Projection-based two-sample inference for sparsely observed multivariate functional data.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Salil Koner, Sheng Luo
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引用次数: 0

Abstract

Modern longitudinal studies collect multiple outcomes as the primary endpoints to understand the complex dynamics of the diseases. Oftentimes, especially in clinical trials, the joint variation among the multidimensional responses plays a significant role in assessing the differential characteristics between two or more groups, rather than drawing inferences based on a single outcome. We develop a projection-based two-sample significance test to identify the population-level difference between the multivariate profiles observed under a sparse longitudinal design. The methodology is built upon widely adopted multivariate functional principal component analysis to reduce the dimension of the infinite-dimensional multi-modal functions while preserving the dynamic correlation between the components. The test applies to a wide class of (non-stationary) covariance structures of the response, and it detects a significant group difference based on a single p-value, thereby overcoming the issue of adjusting for multiple p-values that arise due to comparing the means in each of components separately. Finite-sample numerical studies demonstrate that the test maintains the type-I error, and is powerful to detect significant group differences, compared to the state-of-the-art testing procedures. The test is carried out on two significant longitudinal studies for Alzheimer's disease and Parkinson's disease (PD) patients, namely, TOMMORROW study of individuals at high risk of mild cognitive impairment to detect differences in the cognitive test scores between the pioglitazone and the placebo groups, and Azillect study to assess the efficacy of rasagiline as a potential treatment to slow down the progression of PD.

基于投影的稀疏观测多变量函数数据的双样本推断。
现代纵向研究收集多种结果作为主要终点,以了解疾病的复杂动态。通常情况下,特别是在临床试验中,多维反应之间的联合变化在评估两个或多个组之间的差异特征方面发挥着重要作用,而不是根据单一结果进行推断。我们开发了一种基于投影的双样本显著性检验,以确定在稀疏纵向设计下观察到的多变量特征之间的群体水平差异。该方法建立在广泛采用的多元函数主成分分析的基础上,以降低无限维多模态函数的维度,同时保留各成分之间的动态相关性。该检验适用于反应的多种(非平稳)协方差结构,而且只需一个 p 值就能检测出显著的组间差异,从而克服了因分别比较各分量的均值而产生的多个 p 值的调整问题。有限样本数值研究表明,与最先进的检验程序相比,该检验保持了 I 型误差,并能有力地检测出显著的组间差异。该检验在两项针对阿尔茨海默病和帕金森病(PD)患者的重要纵向研究中进行,即针对轻度认知障碍高危人群的 TOMMORROW 研究,以检测吡格列酮组和安慰剂组之间认知测试得分的差异;以及 Azillect 研究,以评估拉沙吉兰作为一种潜在治疗方法对延缓帕金森病进展的疗效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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