Top 4 Bitss Papers Today in Medicine And Health Sciences


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#1. Mapping the Universe of Registered Reports
Tom E Hardwicke, john Ioannidis
Registered reports present a substantial departure from traditional publishing models with the goal of enhancing the transparency and credibility of the scientific literature. We map the evolving universe of registered reports to assess their growth, implementation, and shortcomings at journals across scientific disciplines.
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Authors: 2
Total Words: 6489
Unqiue Words: 2439

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#2. Publication Bias in Meta-Analyses from Psychology and Medicine: A Meta-Meta-Analysis
Robbie C. M. van Aert, Jelte Wicherts, Marcel A.L.M. van Assen
Publication bias is a substantial problem for the credibility of research in general and of meta-analyses in particular, as it yields overestimated effects and may suggest the existence of non-existing effects. Although there is consensus that publication bias is widespread, how strongly it affects different scientific literatures is currently less well-known. We examine evidence of publication bias in a large-scale data set of meta-analyses published in Psychological Bulletin (representing meta-analyses from psychology) and the Cochrane Database of Systematic Reviews (representing meta-analyses from medical research). Psychology is compared to medicine, because medicine has a longer history than psychology with respect to preregistration of studies as an effort to counter publication bias. The severity of publication bias and its inflating effects on effect size estimation were systematically studied by applying state-of-the-art publication bias tests and the p-uniform method for estimating effect size corrected for publication...
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DenOlmo: @chrisdc77 @BrianNosek However, two recent preprints based on meta-analyses in Psychological Bulletin and Cochrane Library find that there is not much evidence for publication bias. Very suprising, but that's what the data say. https://t.co/yi00SpPBAQ https://t.co/eDawOBymSf
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Authors: 3
Total Words: 16728
Unqiue Words: 4395

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#3. Developing a Guideline for Reporting Mediation Analyses (AGReMA) in Randomized Trials and Observational Studies
Katie Hoeberling, Hopin Lee, James McAuley, Aidan Cashin, Rob Herbert, Steven Kamper, Nicholas Henschke, Christopher Williams
Studies investigating causal mechanisms are becoming increasingly common. Despite growing numbers of publications and trialist embedding mechanism evaluations into randomised controlled trials and observational studies, the reporting accuracy and consistency of mechanism studies is suboptimal. The heterogeneity in the reporting of mechanism evaluations stifles systematic reviews, complicates meta-analyses, and limits transparency and replication. The aim of this initiative is to develop a reporting guideline for mechanism evaluations (mediation analyses). We also plan to develop an accompanying explanation and elaboration (E&E) paper. This project is the first initiative to address the issue of poor reporting quality of mechanism evaluations and has been registered on the “Enhancing the QUAlity and Transparency of health Research” (EQUATOR) network. This initiative is being conducted in accordance with the Guidance for Developers of Health Research Reporting Guidelines. This draft report provides an update on the progress of...
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Authors: 8
Total Words: 5439
Unqiue Words: 1816

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#4. True and False Positive Rates for Different Criteria of Evaluating Statistical Evidence from Clinical Trials
Don van Ravenzwaaij, john Ioannidis
Until recently a typical rule that has often been often used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive (endorsement of an ineffective treatment) rates. In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, and thresholds for what constitutes a clinically meaningful treatment effect. Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when trials have small sample sizes and clinically meaningful effects are not small, particularly...
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Equanimous_1: van Ravenzwaaij, Don, and john Ioannidis. “True and False Positive Rates for Different Criteria of Evaluating Statistical Evidence from Clinical Trials.” BITSS, Dec. 2018. DataCite. https://t.co/yXJWlQ4DjZ
SMirandaField: A new preprint worth checking out: @DonVanRaven & Ioannidis present a simulation study comparing true and false positive rates for different criteria of evaluating statistical evidence in biomed - https://t.co/ixp1KW1Gp3
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Sample Sizes : [20, 50, 100, 400, 20, 20]
Authors: 2
Total Words: 5792
Unqiue Words: 1791

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