Dynamic impairment classification through arrayed comparisons
Dynamic impairment classification through arrayed comparisons
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DOI：
10.1002/sim.9601
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发表时间：
2022-11-01
影响因子：
2
通讯作者：
Becker，James T.
中科院分区：
文献类型：
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作者：
Wang，Zheng;Wang，Zi;Becker，James T.
多变量规范性比较（MNC）方法已被用于识别认知功能障碍。当参与者的认知脑域定期评估，纵向MNC（LMNC）已被引入，以纠正在同一参与者的多个认知域的重复评估之间的相互关系。然而，等到研究结束时进行诊断可能不切实际。例如，在多中心艾滋病队列研究（MACS）的参与者中，认知功能已经被反复评估了35年以上。因此，最好在每次评估时识别认知功能障碍，而未来的评估数量未知，从而控制家庭错误率（FWER）。在这项工作中，我们建议使用连续LMNC检验统计量的差异来构造独立的测试。频率建模可以帮助预测每个参与者将有多少评估，因此Bonferroni类型的校正可以很容易地适应。在多元正态性假设下使用卡方检验，在违反该假设的情况下提出排列检验。我们通过模拟和MACS数据表明，我们的方法将FWER控制在预定水平以下。
The multivariate normative comparison (MNC) method has been used for identifying cognitive impairment. When participants' cognitive brain domains are evaluated regularly, the longitudinal MNC (LMNC) has been introduced 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 (FWER) is controlled with unknown number of assessments in future. In this work, 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 Bonferroni‐type correction can be easily adapted. A chi‐squared test is used under the assumption of multivariate normality, and permutation test is proposed where this assumption is violated. We showed through simulation and the MACS data that our method controlled FWER below a predetermined level.