Ruiyan Luo

Fully Functional Neural Networks for Functional Regression

Abstract

We consider evaluating new or more accurately measured predictive biomarkers for treatment selection based on a previous clinical trial involving standard biomarkers. Instead of rerunning the clinical trial with the new biomarkers, we propose a more efficient approach which requires only either conducting a reproducibility study in which the new biomarkers and standard biomarkers are both measured on a set of patient samples, or adopting replicated measures of the error-contaminated standard biomarkers in the original study. This approach is easier to conduct and much less expensive than studies that require new samples from patients randomized to the intervention. In addition, it makes it possible to perform the estimation of the clinical performance quickly, since there will be no requirement to wait for events to occur as would be the case with prospective validation. The treatment selection is assessed via a working model, but the proposed estimator of the mean restricted lifetime is valid even if the working model is misspecified. The proposed approach is assessed through simulation studies and applied to a cancer study.

About the Speaker

Dr. Ruiyan Luo is a professor in the Department of Population Health Sciences at the Georgia State University. She received her PhD in Statistics from University of Wisconsin-Madison in 2007. Her research interests include functional data analysis, Bayesian statistics, statistical learning, dynamic system modeling, and applications. Her recent research has focused on developing novel methods for linear and nonlinear functional regression models with smooth or spiky functional variables, including models with high-dimensional functional predictors. She is also interested in infectious disease modeling with differential equations and the incorporation of functional data analysis in functional differential equations.