NSF Award
2316868
Censored data arise when the failure time of interest is only partially observed. This project will develop novel and powerful semiparametric transformation models to more effectively analyze such data. Furthermore, this project aims to contribute to the understanding and advancement of personalized medicine, a field focused on tailoring medical decisions and interventions to individual patients. Although primarily motivated by biomedical applications, such as HIV vaccine and AIDS treatment, the outcomes of this research will have broad applications in diverse disciplines, including engineering, economics, finance, and social sciences. The PI will establish new summer research programs designed to increase the participation of underrepresented groups in undergraduate research in statistics and prepare them for success in advanced degree programs and careers in academia and industry. Additionally, the project will offer research training opportunities at the graduate level. <br/> <br/>The new models proposed in this research extend the traditional transformation models in important directions and effectively bridge the gap between additive-multiplicative and transformation models, two of the most prominent models in survival analysis. The presence of infinite dimensional parameters poses considerable computational and theoretical challenges. To address these challenges, this project will develop robust and scalable Expectation-Solving algorithms for estimating model parameters under various censoring mechanisms, including right-censored, interval-censored, and partly interval-censored. Moreover, a new class of semiparametric transformation models will be proposed for estimating optimal treatment regimes, an important topic in personalized medicine. This research will leverage modern empirical process theory to develop new theoretical arguments for investigating the proposed estimators. Simulation studies and real data analysis on HIV vaccine and AIDS clinical trials will be performed to demonstrate the methods. The PI will also create and distribute user-friendly open-source software to researchers and practitioners in the field.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
