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Excess significance bias in the literature on brain volume abnormalities.

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TLDR
There are too many studies with statistically significant results in the literature on brain volume abnormalities that suggest strong biases in the Literature, with selective outcome reporting and selective analyses reporting being possible explanations.
Abstract
Context Many studies report volume abnormalities in diverse brain structures in patients with various mental health conditions. Objective To evaluate whether there is evidence for an excess number of statistically significant results in studies of brain volume abnormalities that suggest the presence of bias in the literature. Data Sources PubMed (articles published from January 2006 to December 2009). Study Selection Recent meta-analyses of brain volume abnormalities in participants with various mental health conditions vs control participants with 6 or more data sets included, excluding voxel-based morphometry. Data Extraction Standardized effect sizes were extracted in each data set, and it was noted whether the results were“positive” (P  Data Synthesis From 8 articles, 41 meta-analyses with 461 data sets were evaluated (median, 10 data sets per meta-analysis) pertaining to 7 conditions. Twenty-one of the 41 meta-analyses had found statistically significant associations, and 142 of 461 (31%) data sets had positive results. Even if the summary effect sizes of the meta-analyses were unbiased, the expected number of positive results would have been only 78.5 compared with the observed number of 142 (P  Conclusion There are too many studies with statistically significant results in the literature on brain volume abnormalities. This pattern suggests strong biases in the literature, with selective outcome reporting and selective analyses reporting being possible explanations.

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Citations
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Grey and White Matter Alterations in Juvenile Myoclonic Epilepsy: A Comprehensive Review.

TL;DR: Converging evidence from neuroimaging studies strongly suggests that JME is a predominantly thalamofrontal network epilepsy, challenging the traditional concept of JME as a generalized epilepsy.
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Examining reproducibility in psychology: A hybrid method for combining a statistically significant original study and a replication

TL;DR: It is demonstrated that the conclusions based on the hybrid method are often in line with those of the replication, suggesting that many published psychological studies have smaller effect sizes than those reported in the original study, and that some effects may even be absent.
References
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Journal ArticleDOI

Measuring inconsistency in meta-analyses

TL;DR: A new quantity is developed, I 2, which the authors believe gives a better measure of the consistency between trials in a meta-analysis, which is susceptible to the number of trials included in the meta- analysis.
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Quantifying heterogeneity in a meta‐analysis

TL;DR: It is concluded that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity, and one or both should be presented in publishedMeta-an analyses in preference to the test for heterogeneity.

Why Most Published Research Findings Are False

TL;DR: In this paper, the authors discuss the implications of these problems for the conduct and interpretation of research and suggest that claimed research findings may often be simply accurate measures of the prevailing bias.
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The combination of estimates from different experiments.

TL;DR: The problem of making a combined estimate has been discussed previously by Cochran and Yates and Cochran (1937) for agricultural experiments, and by Bliss (1952) for bioassays in different laboratories as discussed by the authors.
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The Handbook of Research Synthesis.

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