Title: Modeling of Long Term Survival Outcomes
Abstract: Modern data storage methods allow researchers to track patient health outcome information through different registries, clinical trials, and other studies. These methodsyield large-scale databases containing long-term follow- up of patients, measures of high-dimensional patient characteristics, and multi-varying comorbidities. We examine a family of survival models designed to accommodate features of modern data using the multi-resolution hazard (MRH) methodology. The MRH model is a Bayesian, semi-parametric survival model for estimation of the hazard rate and the effects of covariates on survival time. The model has been extended to incorporate non-proportional hazards as well as periods of time with sparsely observed failures. A case study of long-term prostate cancer survivors is examined,using RTOG clinical trials data, and implementation performed via the “MRH” package in R. Future directions for incorporation of time-varying covariates and multiple outcomes are examined, presented with corresponding applications.
Bio: Dr. Hagar received a PhD (2010) in Biostatistics from the University of California-Davis and is now a Post-doctoral researcher at the Applied Mathematics Department, University of Colorado-Boulder. Dr. Hagar has worked on multiple projects with a focus on Bayesian estimation methods in survival analysis using big data and datasets with long-term patient follow up. Dr. Hagar has 16 published manuscripts that are the result of multidisciplinary collaborations
Contact Name: Gabriel Huerta
Contact Email: firstname.lastname@example.org