Wang Zheng, Cheng Yu, Seaberg Eric C, Rubin Leah H, Levine Andrew J, Becker James T
Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Stat Med. 2021 Mar 15;40(6):1440-1452. doi: 10.1002/sim.8850. Epub 2020 Dec 9.
Motivated by the Multicenter AIDS Cohort Study (MACS), we develop classification procedures for cognitive impairment based on longitudinal measures. To control family-wise error, we adapt the cross-sectional multivariate normative comparisons (MNC) method to the longitudinal setting. The cross-sectional MNC was proposed to control family-wise error by measuring the distance between multiple domain scores of a participant and the norms of healthy controls and specifically accounting for intercorrelations 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 family-wise error rate due to multiple testing over repeated visits. Thus, we propose longitudinal MNC procedures that are constructed based on multivariate mixed effects models. A 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 family-wise error at a predetermined level. A dataset from a neuropsychological substudy of the MACS is used to illustrate the applications of our proposed classification procedures.
受多中心艾滋病队列研究（MACS）的启发，我们基于纵向测量开发了认知障碍的分类程序。为了控制家族性错误率，我们将横断面多元规范比较（MNC）方法应用于纵向研究。横断面MNC方法是通过测量参与者多个领域得分与健康对照规范之间的距离，并特别考虑所有领域得分之间的相互关系来控制家族性错误率。然而，在纵向研究中，领域得分会被多次记录，由于在重复访视中进行多次检验，在每次访视时应用横断面MNC仍会使家族性错误率膨胀。因此，我们提出了基于多元混合效应模型构建的纵向MNC程序。一种检验程序是从横断面MNC改编而来，用于对纵向多元正态数据进行损伤分类。同时，提出了一种置换程序来处理偏态数据。通过模拟，我们表明我们的方法可以在预定水平上有效地控制家族性错误率。MACS神经心理学子研究的一个数据集用于说明我们提出的分类程序的应用。
