Meta-Analysis: Is There an Efficiency Gain by Using Original Data?
主 题: Meta-Analysis: Is There an Efficiency Gain by Using Original Data?
报告人: Danyu Lin（University of North Carolina）
时 间: 2014-06-12 16:00-17:00
地 点: 理科一号楼1114（统计中心活动）
Meta-analysis is widely used to synthesize the results of multiple studies. Although meta-analysis is traditionally carried out by combining the summary statistics of relevant studies, advances in technologies and communications have made it increasingly feasible to access the original data on individual participants. We investigate the relative efficiency of analyzing original data versus combining summary statistics. We show that, for all commonly used parametric and semiparametric models, there is no asymptotic efficiency gain by analyzing original data if the parameter of main interest has a common value across studies; when the parameter of main interest follows a random-effect distribution, the maximum likelihood estimation of original data can be even less efficient than combining summary statistics. We demonstrate these theoretical results using both simulated and real data.
About the speaker
Danyu Lin is the Dennis Gillings Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill. Professor Lin is an internationally renowned statistician who has made fundamental contributions to many areas of statistics, including survival analysis and statistical genetics. He has published over 150 peer-reviewed papers, most of which appeared in top statistical journals. Several of his methods have been incorporated into commercial software packages, such as SAS, S-Plus and STATA, and widely used in practice. Professor Lin is on Thomson ISI\'s list of Highly Cited Researchers in Mathematics. He is a former recipient of the Mortimer Spiegelman Gold Medal from the American Public Health Association and a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. He currently serves as an Associate Editor of Biometrika and Journal of the American Statistical Association.