Power and reproducibility in the external validation of brain-phenotype predictions.

ABSTRACT: Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation

SUBMITTER: Rosenblatt M 

PROVIDER: S-EPMC10634903 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature