Data Integration With Biased Summary Data via Generalized Entropy Balancing

Music City Center 

Statistical methods for integrating individual-level data with external summary data have attracted attention due to their potential to significantly reduce data collection costs. Effective utilization of summary data can enhance estimation precision, thereby saving both time and resources. However, incorporating external data introduces the risk of bias, primarily due to potential differences in background distributions between the current study and the external source.
Model-based approaches, such as mass imputation and propensity score balancing, have been developed to integrate external summary data with internal individual-level data while mitigating these biases. Nonetheless, these methods remain vulnerable to bias from model misspecification.
In this paper, we propose a methodology utilizing generalized entropy balancing, designed to integrate external summary data even when derived from biased samples. Our method exhibits double robustness, offering enhanced protection against specific types of model misspecification.
We illustrate the versatility and effectiveness of our proposed estimator through an application to the analysis of Nationwide Public-Access Defibrillation data in Japan.