A0987
Title: Efficient case-cohort design using balanced sampling
Authors: Sangwook Kang - Yonsei University (Korea, South) [presenting]
Kaeum Choi - Emory University (United States)
Abstract: The case-cohort design is a cost-efficient two-phase design for analyzing survival data when key risk factors are expensive to assess, and the event rate is low. Traditionally, subcohorts are selected via simple random sampling, which might not fully utilize available information. An efficient sampling design is introduced using balanced sampling for subcohort selection within the case-cohort design. A notable benefit of employing balanced sampling is the automatic calibration of auxiliary variables available for the entire cohort. Under a Cox model, it has been demonstrated that the calibration of sampling weights, utilizing auxiliary variables highly correlated with the main risk factor, significantly enhances the efficiency of regression coefficient estimators. Extensive simulation experiments show the reduced variabilities under the proposed approach in comparison to those under both simple random sampling. The proposed design and estimation procedure are further illustrated using the well-established National Wilms Tumor Study dataset.
