More Efficient Estimators for Case-Cohort Studies

• Activity Number: 233
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): So Young Kim*+ and Jianwen Cai and Wenbin Lu
• Companies: The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and North Carolina State University
• Keywords: Case-corhot study ; Multiple diseases outcomes ; Multivariate failure time ; Survival analysis ; Proportional hazards ; Relative efficiency

Abstract

Case-cohort study design is generally used to reduce cost in large cohort studies. The case-cohort design consists of a random sample of the entire cohort, named subcohort, and all the subjects with the disease of interest. When several diseases are of interest, several case-cohort studies are usually conducted using the same subcohort. When these case-cohort data are analyzed, the common practice is to analyze each disease separately ignoring data collected in subjects with the other diseases. This is not efficient use of the data. In this paper, we propose more efficient estimators by using all available information. We consider both joint analysis and separate analysis. We propose an estimating equation approach with a new weight function. We establish that the proposed estimator is consistent and asymptotically normally distributed. Simulation studies show that the proposed methods using all available information gain efficiency. For comparing the effect of the exposure on different diseases, tests based on the joint analysis are more powerful than those based on the separate analysis. We apply our proposed method to the data from the Busselton Health Study.