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Misunderstanding publication bias: editors are not blameless after all.

ABSTRACT: In analysing whether there is an editorial bias in favour of positive studies, researchers have made implicit assumptions that are implausible. In particular, to justify the conclusion that there is no bias because observed editorial acceptance rates do not favour positive studies, the assumption that the decision to submit an article is based solely on quality would be required. If, on the other hand, submission were based on perceived probability of acceptance, negative and positive studies would not differ in terms of acceptance rates, but in terms of quality. It is shown, using a simple graphical model, how similar underlying situations as regards the relationship between quality and probability of acceptance on the one hand and study outcome (positive or negative) and probability of acceptance on the other could produce dramatically different results depending on the behaviour of authors. Furthermore, there is, in fact, some evidence that submitted negative studies are, on average, of higher quality than positive ones. This calls into question the standard interpretation of the studies examining editorial bias. It would appear that despite similar probabilities of acceptance for negative and positive studies, editors could be discriminating against negative studies.

SUBMITTER: Senn S

PROVIDER: S-EPMC3782365 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Misunderstanding publication bias: editors are not blameless after all.

Senn Stephen S

F1000Research 20121204

In analysing whether there is an editorial bias in favour of positive studies, researchers have made implicit assumptions that are implausible. In particular, to justify the conclusion that there is no bias because observed editorial acceptance rates do not favour positive studies, the assumption that the decision to submit an article is based solely on quality would be required. If, on the other hand, submission were based on perceived probability of acceptance, negative and positive studies wo ...[more]

PMID: 24358828

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Project description:Background:Publication bias is a form of scientific misconduct. It threatens the validity of research results and the credibility of science. Although several tests on publication bias exist, no in-depth evaluations are available that examine which test performs best for different research settings. Methods:Four tests on publication bias, Egger's test (FAT), p-uniform, the test of excess significance (TES), as well as the caliper test, were evaluated in a Monte Carlo simulation. Two different types of publication bias and its degree (0%, 50%, 100%) were simulated. The type of publication bias was defined either as file-drawer, meaning the repeated analysis of new datasets, or p-hacking, meaning the inclusion of covariates in order to obtain a significant result. In addition, the underlying effect (β = 0, 0.5, 1, 1.5), effect heterogeneity, the number of observations in the simulated primary studies (N = 100, 500), and the number of observations for the publication bias tests (K = 100, 1,000) were varied. Results:All tests evaluated were able to identify publication bias both in the file-drawer and p-hacking condition. The false positive rates were, with the exception of the 15%- and 20%-caliper test, unbiased. The FAT had the largest statistical power in the file-drawer conditions, whereas under p-hacking the TES was, except under effect heterogeneity, slightly better. The CTs were, however, inferior to the other tests under effect homogeneity and had a decent statistical power only in conditions with 1,000 primary studies. Discussion:The FAT is recommended as a test for publication bias in standard meta-analyses with no or only small effect heterogeneity. If two-sided publication bias is suspected as well as under p-hacking the TES is the first alternative to the FAT. The 5%-caliper test is recommended under conditions of effect heterogeneity and a large number of primary studies, which may be found if publication bias is examined in a discipline-wide setting when primary studies cover different research problems.

Project description:BackgroundSystematic reviews and meta-analyses of pre-clinical studies, in vivo animal experiments in particular, can influence clinical care. Publication bias is one of the major threats of validity in systematic reviews and meta-analyses. Previous empirical studies suggested that systematic reviews and meta-analyses have become more prevalent until 2010 and found evidence for compromised methodological rigor with a trend towards improvement. We aim to comprehensively summarize and update the evidence base on systematic reviews and meta-analyses of animal studies, their methodological quality and assessment of publication bias in particular.Methods/designThe objectives of this systematic review are as follows: •To investigate the epidemiology of published systematic reviews of animal studies until present. •To examine methodological features of systematic reviews and meta-analyses of animal studies with special attention to the assessment of publication bias. •To investigate the influence of systematic reviews of animal studies on clinical research by examining citations of the systematic reviews by clinical studies. Eligible studies for this systematic review constitute systematic reviews and meta-analyses that summarize in vivo animal experiments with the purpose of reviewing animal evidence to inform human health. We will exclude genome-wide association studies and animal experiments with the main purpose to learn more about fundamental biology, physical functioning or behavior. In addition to the inclusion of systematic reviews and meta-analyses identified by other empirical studies, we will systematically search Ovid Medline, Embase, ToxNet, and ScienceDirect from 2009 to January 2013 for further eligible studies without language restrictions. Two reviewers working independently will assess titles, abstracts, and full texts for eligibility and extract relevant data from included studies. Data reporting will involve a descriptive summary of meta-analyses and systematic reviews.DiscussionResults are expected to be publicly available later in 2013 and may form the basis for recommendations to improve the quality of systematic reviews and meta-analyses of animal studies and their use with respect to clinical care.

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Misunderstanding publication bias: editors are not blameless after all.

Publications

Misunderstanding publication bias: editors are not blameless after all.

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