报告题目：New development for meta-analysis with few studies
报 告 人： 童铁军 教授 香港浸会大学
报告时间：2021年11月9日 上午 9:00-10:00
报告地点：腾讯会议 ID：251 431 869
或点击链接直接加入会议：https://meeting.tencent.com/dm/ApRMefwBzjeM
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：Meta-analysis is a statistical technique to synthesize the research findings from multiple studies for decision making. It has been increasingly popular in the past several decades, mainly due to its wide application in evidence-based practice. In meta-analysis, the two commonly used models include the common-effect model (CEM) and the random-effects model (REM). More recently, the fixed-effects model (FEM) is also recognized as an essential model for meta-analysis, especially when the number of studies is very small. FEM not only avoids the unrealistic assumption on a common effect in CEM when the heterogeneity exists, but also avoids the low estimation accuracy of the between-study variance in REM when there are only few studies. In this talk, I will give a brief introduction on some new developments related to meta-analysis with FEM, including the research outputs from our own group.
报告人简介：童铁军，香港浸会大学数学系教授，副系主任，国际统计协会当选会员。主要科研方向包括非参数回归模型、高维数据分析、Meta分析和循证医学。2005年于美国加州大学圣巴巴拉分校获得统计学博士学位，2005-2007年在耶鲁大学从事博士后研究，2007-2010年在科罗拉多大学博尔德分校担任助理教授，2010年至今任职于香港浸会大学。已在国际知名的学术期刊JASA、JMLR、Statistical Science等发表学术论文80余篇，并成功收编葫芦娃B5小分队（Biometrika、Biometrics、Biostatistics、Bernoulli、Bioinformatics）。此外，作为业余爱好（目前已不务正业）曾发表两篇ESI热点文章和三篇ESI高被引文章，单篇论文最高引用2900余次。
