Title: On estimation and selection for semiparametric models in meta-analysis
Authors: Sunyoung Shin - University of Texas at Dallas (United States) [presenting]
Abstract: Combining large-scale datasets of multiple studies is a valuable approach to fully utilizing the collected data. However, such studies often have privacy policies or data transfer issues that prevent individual-level data sharing. A meta-analysis combines large-scale datasets using compressed information in summary statistics without requiring individual-level data. We develop a general likelihood theory on meta-analysis with semiparametric models. The theoretical framework embraces meta-analysis of studies with different observation schemes that generate various data types. We propose a method of meta-estimation and selection based on summary statistics. The resulting estimator has desirable asymptotic properties under mild assumptions. The superior performance and practical utility of the proposed method are demonstrated through numerical studies.