Statistical tests and identifiability conditions for pooling and analyzing multisite datasets.

ABSTRACT: When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our techn

SUBMITTER: Zhou HH 

PROVIDER: S-EPMC5816202 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature