Title: Weight calibration to improve efficiency for estimating pure absolute risks from the proportional and additive hazards model in the nested case-control design
Speaker: Yei Eun Shin, PhD, Biostatistics Branch | Division of Cancer Epidemiology and Genetics, National Cancer Institute | National Institutes of Health
Date: Tuesday February 16, 2021
Abstract: Cohort studies provide information on the risks of disease. For rare outcomes, large cohorts are needed to have sufficient numbers of events, making it costly to obtain covariate information on all cohort members. Nested case-control design (NCC) is one of the most popular cost-effective subsampling designs in such epidemiological studies. Standard NCC studies only use case-control subsamples in which information are complete. Recent studies incorporate covariate information available in the entire cohort using weight calibration techniques for improving the estimation of the covariate effects of hazard models. My objective is to extend the weight calibration approaches to improve the estimation of pure absolute risks by additionally incorporating survival information such as follow-up times. Two model frameworks, Cox proportional hazards model and Aalen’s additive hazards model, are considered. Simulations show how much precision is improved by calibration and confirm the validity of inference based on asymptotic normality. Examples are provided using data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Study.