1 research outputs found
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, -consistency and Neuroscience Applications
Many studies in biomedical and health sciences involve small sample sizes due
to logistic or financial constraints. Often, identifying weak (but
scientifically interesting) associations between a set of predictors and a
response necessitates pooling datasets from multiple diverse labs or groups.
While there is a rich literature in statistical machine learning to address
distributional shifts and inference in multi-site datasets, it is less clear
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.Comment: 34th International Conference on Machine Learnin