Integrating Aggregated and Individual-Level Data from Heterogeneous Data Sources

Thomas M. Menino Convention & Exhibition Center 

Causal inference across multiple data sources has the potential to improve the generalizability of scientific findings. Integrating data sources with partially observed outcomes or covariates provides a promising framework for enhancing estimation efficiency and external validity. Under random sampling from a common population, our previous work showed that integrating large incomplete datasets with summary-level data yields efficient, unbiased estimates. In this study, we propose a novel statistical framework for integrating summary-level data with the information of heterogeneous data sources. The proposed method estimates study-specific sampling weights based on the auxiliary information and uses them to recalibrate the estimating equations for the full model parameters. The performance of the proposed method will be evaluated under various sampling designs using simulation studies and applied to the reanalysis of data from U.S. cancer registries and summary-level odds ratio estimates of selected colorectal cancer (CRC) risk factors while relaxing the random sampling assumption.

Casual Inference

Data integration

Data fusion

Aggregated data

Propensity score

Sampling weight calibration 

Biometrics Section