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The integration and use of data from multiple studies, for the development ofprediction models is an important task in several scientific fields. We proposea framework for generalist and specialist predictions that leverages multipledatasets, with potential differences in the relationships between predictorsand outcomes. Our framework uses stacking, and it includes three majorcomponents: 1) an ensemble of prediction models trained on one or moredatasets, 2) task-specific utility functions and 3) a no-data-reuse techniquefor estimating stacking weights. We illustrate that under mild regularityconditions the framework produces stacked PFs with oracle properties. Inparticular we show that the the stacking weights are nearly optimal. We alsocharacterize the scenario where the proposed no-data-reuse technique increasesprediction accuracy compared to stacking with data reuse in a special case.Weperform a simulation study to illustrate these results. We apply our frameworkto predict mortality using a collection of datasets on long-term exposure toair pollutants.

Cross-study learning for generalist and specialist predictions