报告题目: Multivariate normative comparisons for impairment classification based on retrospective longitudinal data

报告时间：2019年12月24日，星期二，上午10：30-11：:30

报告地点：数学楼二楼2-3会议室

报告摘要:

Motivated by the Multicenter AIDS Cohort Study (MACS), we develop classification procedures for cognitive impairment based on longitudinal measures. To control family-wise error rate (FWER), we adapt the cross-sectional multivariate normative comparisons (MNC) method to the longitudinal setting. The cross-sectional MNC was proposed to control FWER by measuring the distance between multiple domain scores of a participant and the norms of healthy controls and specifically accounting for inter-correlations among all domain scores. However, in a longitudinal setting where domain scores are recorded multiple times, applying the cross-sectional MNC at each visit will still have inflated FWER due to multiple testing over repeated visits. Thus, we propose longitudinal MNC procedures that are constructed based on multivariate mixed effects models. A Chi-square test procedure is adapted from the cross-sectional MNC to classify impairment on longitudinal multivariate normal data. Meanwhile, a permutation procedure is proposed to handle skewed data. Through simulations we show that our methods can effectively control FWER at a pre-determined level. A dataset from a neuropsychological substudy of the MACS is used to illustrate the applications of our proposed classification procedures.

报告人简介：

成玉，1999年本科毕业于中国科技大学，2001年获新加坡国立大学统计学硕士学位，2006年获威斯康辛大学统计学博士学位，现任匹兹堡大学副教授。她的研究兴趣包括：Dynamic treatment regimes and SMART, Disease classification, Risk evaluation and screening, Quantile association等，目前在Journal of the Royal Statistical Society Series B, Journal of the American Statistical Association, Biometrika等权威期刊发表学术论文80余篇，并担任Journal of Statistical Research, Lifetime Data Analysis等学术期刊的AE。