Dr. Yu Cheng

Professor and Acting Chair, Department of Statistics

University of Pittsburgh

ABSTRACT: The Multivariate Normative Comparison (MNC) method has been used for identifying cognitive impairment. In this talk, we will first introduce the longitudinal MNC method, which has been developed by Zheng et al. (2021) to correct for the intercorrelation among repeated assessments of multiple cognitive domains in the same participant. However, it may not be practical to wait until the end of study for diagnosis. For example, in participants of the Multicenter AIDS Cohort Study (MACS), cognitive functioning has been evaluated repeatedly for more than 35 years. Therefore, it is optimal to identify cognitive impairment at each assessment, while the family-wise error rate is controlled with an unknown number of assessments in the future. We propose to use the difference of consecutive LMNC test statistics to construct independent tests. Frequency modeling can help predict how many assessments each participant will have so that Bonferroni-type correction can be easily adapted. A Chi-squared test is used under the assumption of multivariate normality, and a permutation test is proposed where this assumption is violated. We show through simulation and the MACS data that our method controls family-wise error rate below a pre-determined level.