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新加坡国立大学栗家量副教授:Multi-category Diagnostic Accuracy based on Logistic Regression

发布时间:2018-09-01

主 题:Multi-category Diagnostic Accuracy based on Logistic Regression

主讲人:新加坡国立大学栗家量副教授

主持人:统计学院林华珍教授

时 间:2018年9月5日(星期三)下午3:00-4:00

地 点:弘远楼408会议室

主办单位:统计研究中心 统计学院  科研处

主讲人简介:

栗家量博士于中国科学 技术大学获得统计学学士学位,于美国威斯康星大学获得公共健康学硕十学位与统计学博士学位。现在任职于新加坡国立大学统计与应用概率系副教授。栗博士已发表论文120余篇,其中包括Annals of Statistics, JASA, Biometrics等统计学顶级杂志。栗博士著有一本Chapman&Hall CRC Press出版的专著Survival Analysis in Medicine and Genetics.根据Google Scholar上面今年的最新数据,他的引用量超过2000.他的h-index为25.栗博士担当过Biometrics与Lifetime Data Analysis等统计学杂志的副主编。栗家量曾在新加坡作为principal investigator主持了多个国家支持的研究项目。

详情请见其个人主页:https://blog.nus.edu.sg/jialiang/


摘要:

We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task. For qualitative response variables with more than two categories, many traditional accuracy measures such as sensitivity, specificity and area under the ROC curve are no longer applicable. In recent literature new diagnostic accuracy measures are introduced in medical research studies. In this paper, important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples. We offer problembased R code to illustrate how to perform these statistical computations step by step. We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics. Our program can be adapted to many classifiers among which logistic regression may be the most popular approach. We thus base our discussion and illustration completely on the logistic regression in this paper.