Placing epidemiological results in the context of multiplicity and typical correlations of exposures

Journal of Epidemiology and Community Health

Volumn 68, Issue 11, 2014, Pages 1096-1100

  Patel, Chirag J ^a   Ioannidis, John P A ^b,c  

^a Harvard Medical School Center for Biomedical Informatics   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CORRELATION; DATA SET; DEMOGRAPHIC SURVEY; EPIDEMIOLOGY; HEALTH SURVEY;

ENVIRONMENTAL EXPOSURE; EPIDEMIOLOGY; HUMAN; NUTRITION; OUTCOME ASSESSMENT; PROCEDURES; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA;

DATA INTERPRETATION, STATISTICAL; ENVIRONMENTAL EXPOSURE; EPIDEMIOLOGIC RESEARCH DESIGN; EPIDEMIOLOGIC STUDIES; HUMANS; NUTRITION SURVEYS; OUTCOME ASSESSMENT (HEALTH CARE);

EID: 84911368398     PISSN: 0143005X     EISSN: 14702738     Source Type: Journal    
DOI: 10.1136/jech-2014-204195     Document Type: Article

References (40)

1. Wang Z, Klipfell E, Bennett BJ, et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2011;472:57-63.
2. Rappaport SM, Barupal DK, Wishart D, et al. The blood exposome and its role in discovering causes of disease. Environ Health Perspect 2014 doi: http://dx.doi.org/ 10.1289/ehp.1308015.
3. Idaghdour Y, Czika W, Shianna KV, et al. Geographical genomics of human leukocyte gene expression variation in southern Morocco. Nat Genet 2010;42:62-7.
4. Athersuch TJ. The role of metabolomics in characterizing the human exposome. Bioanalysis 2012;4:2207-12.
5. Wang TJ, Larson MG, Vasan RS, et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011;17:448-53.
6. Spira A, Beane J, Shah V, et al. Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc Natl Acad Sci USA 2004;101:10143-8.
7. Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 1985;122:51-65.
8. Sun M, Fernstrom JD, Jia W, et al. A wearable electronic system for objective dietary assessment. J Am Diet Assoc 2010;110:45-7.
9. Kerr J, Duncan S, Schipperijn J. Using global positioning systems in health research: a practical approach to data collection and processing. Am J Prev Med 2011;41:532-40.
10. Madan A, Cebrian ME, Lazer D, et al. Social Sensing to Model Epidemiological Behavior Change. Proceedings of ACM Ubicomp 2010.
11. Rappaport SM, Smith MT. Environment and disease risks. Science 2010;330:460-61.
12. Patel CJ, Bhattacharya J, Butte AJ. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS ONE 2010;5:e10746.
13. Patel CJ, Cullen MR, Ioannidis JP, et al. Systematic evaluation of environmental factors: persistent pollutants and nutrients correlated with serum lipid levels. Int J Epidemiol 2012;41:828-43.
14. Patel CJ, Ioannidis JPA. Studying the elusive environment in large-scale. J Am Med Assoc 2014. In press.
15. Patel CJ, Rehkopf DH, Leppert JT, 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 2013;42:1795-810.
16. Patel CJ, Yang T, Hu Z, et al. Investigation of maternal environmental exposures in association with self-reported preterm birth. Reprod Toxicol 2013;45C:1-7.
17. Tzoulaki I, Patel CJ, Okamura T, et al. A nutrient-wide association study on blood pressure. Circulation 2012;126:2456-64.
18. Hall MA, Dudek SM, Goodloe R, et al. Environment-wide association study (EWAS) for type 2 diabetes in the Marshfield Personalized Medicine Research Project Biobank. Pac Symp Biocomput 2014:200-11.
19. Lind PM, Riserus U, Salihovic S, et al. An environmental wide association study (EWAS) approach to the metabolic syndrome. Environ Int 2013;55:1-8.
20. Rappaport SM. Biomarkers intersect with the exposome. Biomarkers 2012;17:483-9.
21. Rhee EP, Cheng S, Larson MG, et al. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest 2011;121:1402-11.
22. Townsend MK, Clish CB, Kraft P, et al. Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clin Chem 2013;59:1657-67.
23. Ioannidis JPA. Why most published research findings are false. PLoS Med 2005;2: e124.
24. Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology 1990;1:43-6.
25. Dal-Re R, Ioannidis JP, Bracken MB, et al. Making prospective registration of observational research a reality. Sci Transl Med 2014;6:224cm1.
26. Sterne JA, Davey Smith G. Sifting the evidence-what's wrong with significance tests?. BMJ 2001;322:226-31.
27. Pearson TA, Manolio TA. How to interpret a genome-wide association study. J Am Med Assoc 2008;299:1335-44.
28. Ioannidis J, Loy EY, Poulton R, et al. Researching genetic versus nongenetic determinants of disease: a comparison and proposed unification. Sci Transl Med 2009;1:8.
29. Smith GD, Lawlor DA, Harbord R, et al. Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology. PLoS Med 2007;4:e352.
30. Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS). National health and nutrition examination survey data, 2003- 2004. Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention.
31. Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 2004;74:765-9.
32. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 1995;57: 289-300.
33. Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci USA 2003;100:9440-5.
34. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2014;37(Suppl 1):S81-90.
35. Chinn S. A simple method for converting an odds ratio to effect size for use in meta-analysis. Stat Med 2000;19:3127-31.
36. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology 1999;10:37-48.
37. Hernan MA, Hernandez-Diaz S, Werler MM, et al. Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology. Am J Epidemiol 2002;155:176-84.
38. Guyatt GH, Oxman AD, Vist GE, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008;336:924-6.
39. Ioannidis JPA. Why most discovered true associations are inflated. Epidemiology 2008;19:640-8.
40. Siontis GC, Ioannidis JP. Risk factors and interventions with statistically significant tiny effects. Int J Epidemiol 2011;40:1292-307.