In this seminar presented by the Department of Epidemiology & Biostatistics, we will discuss a linkage between the big-data analysis and the classical meta-analysis. In data-rich big-data analysis, a commonly used approach is the so-called “Divide-and-Recombine”, which is linked with the classical “data-poor” statistical meta-analysis. We review the classical fixed-effects and random-effects meta-analysis methods and further discuss the relative efficiency under a general likelihood inference setting.
Dr. Ding-Geng (Din) Chen
Executive Director and Professor in Biostatistics
College of Health Solutions, Arizona State University
*This talk is based on the publication: Chen, D.G, Liu, D., Min, X. and Zhang H. (2020). Relative efficiency of using summary and individual information in random-effects meta-analysis. Biometrics, 76(4): 119- 1329. (https://doi.org/10.1111/biom.13238)
Zoom link: arizona.zoom.us/j/84748481999