Analyzing Survival Data with Graphical Proportional Hazards Measurement Error Models

• Activity Number: 176 - New England Statistical Society Invited Papers on Novel Developments and Future Directions of Statistics in Data Science
• Type: Invited
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
• Sponsor: New England Statistical Society
• Author(s): Grace Yi*
• Companies: University of Western Ontario
• Keywords: survival data; measurement error; network structure; proportional hazards model; high dimensional data; estimation

Abstract

In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.