Publication bias
Publication bias arises from the tendency for researchers and editors to handle experimental results that are positive (they found something) differently from results that are negative (found that something did not happen) or inconclusive.
Publication bias has been documented to occur in studies of medical interventions. Publication bias, or the related outcome reporting bias (see below), may occur in 25% to 60% of some types of articles.
Publication bias may also occur in studies of diagnostic tests. Publication bias may be more of a problem in diagnostic test research than in randomized controlled trials because studies of diagnostic tests can be secondary analyses of databases and do not have to be registered prior to publication.
There may be publication bias in studies of publication bias.

# Definition
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. A related term, "the file drawer problem", refers to the tendency for those negative or inconclusive results to remain hidden and unpublished.
Even a small number of studies lost in the file drawer can 
result in a significant bias..

# Selective reporting bias
Selective reporting bias, or 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.
Selective reporting also occurs when "publications ignored the results of intention to treat analyses and reported the more favourable per protocol analyses only."
Related terms that have been coined are p-hacking and HARKing (Hypothesizing After the Results are Known).
Omitted-variable bias occurs when an "adjusting variable has an own effect on the dependent variable and is correlated with the variable of interest, excluding this adjusting variable from the regression induces omitted-variable bias".
Industry publications may be more likely to interpret their results as favorable to their product although analyses of the primary outcome may be less supportive>
For example, skeptics often argue that there is (or at least was) a strong publication bias in the field of parapsychology, leading to a File drawer problem.

# Small study effect
The small study effect is closely related. The small study effect is the observation that small studies tend to report more positive results. This is especially a threat when the original studies in a meta-analysis are less than 50 patients in size.

# Examples
Suppose that several studies about the influence of power lines on cancer are performed. They are admitted for publication only if they show a correlation with a 95%  confidence level. If only the positive results make it to publication,  because negative results are just shelved, we do not know how many studies were performed, so it is possible that all the published results are type I errors.

# Detection
P-curves may or may not be able to detect p-hacking.
The caliper test may be able to detect publication biases

# 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 be 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 funnel plot to quantify the effects of bias.

# Possible examples
An example of probable publication bias is in the studies of glucosamine and chondroitin for treatment of osteoarthritis. In an initial meta-analysis, the authors noted evidence of publication bias during examination of the results. A subsequent large randomized controlled trial and meta-analyses including the large trial were negative.
Another example is the selective publication of randomized controlled trials of antidepressants or of positive trials in general.
One study 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". 
One possible interpretation of this result is  selective publication (publication bias).
Ioannidis has inventoried factors that should alert readers to risks of publication bias .

# Prevention

## 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 research unless that research was registered in a public database from the start.
In this way, negative results should no longer be able to disappear.

## Independent data analysis
COnstraints on authors by industry are common.
In 2002, the Journal of the American Medical Association, (JAMA), added to its instructions to authors, "For industry-sponsored research studies, an investigator who is not an employee of the sponsoring company, and who ideally is the principal investigator, should provide a statement that he or she "had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analyses."
In 2010, JAMA added "Moreover, in industry-sponsored studies, data collection and data management should be conducted primarily or solely by the academic investigators independently of the study sponsor or other for-profit research organization, and with additional monitoring and oversight, such as under the auspices of an academic independent data and safety monitoring committee."
In 2012, JAMA stated, "all reports containing original data, regardless of funding source, analysis of the data must be conducted by a statistician at an academic institution, rather than by statisticians employed by the sponsor or by a commercial contract research organization. The biostatistician should have full access to the entire raw data set and must be a faculty member at a medical school or academic center (such as a university) or an employee of a government research institute, such that the academic organization has oversight over the person conducting the analysis"
This policy resulted in JAMA publishing fewer trials and fewer industry-funded trials.
Accordingly, SPIRIT guidelines for trial protocols includes:
- Item 29: Statement of who will have access to the final trial dataset, and disclosure of contractual agreements that limit such access for investigators
Accordingly, the International Committee of Medical Journal Editors (ICMJE) has:
- Recommended "Authors should avoid entering into agreements with study sponsors, both for-profit and nonprofit, that interfere Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals with authors access to all of the studys data or that interfere with their ability to analyze and interpret the data and to prepare and publish manuscripts independently when and where they choose".
- Suggested that journals add requests of their authors to state I had full access to all of the data in this study and I take complete responsibility for the integrity of the data and the accuracy of the data analysis.
However, most medical schools do not examine contracts between academics and industry.
Academics report conflict and lack of data access.