Sparse and Efficient Estimation with Semiparametric Models in Meta-Analysis

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In many scientific fields, such as genomics, epidemiology, and economics, combining large-scale datasets of multiple studies is a valuable approach to fully utilizing the collected data. However, such studies often have privacy policies that prevent individual-level data sharing. In biomedical research, for example, while data integration can boost the power of evaluating risk factors of a disease, study protocols typically prohibit sharing participant-level genomic and clinical data among the studies. This project will investigate meta-analysis that combines studies using compressed information in summary statistics without requiring individual-level data. The PI plans to establish a broad framework with semiparametric regression models and to develop concrete and computationally efficient methods with theoretical guarantees. Both undergraduate and graduate students will receive training through involvement in the research project.The PI will study the general likelihood theory for meta-analysis with semiparametric regression. The theoretical framework to be established will embrace meta-analysis of studies with different observation schemes that generate various data types. The project will deal with both homogeneous and heterogeneous structures of meta-analysis. The PI will develop semiparametric methods based on summary statistics with the aim of efficient estimation and sparse structure recovery. In developing the methods, the research will focus on using and extending techniques such as least-squares approximation and regularization. Statistical properties and optimization algorithms of these methods will be studied under both structure types of meta-analysis. The resulting methods will be applicable to various studies such as large-scale public health studies, prognostic signature studies, and genome-wide association studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.