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ERIC Number: EJ1275045
Record Type: Journal
Publication Date: 2020-Nov
Pages: 18
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1759-2879
EISSN: N/A
Available Date: N/A
Estimating the Prevalence of Missing Experiments in a Neuroimaging Meta-Analysis
Samartsidis, Pantelis; Montagna, Silvia; Laird, Angela R.; Fox, Peter T.; Johnson, Timothy D.; Nichols, Thomas E.
Research Synthesis Methods, v11 n6 p866-883 Nov 2020
Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple functional magnetic resonance imaging experiments with the goal of obtaining results that are more likely to generalize. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publication 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 modeling approach that allows us to estimate the prevalence of nonsignificant 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. The R code that we used is available at https://osf.io/ayhfv/.
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www-wiley-com.bibliotheek.ehb.be/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Institutes of Health (DHHS); National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: 5R01NS075066; R012R01EB01561104; R01DA041353; MH074457; 1631325
Author Affiliations: N/A