publication bias


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publication bias

(pŭb″lĭ-kā′shŭn) [L. publicare, to confiscate, make public]
The tendency of professional journals to publish and report the results of research trials that show a large positive clinical effect. Studies that demonstrate no association between variables or those that show neutral effects are less likely to be published or reported.

publication bias

The tendency to publish reports of research that appears to support a hypothesis and to refrain from publishing findings that do not, thereby creating opinions about the truth of the hypothesis that may be unduly optimistic.
References in periodicals archive ?
Publication bias Studies were missing from body of evidence, resulting in an over- or underestimate of true effects from exposure.
The number of included trials in this review was too small to conduct any sufficient additional analysis of publication bias.
Afterward, several exploratory analyses were conducted to detect the presence of publication bias.
It is also possible that the mixture of significant and nonsignificant findings offset potential publication bias for this study.
To assess the influence of publication bias on the findings, a funnel plot was constructed based on the 10 studies.
The second aim of Meta analysis is to examine the possibility of publication bias.
To investigate publication bias, a funnel plot was generated, using the datasets from the included clinical trials.
Publication bias was evaluated through funnel plots and Egger's linear regression test, with six studies demonstrating no publication bias.
In some cases, these may be trials with negative or nonsignificant results would introduce a publication bias, also known as file-drawer bias.
Tests for publication bias and heterogeneity did not reach statistical significance.
Previous surgical mortality estimates have been based mostly on self-report and literature review, with the risk of reporting bias, recall bias, and publication bias.
The existing literature is also limited by publication bias and many studies are underpowered to detect quality-of-life differences.

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