Efficient Estimation of Relative Risk in Case-Cohort Studies

• Activity Number: 145
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Emmanuel Sampene*+ and Abdus Wahed
• Companies: and University of Pittsburgh
• Keywords: Case-cohort study ; Two-stage design ; Regular asymptotically linear ; Influence Function ; Martingale theory

Abstract

A case-cohort study is a two-phase study where at the first phase a representative sample, referred to as the study cohort, is selected from the target population. At the second phase, a subsample is selected from the cohort based on the case status. All cases are included in the subsample whereas only random samples of controls are included. The endpoint of interest in such studies is usually the failure time. Several methods have been proposed to estimate the relative risk in a case-cohort study. These methods almost always disregard the covariate information that is not included in the sampled study sub-cohort, and therefore, results in the loss of efficiency. To make better use of the covariate information, we propose the locally efficient estimator (LEE) based on Robins et al. (1994) by restricting the estimator to a class of regular asymptotically linear estimators. We derive the expression for the most efficient estimator in this sub-class. The properties of this estimator are investigated through simulation and application to the Wilm's tumor study.