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• W3103169210 abstract "Abstract Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple fMRI experiments with the goal of obtaining results that are more likely to generalise. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publications bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero-truncated modelling approach that allows us to estimate the prevalence of non-significant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported." @default.
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• W3103169210 date "2017-11-27" @default.
• W3103169210 modified "2023-10-11" @default.
• W3103169210 title "Estimating the prevalence of missing experiments in a neuroimaging meta-analysis" @default.
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• W3103169210 doi "https://doi.org/10.1101/225425" @default.
• W3103169210 hasPublicationYear "2017" @default.
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