10.17863/CAM.57538
Samartsidis, Pantelis
0000-0002-4491-9655
Montagna, Silvia
Laird, Angela R
Fox, Peter T
Johnson, Timothy D
Nichols, Thomas E
0000-0002-4516-5103
Estimating the prevalence of missing experiments in a neuroimaging meta-analysis.
Wiley
2020
meta-analysis
neuroimaging
publication-bias
zero-truncated modeling
Brain Mapping
Computer Graphics
Computer Simulation
Connectome
Data Interpretation, Statistical
Databases, Factual
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Meta-Analysis as Topic
Monte Carlo Method
Neuroimaging
Prevalence
Apollo - University of Cambridge Repository
University of Cambridge
013meh722
2020-09-17
2020-09-17
2020-11
eng
Article
https://www.repository.cam.ac.uk/handle/1810/310444
10.1002/jrsm.1448
All rights reserved
open.access
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/.