock, S.J., et al. Strengthening the reporting of observational studies in epidemiology (STROBE): explanation and elaboration. Int J Surg 12:12 (2014), 1500–1524.
36. [36] Ioannidis, J.P.A., Why most discovered true associations are inflated. Epidemiology 19 (2008), 640–648.
37. [37] Patel, C.J., Burford, B., Ioannidis, J.P.A., Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations. J Clin Epidemiol 68 (2015), 1046–1058.
38. [38] Lunt, M., Solomon, D., Rothman, K., Glynn, R., Hyrich, K., Symmons, D.P., et al. Different methods of balancing covariates leading to different effect estimates in the presence of effect modification. Am J Epidemiol 169 (2009), 909–917.
39. [39] Kavvoura, F.K., Liberopoulos, G., Ioannidis, J.P.A., Selection in reported epidemiological risks: an empirical assessment. PLoS Med, 4, 2007, e79.
40. [40] de Vries, F., de Vries, C., Cooper, C., Leufkens, B., van Staa, T.-P., Reanalysis of two studies with contrasting results on the association between statin use and fracture risk: the general practice research database. Int J Epidemiol 35 (2006), 1301–1308.
41. [41] Belbasis, L., Panagiotou, O.A., Dosis, V., Evangelou, E., A systematic appraisal of field synopses in genetic epidemiology: a HuGE review. Am J Epidemiol 181 (2015), 1–16.
42. [42] Khoury, M.J., McBride, C.M., Schully, S.D., Ioannidis, J.P.A., Feero, W.G., Janssens, A.C.J.W., et al. The scientific foundation for personal genomics: recommendations from a National Institutes of Health-Centers for Disease Control and Prevention multidisciplinary workshop. Genet Med 11 (2009), 559–567.
43. [43] Allen, N.C., Bagade, S., McQueen, M.B., Ioannidis, J.P.A., Kavvoura, F.K., Khoury, M.J., et al. Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: the SzGene database. Nat Genet 40 (2008), 827–834.
44. [44] Patel, C.J., Bhattacharya, J., Butte, A.J., An environment-wide association study (EWAS) on type 2 diabetes mellitus. PLoS One, 5, 2010, e10746.
45. [45] Patel, C.J., Cullen, M.R., Ioannidis, J.P.A., Butte, A.J., Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels. Int J Epidemiol 41 (2012), 828–843.
46. [46] Patel, C.J., Rehkopf, D.H., Leppert, J.T., Bortz, W.M., Cullen, M.R., Chertow, G.M., et al. Systematic evaluation of environmental and behavioural factors associated with all-cause mortality in the United states national health and nutrition examination Survey. Int J Epidemiol 42 (2013), 1795–1810.
47. [47] Patel, C.J., Ioannidis, J.P., Studying the elusive environment in large scale. JAMA 311 (2014), 2173–2174.
48. [48] Patel, C.J., Ioannidis, J.P.A., Placing epidemiological results in the context of multiplicity and typical correlations of exposures. J Epidemiol Community Health 68 (2014), 1096–1100.
49. [49] Patel, C.J., Manrai, A.K., Development of exposome correlation globes to map out environment-wide associations. Pac Symp Biocomput, 2015, 231–242.
50. [50] Ryan, P.B., Madigan, D., Stang, P.E., Overhage, J.M., Racoosin, J.A., Hartzema, A.G., Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med 31 (2012), 4401–4415.