Leveraging External Information from a Different Outcome Model with the Current Study

Music City Center 

Leveraging external information from related studies can improve prediction accuracy with insufficient data. However, conventional methods only consider incorporating information from the external data with the same outcome. In this paper, we develop an integration framework for the settings where the external and internal data are relevant but may be subject to different types of outcomes. The proposed framework utilizes the generic structure of certain models to bridge the different outcomes and introduces the statistics distance information to characterize the heterogeneity across different populations and outcomes. Illustrative examples discussed in this paper include the integration of continuous outcome data with binary outcome data and the integration of discrete survival outcome data with continuous survival outcome data. We evaluate the performance of the proposed method through comprehensive numerical simulations. We apply the proposed framework to multiple analyses of the acute kidney injury (AKI) study in populations who received immune checkpoint inhibitor (ICI) treatments.

data integration

outcome heterogeneity

population heterogeneity


Kullback-Leibler information 

Biometrics Section