Extensions of PROLONG: Penalized Regression On Longitudinal Multi-Omics Data with Network and Group

Oregon Convention Center 

There is a growing interest in longitudinal omics data, but there are gaps in existing high-dimensional methodology. In particular, we are focused on modeling general continuous longitudinal outcomes with continuous longitudinal multi-omics predictors. Simple univariate longitudinal models do not leverage the correlation across predictors, thus losing power. Our method, PROLONG, leverages the first differences of the data to address the piecewise linear structure and the observed time dependence and applies penalties that induce sparsity while incorporating the dependence structure of the data. This presentation will review PROLONG and discuss recent extensions to multiple treatment arms, mixed effects, and general multi-omic data.

Omics

Longitudinal

High-dimensional

Biomarkers

TB

Metabolomics 

Biometrics Section