Dynamical correlation for multivariate longitudinal data

• 成果类型: Article
• 署名作者: Dubin, JA; Müller, HG
• 署名单位: Yale University; University of California System; University of California Davis
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001989
• 发表日期: 2005
• 页码: 872-881
• 关键词: additive regression-model; canonical-analysis; time

摘要

Nonparametric methodology for longitudinal data analysis is becoming increasingly popular. The analysis of multivariate longitudinal data, where data on several time courses are recorded for each subject, has received considerably less attention, despite its importance for practical data analysis. In particular, there is a need for measures and estimates to capture dependency between the components of vector-valued longitudinal data. We propose and analyze a simple and effective nonparametric method to quantify the covariation of components of multivariate longitudinal observations, which are viewed as realizations of a random process. This includes the notion of a correlation between derivatives and time-shifted versions. The concept of dynamical correlation is based on a scalar product obtained from pairs of standardized smoothed curves. The proposed method can be used when observation times are irregular and not matching between subjects or between responses within a subject. For higher-dimensional data, one may construct a dynamical correlation matrix that then serves as a starting point for standard multivariate analysis techniques, such as principal components. We iliustrate our methods via simulations as well as with data on five acute-phase blood proteins measured longitudinally from a study of hemodialysis patients.