Improving the efficiency of estimation in the additive hazards model for stratified case-cohort design with multiple diseases

• Published in: Statistics in medicine Vol. 35; no. 2; pp. 282 - 293
• Main Authors: Kim, Soyoung, Cai, Jianwen, Couper, David
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 30.01.2016; Wiley Subscription Services, Inc
• Subjects: additive hazards models; Biostatistics - methods; case-cohort study; Cohort Studies; Computer Simulation; Coronary Disease - etiology; Correlation analysis; Cyclooxygenase 1 - genetics; Disease - etiology; Efficiency; Estimating techniques; Humans; multiple events; Multivariate Analysis; multivariate diseases outcomes; Proportional Hazards Models; Risk Factors; Sampling; Simulation; stratified sampling; Stroke - etiology; survival analysis; multiple events; survival analysis; multivariate diseases outcomes; stratified sampling; case-cohort study; additive hazards models
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.6623

The case–cohort study design has often been used in studies of a rare disease or for a common disease with some biospecimens needing to be preserved for future studies. A case–cohort study design consists of a random sample, called the subcohort, and all or a portion of the subjects with the disease of interest. One advantage of the case–cohort design is that the same subcohort can be used for studying multiple diseases. Stratified random sampling is often used for the subcohort. Additive hazards models are often preferred in studies where the risk difference, instead of relative risk, is of main interest. Existing methods do not use the available covariate information fully. We propose a more efficient estimator by making full use of available covariate information for the additive hazards model with data from a stratified case–cohort design with rare (the traditional situation) and non‐rare (the generalized situation) diseases. We propose an estimating equation approach with a new weight function. The proposed estimators are shown to be consistent and asymptotically normally distributed. Simulation studies show that the proposed method using all available information leads to efficiency gain and stratification of the subcohort improves efficiency when the strata are highly correlated with the covariates. Our proposed method is applied to data from the Atherosclerosis Risk in Communities study. Copyright © 2015 John Wiley & Sons, Ltd.