Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development

Abstract

For longitudinal studies with multivariate observations, we propose statistical methods to identify clusters of archetypal subjects by using techniques from functional data analysis and to relate longitudinal patterns to outcomes. We demonstrate how this approach can be applied to examine associations between multiple time-varying exposures and subsequent health outcomes, where the former are recorded sparsely and irregularly in time, with emphasis on the utility of multiple longitudinal observations in the framework of dimension reduction techniques. In applications to children’s growth data, we investigate archetypes of infant growth patterns and identify subgroups that are related to cognitive development in childhood. Specifically, “Stunting” and “Faltering” time-dynamic patterns of head circumference, body length and weight in the first 12 months are associated with lower levels of long-term cognitive development in comparison to “Generally Large” and “Catch-up” growth. Our findings provide evidence for the statistical association between multivariate growth patterns in infancy and long-term cognitive development.

Time Dynamics of COVID-19

“…Slovenia). These archetypes are derived from the extreme ends of the two main modes of variation [25,26] observed for the sample, which emerge from functional principal component analysis (FPCA, Figure 2). FPCA is similar to ordinary principal component analysis in the sense that it projects high dimensional curve data into a low dimensional space, representing them as a random vector of functional principal component (FPC) scores (see Methods).…”

Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers

“…The first is to separately apply the univariate FPCA procedure to each biomarker process. The second is to use multivariate functional principal component analysis (MFPCA) techniques (Berrendero, Justel, & Svarc, 2011;Chiou, Chen, & Yang, 2014;Chiou & Müller, 2014;Han et al, 2018;Happ & Greven, 2018;Jacques & Preda, 2014;Ramsay & Silverman, 2005). These techniques have been particularly developed to accommodate the correlation among multiple processes and provide more parsimonious representation of the data.…”

Modeling of rainfall variability using functional principal component method: a case study of Taiz region, Yemen

“…Consequently, the applications of functional data analysis techniques have been successfully implemented in many different fields during the last years, for examples, environment and ecology (Gao and Niemeie 2008;Ikeda et al 2008;Torres et al 2010;Sierra et al 2017;Dennis et al 2019), hydrology and metrology (Berrendero et al 2011;Suhaila et al 2011;Chebana et al 2012;Suhaila and Yusop 2016;Beyaztas and Yaseen 2019;Hael et al 2020), in economics and finance (Müller et al 2011;Feng and Qian 2018), demography (Hyndman and Booth 2008;Hyndman and Shang 2010;Han et al 2018), and science and biomedicine (Illian et al 2009;Sánchez-Sánchez et al 2018;Granato et al 2018;Reiss and Xu 2019). For more details, (Ullah and Finch 2013) prepared a systematic review to identify FDA application studies in many different areas.…”

“…Their FPCs results showed significant differences in terms of the high/increase and low/decrease between species, and that helped them in understanding and simplifying complicated temporal changes. (Han et al 2018) used functional principal component analysis to identify multivariate patterns of growth data of children; their findings provided evidence for the statistical association between multivariate growth patterns. (Feng and Qian 2018) analyzed and predicted the interest rates of Chinese term structure using functional principal component analysis, the results of the functional principal component model demonstrated good forecasting compared approvingly with other models.…”