Academic

With the increasing availability of data in the public domain, there has been a growing interest in exploiting information from various sources to facilitate the decision-making processes. However, in real-world applications, particularly those dealing with sensitive areas such as healthcare and finance, individual-level data are often unavailable, leaving only aggregate data from external sources. In this talk, I will demonstrate how one can leverage the external aggregate data to improve the estimation efficiency in smaller-scale studies via the empirical likelihood framework. This approach can accommodate the heterogeneity and uncertainty in external information simultaneously. Under a similar framework, I will also introduce a novel approach for transporting evidence from clinical studies to target populations using only covariate summary statistics to account for distributional shifts and uncertainty in the external information. Conditions to ensure the validity of the proposed estimators will be examined.