Use Whole Cohort Information to Improve the Efficiency of Multivariate Marginal Hazard Model for Case-Cohort Studies

• Activity Number: 126
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Hongtao Zhang*+ and Jianwen Cai
• Companies: and University of North Carolina at Chapel Hill
• Keywords: Case-cohort study ; Multiple disease outcomes ; Survival analysis

Abstract

Case-cohort design is widely used in large cohort studies in which it is prohibitively costly to assemble covariate history for all subjects of the full cohort, especially when the disease rate is low. The same subcohort sample may be used to compare the effect of a risk factor on different types of diseases. Under this circumstance, times to different events need to be modeled simultaneously. Existing case-cohort estimators for multiple disease outcomes only utilize the covariate information in cases and subcohort controls, though many covariates are measured for everyone in the full cohort. Intuitively, making full use of the covariate information can improve efficiency. To this end, we consider a class of doubly-weighted estimators for both regular and generalized case-cohort studies with multiple disease outcomes. The asymptotic properties of the proposed estimators are derived and simulation studies show it can lead to a gain in efficiency with properly chosen weight function. The proposed method is applied to the Atherosclerosis Risk in Communities (ARIC) study.