Tensor Landmark Analysis With Application to ADNI data

Recent advancements in data collection have facilitated the use of multidimensional arrays, also known as tensors, in prediction of health outcomes. In this article, we introduce a tensor landmark model for predicting survival outcomes using multiple longitudinal biomarkers as tensor covariates through CANDECOMP/PARAFAC (CP) decomposition. An iteratively reweighted least squares estimation is adopted for components of the tensor coefficients in the landmark model. We also present empirical results of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for right-censored data to select the CP rank. Simulations and Alzheimer's disease neuroimaging initiative (ADNI) data analysis demonstrate that our proposed model accurately estimates survival coefficients and predicts survival probabilities. The implementation code can be found online (https://github.com/sparkqkr/TensorCoxReg).