American Statistical Association New York City Metropolitan Area Chapter Levin Lecture Series: Spring 2019 Colloquium Seminars Department of Biostatistics Mailman School of Public Health Columbia University

Chronic diseases such as COPD and Huntington's disease progress slowly over extended periods. The knowledge of how progression is affected by differing patient characteristics is crucial for informing clinical decisions. However, understanding disease progression from real-world data is challenging. Not only are observations noisy, and irregular in time, but the rate of progression may exhibit significant variation across patients. In addition, different stages of the target disease may have unbalanced coverage in the observational data. We propose a disease progression model with covariates to tackle two difficulties when modeling disease progression with observational data. First, the proposed model explicitly accounts for patient level heterogeneity in progression by conditioning on patient characteristics. Second, the model mitigates the difficulties caused by unbalanced samples by leveraging multi-task learning structures. We demonstrate the capabilities of the proposed model by both simulation studies and applications to real-world disease registry data.