Risk Factor Identification in Heterogeneous Disease Progression with L1-Regularized Multi-state Models.

Abstract

Multi-state model (MSM) is a useful tool to analyze longitudinal data for modeling disease progression at multiple time points. While the regularization approaches to variable selection have been widely used, extending them to MSM remains largely unexplored. In this paper, we have developed the L1-regularized multi-state model (L1MSTATE) framework that enables parameter estimation and variable selection simultaneously. The regularized optimization problem was solved by deriving a one-step coordinate descent algorithm with great computational efficiency. The L1MSTATE approach was evaluated using extensive simulation studies, and it showed that L1MSTATE outperformed existing regularized multi-state models in terms of the accurate identification of risk factors. It also outperformed the un-regularized multi-state models (MSTATE) in terms of identifying the important risk factors in situations with small sample sizes. The power of L1MSTATE in predicting the transition probabilities comparing with MSTATE was demonstrated using the Europe Blood and Marrow Transplantation (EBMT) dataset. The L1MSTATE was implemented in the open-access R package 'L1mstate'.