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Publication bias arises from the tendency for researchers, editors, and pharmaceutical companies to handle the reporting of experimental results that are positive (i.e. they show a significant finding) differently from results that are negative (i.e. supporting the null hypothesis) or inconclusive.

In an effort to decrease this problem some prominent medical journals require registration of a trial before it commences so that unfavorable results are not withheld from publication.


Publication bias occurs when the publication of research results depends on their nature and direction.[1]

Positive results bias, a type of publication bias, occurs when authors are more likely to submit, or editors accept, positive than null (negative or inconclusive) results.[2] A related term, "the file drawer problem", refers to the tendency for negative or inconclusive results to remain unpublished by their authors.[3]

Outcome reporting bias occurs when several outcomes within a trial are measured but these are reported selectively depending on the strength and direction of those results. A related term that has been coined is HARKing (Hypothesizing After the Results are Known).[4]

The file drawer effect

The file drawer effect, or file drawer problem, is that many studies in a given area of research may be conducted but never reported, and those that are not reported may on average report different results from those that are reported. An extreme scenario is that a given null hypothesis of interest is in fact true, i.e. the association being studied does not exist, but the 5% of studies that by chance show a statistically significant result are published, while the remaining 95% where the null hypothesis was not rejected languish in researchers' file drawers. Even a small number of studies lost "in the file drawer" can result in a significant bias.[5].

The term was coined by the psychologist Robert Rosenthal in 1979.[6]

Effect on meta-analysis

The effect of this is that published studies may not be truly representative of all valid studies undertaken, and this bias may distort meta-analyses and systematic reviews of large numbers of studies - on which evidence-based medicine, for example, increasingly relies. The problem may beTemplate:Weasel-inline particularly significant when the research is sponsored by entities that may have a financial interest in achieving favourable results.

Those undertaking meta-analyses and systematic reviews need to take account of publication bias in the methods they use for identifying the studies to include in the review. Among other techniques to minimise the effects of publication bias, they may need to perform a thorough search for unpublished studies, and to use such analytical tools as a Begg's funnel plot or Egger's plot to quantify the potential presence of publication bias. Tests for publications bias rely on the underlying theory that small studies with small sample size (and large variance) would be more prone to publication bias, while large-scale studies would be less likely to escape public knowledge and more likely to be published regardless of significance of findings. Thus, when overall estimates are plotted against the variance (sample size), a symmetrical funnel is usually formed in the absence of publication bias, while a skewed asymmetrical funnel is observed in presence of potential publication bias.

Extending the funnel plot, the "Trim and Fill" method has also been suggested as a method to infer the existence of unpublished hidden studies, as determined from a funnel plot, and subsequently correct the meta-analysis by imputing the presence of missing studies to yield an unbiased pooled estimate.

Examples of publication bias

One study[7] compared Chinese and non-Chinese studies of gene-disease associations and found that "Chinese studies in general reported a stronger gene-disease association and more frequently a statistically significant result"[8]. One possible interpretation of this result is selective publication (publication bias).

Risks and remedies


According to researcher John Ioannidis, negative papers are most likely to be suppressed:[9]

  1. when the studies conducted in a field are smaller
  2. when effect sizes are smaller
  3. when there is a greater number and lesser preselection of tested relationships
  4. where there is greater flexibility in designs, definitions, outcomes, and analytical modes
  5. when there is greater financial and other interest and prejudice
  6. when more teams are involved in a scientific field in chase of statistical significance.

Ioannidis further asserts that "claimed research findings may often be simply accurate measures of the prevailing bias".


Ioannidis' remedies include:

  1. Better powered studies
    • Low-bias meta-analysis
    • Large studies where they can be expected to very definitive results or test major, general concepts
  2. Enhanced research standards including
    • Pre-registration of protocols (as for randomized trials)
    • Registration or networking of data collections within fields (as in fields where researchers are expected to generate hypotheses after collecting data)
    • Adopting from randomized controlled trials the principles of developing and adhering to a protocol.
  3. Considering, before running an experiment, what they believe the chances are that they are testing a true or non-true relationship.
    • Properly assessing the false positive report probability based on the statistical power of the test[10]
    • Reconfirming (whenever ethically acceptable) established findings of "classic" studies, using large studies designed with minimal bias

Study registration

In September 2004, editors of several prominent medical journals (including the New England Journal of Medicine, The Lancet, Annals of Internal Medicine, and JAMA) announced that they would no longer publish results of drug research sponsored by pharmaceutical companies unless that research was registered in a public database from the start.[11] In this way, negative results should no longer be able to disappear[dubious]. Furthermore, some journals, e.g. Trials, encourage publication of study protocols in their journals.[12]

See also


  1. K. Dickersin (March 1990). The existence of publication bias and risk factors for its occurrence. JAMA 263 (10): 1385–1359.
  2. D. L. Sackett (1979). Bias in analytic research. J Chronic Dis 32 (1-2): 51–63.
  3. Robert Rosenthal (May 1979). The file drawer problem and tolerance for null results. Psychological Bulletin 86 (3): 638–641.
  4. N. L .Kerr (1998). HARKing: Hypothesizing After the Results are Known. Personality and Social Psychology 2 (3): 196–217.
  5. Jeffrey D. Scargle (2000). Publication Bias: The "File-Drawer Problem" in Scientific Inference. Journal of Scientific Exploration 14 (2): 94–106.
  6. Rosenthal, Robert (1979). The file drawer problem and tolerance for null results. Psychological Bulletin 86: 638–641.
  7. Zhenglun Pan, Thomas A. Trikalinos, Fotini K. Kavvoura, Joseph Lau, John P. A. Ioannidis, "Local literature bias in genetic epidemiology: An empirical evaluation of the Chinese literature". PLoS Medicine, 2(12):e334, 2005 December.
  8. Jin Ling Tang, "Selection Bias in Meta-Analyses of Gene-Disease Associations", PLoS Medicine, 2(12):e409, 2005 December.
  9. Ioannidis J (2005). Why most published research findings are false. PLoS Med 2 (8): e124.
  10. Wacholder,S. (March 2004). Assessing the Probability That a Positive Report is False: An Approach for Molecular Epidemiology Studies. JNCI 96 (6): 434–42.
  11. includeonly>(The Washington Post). "Medical journal editors take hard line on drug research",, 2004-09-10. Retrieved on 2008-02-03.
  12. Instructions for Trials authors - Study protocol.

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