Multivariate functional partial least squares for classification using longitudinal data.

The use of statistical methods to predict outcomes using high dimensional datasets in medicine is becoming increasingly popular for forecasting and monitoring patient health. Our work is motivated by a longitudinal dataset containing 1H NMR spectra of metabolites of 18 patients undergoing a kidney transplant alongside their graft outcomes that fall into one of three categories: acute rejection, delayed graft function and primary function. We proposed a functional partial least squares (FPLS) model that extends existing PLS methods for the analysis of longitudinally measured scalar omics datasets to the case of longitudinally measured functional datasets.