Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability.

ABSTRACT: Large external data sources may be available to augment studies that collect data to address a specific research objective. In this article we consider the problem of building regression models for prediction based on individual-level data from an "internal" study while incorporating summary information from an "external" big data source. We extend the work of Chatterjee et al (2016a) by introducing an adaptive empirical Bayes shrinkage estimator that uses the external summary-level information and the internal data to trade bias with variance for protection against departures in the conditional probability distribution of the outcome given a set of covariates between the two populations. We use simulation studies and a real data application using external summary information from the Pros

SUBMITTER: Estes JP 

PROVIDER: S-EPMC6529204 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature