When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, ℓ 2-consistency and Neuroscience Applications.
Year: 2017
Type: article
Abstract: such pooling is guaranteed to help (and when it does not) - independent of the inference algorithms we use. In this paper, we present a hypothesis test to answer this question, both for classical and high dimensional linear regression. We precisely identify regimes where pooling datasets across multiple sites is sensible, and how such policy decisions can be made via simple checks executable on each site before any data transfer ever happens. With a focus on Alzheimer's disease studies, we present empirical results showing that in regimes suggested by our analysis, pooling a local dataset with data from an international study improves power.
Source: PubMed
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Maximizing Power in AD Clinical Trials via Multimodal Machine Learning, Institutional Clinical and Translational Science Award (UW-Madison)(UL1), The Center for Predictive Computational Phenotyping-1 Overall, Manifold-valued statistical models for longitudinal morphometic analysis in preclinical Alzheimer's disease (AD), Wisconsin Alzheimer's Disease Research Center, The Longitudinal Course of Imaging Biomarkers in People At Risk for Alzheimer’s Disease
OpenAlex ID: W2963286678