European Journal of Epidemiology

, Volume 20, Issue 9, pp 739–745 | Cite as

Molecular Bias

  • John P.A. IoannidisEmail author


Bias is ubiquitous in research. The advent of the molecular era provides a unique opportunity to study the consequences of bias with large-scale empirical evidence accumulated in the massive data produced by the current discovery-oriented scientific effort, rather than just with theoretical speculations and constructs. Here I discuss some empirical evidence about manifestations of bias in molecular epidemiology. Bias may manifest as either heterogeneity or as deviation from the true estimates. The failure to translate molecular knowledge and the failure to replicate information are some typical hallmarks of bias at action. The acquired knowledge about the behaviour and manifestations of bias in molecular fields can be transferred back also to more traditional fields of epidemiology and medical research. Getting rid of false claims of the past is at least as important as producing new scientific discoveries. In many fields, the observed effects sizes that circulate as established knowledge are practically estimating only the net bias that has operated in the field all along. Issues of plausibility (in particular biological plausibility), replication, and credibility that form the theoretical basis of epidemiology and etiological inference can now be approached with large-scale empirical data.


Bias Replication Validity Research Molecular Medicine 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sackett, DL 1979Bias in analytic researchJ Chronic Dis325163CrossRefPubMedGoogle Scholar
  2. 2.
    Olkin, I 1995Meta-analysis: Reconciling the results of independent studiesStat Med14457472PubMedGoogle Scholar
  3. 3.
    Lau, J, Ioannidis, JP, Schmid, CH 1997Quantitative synthesis in systematic reviewsAnn Intern Med127820826PubMedGoogle Scholar
  4. 4.
    Contopoulos-Ioannidis, DG, Ntzani, E, Ioannidis, JP 2003Translation of highly promising basic science research into clinical applicationsAm J Med114477484CrossRefPubMedGoogle Scholar
  5. 5.
    Ioannidis, JP 2004Materializing research promises: Opportunities, priorities and conflicts in translational medicineJ Transl Med25CrossRefPubMedGoogle Scholar
  6. 6.
    Ransohoff, DF 2005Bias as a threat to the validity of cancer molecular-marker researchNat Rev Cancer5142149CrossRefPubMedGoogle Scholar
  7. 7.
    Ransohoff, DF 2004Rules of evidence for cancer molecular-marker discovery and validationNat Rev Cancer4309314CrossRefPubMedGoogle Scholar
  8. 8.
    Ioannidis, JP, Ntzani, EE, Trikalinos, TA, Contopoulos-Ioannidis, DG 2001Replication validity of genetic association studiesNat Genet29306309CrossRefPubMedGoogle Scholar
  9. 9.
    Khoury, MJ, Little, J 2000Human genome epidemiologic reviews: The beginning of something HuGEAm J Epidemiol15123PubMedGoogle Scholar
  10. 10.
    Ioannidis, JP 2003Genetic associations: False or true?Trends Mol Med9135138CrossRefPubMedGoogle Scholar
  11. 11.
    Trikalinos, TA, Ntzani, EE, Contopoulos-Ioannidis, DG, Ioannidis, JP 2004Establishment of genetic associations for complex diseases is independent of early study findingsEur J Hum Genet12762769CrossRefPubMedGoogle Scholar
  12. 12.
    Trikalinos, TA, Churchill, R, Ferri, M,  et al. 2004Effect sizes in cumulative meta-analyses of mental health randomized trials evolved over timeJ Clin Epidemiol.5711241130CrossRefPubMedGoogle Scholar
  13. 13.
    Ioannidis, JP 2005Contradicted and initially stronger effects in highly cited clinical researchJAMA294218228CrossRefPubMedGoogle Scholar
  14. 14.
    Lawlor, DA, Davey Smith, G, Kundu, D, Bruckdorfer, KR, Ebrahim, S 2004Those confounded vitamins: What can we learn from the differences between observational versus randomised trial evidence?Lancet36317241727CrossRefPubMedGoogle Scholar
  15. 15.
    Rossouw, JE, Anderson, GL, Prentice, RL,  et al. 2002Risks and benefits of estrogen plus progestin in healthy postmenopausal women: Principal results From the Women’s Health Initiative randomized controlled trialJAMA288321333PubMedGoogle Scholar
  16. 16.
    Wacholder, S, Chanock, S, Garcia-Closas, M, El Ghormli, L, Rothman, N 2004Assessing the probability that a positive report is false: An approach for molecular epidemiology studiesJ Natl Cancer Inst96434442PubMedGoogle Scholar
  17. 17.
    Rebbeck, TR, Spitz, M, Wu, X 2004Assessing the function of genetic variants in candidate gene association studiesNat Rev Genet5589597CrossRefPubMedGoogle Scholar
  18. 18.
    Lau, J, Ioannidis, JP, Schmid, CH 1998Summing up evidence: One answer is not always enoughLancet351123127CrossRefPubMedGoogle Scholar
  19. 19.
    Ioannidis JPA. Differentiating biases from genuine heterogeneity: Distinguishing artifactual from substantive effects. In: Rothstein HR, Sutton AJ, Borenstein M (eds), Publication bias in meta-analysis: Prevention, assessment and adjustments. John Wiley and Sons, New York: 2005.Google Scholar
  20. 20.
    Egger, M, Davey Smith, G, Schneider, M, Minder, C 1997Bias in meta-analysis detected by a simple, graphical testBMJ315629634PubMedGoogle Scholar
  21. 21.
    Ioannidis, J, Lau, J 2001Evolution of treatment effects over time: Empirical insight from recursive cumulative metaanalysesProc Natl Acad Sci USA98831836CrossRefPubMedGoogle Scholar
  22. 22.
    Ioannidis, JP, Contopoulos-Ioannidis, DG, Lau, J 1999Recursive cumulative meta-analysis: A diagnostic for the evolution of total randomized evidence from group and individual patient dataJ Clin Epidemiol52281291CrossRefPubMedGoogle Scholar
  23. 23.
    Ioannidis, JP, Trikalinos, TA, Ntzani, EE, Contopoulos-Ioannidis, DG 2003Genetic associations in large versus small studies: An empirical assessmentLancet361567571CrossRefPubMedGoogle Scholar
  24. 24.
    Ioannidis, JP, Trikalinos, TA 2005Early extreme contradictory estimates may appear in published research: The Proteus phenomenon in molecular genetics research and randomized trialsJ Clin Epidemiol58543549CrossRefPubMedGoogle Scholar
  25. 25.
    Kyzas, PA, Loizou, KT, Ioannidis, JP 2005Selective reporting biases in cancer prognostic factor studiesJ Natl Cancer Inst9710431055PubMedGoogle Scholar
  26. 26.
    Ioannidis, JP 2005Why most published research findings are falsePLoS Med2e124CrossRefPubMedGoogle Scholar
  27. 27.
    Angelis, CD, Drazen, JM, Frizelle, FA,  et al. 2005Is this clinical trial fully registered? A statement from the International Committee of Medical Journal EditorsAnn Intern Med143146148PubMedGoogle Scholar
  28. 28.
    Smith, R 2005Medical journals are an extension of the marketing arm of pharmaceutical companiesPLoS Med2e138CrossRefPubMedGoogle Scholar
  29. 29.
    Ioannidis, JP, Bernstein, J, Boffetta, P,  et al. 2005A network of investigator networks in human genome epidemiologyAm J Epidemiol162302304CrossRefPubMedGoogle Scholar
  30. 30.
    Ioannidis, JP, Rosenberg, PS, Goedert, JJ, O’Brien, TR 2002International meta-analysis of HIV host genetics. Commentary: Meta-analysis of individual participants’ data in genetic epidemiologyAm J Epidemiol156204210CrossRefPubMedGoogle Scholar
  31. 31.
    Ioannidis JPA. Grading the credibility of molecular evidence for complex diseases. Int J Epidemiol 2005 (in press).Google Scholar
  32. 32.
    Michiels, S, Koscielny, S, Hill, C 2005Prediction of cancer outcome with microarrays: A multiple random validation strategyLancet365488492PubMedGoogle Scholar
  33. 33.
    Ioannidis, JP 2005Microarrays and molecular research: Noise discovery?Lancet.365454455PubMedGoogle Scholar
  34. 34.
    Ntzani, EE, Ioannidis, JP 2003Predictive ability of DNA microarrays for cancer outcomes and correlates: An empirical assessmentLancet36214391444CrossRefPubMedGoogle Scholar
  35. 35.
    Ransohoff, DF 2005Lessons from controversy: Ovarian cancer screening and serum proteomicsJ Natl Cancer Inst97315319PubMedGoogle Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  1. 1.Department of Hygiene and EpidemiologyUniversity of Ioannina School of MedicineIoanninaGreece

Personalised recommendations