New Developments on Quantile Regression Analysis of Censored Data: Theory, Methodology and Computation

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

Quantile regression has recently emerged as a valuable semiparametric alternative to the popular Cox model for analyzing censored data. It directly models survival time; thus is easy to interpret. More importantly, it relaxes the proportional hazards constraint associated with the Cox model and is particularly powerful for heterogeneous data. Despite the remarkable recent progress, several important and challenging statistical problems remain unsolved. For example, there exists limited literature on censored quantile regression when the sample arises from an observational study and is not representative of the target population; when the censored data come from genomics studies involving high-dimensional covariates; or when random effects are present due to the incorporation of latent variables. Motivated by these challenging problems, this project will develop novel methodology, theory and algorithms, which have the potential to significantly advance the applications of censored quantile regression. The PI will rigorously study the theoretical properties of the proposed new procedures and investigate their applications in practical data analysis. Censored data arise in diverse fields such as economics, engineering, medicine, psychology and sociology. The new methodology and theory are expected to make important contributions to the current body of knowledge on statistical analysis of survival data. In particular, the proposed research will make timely contributions to high-dimensional data analysis with censored responses, which has important applications in modern genomics and is still a relatively unexplored research area. The PI will develop useful software packages and make them freely available to the research community. The research results will be incorporated in different levels of statistical courses. The PI will also incorporate her research activity with graduate education. Students from minority groups will be especially encouraged to participate in the proposed projects.