Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case–Cohort Studies
Article first published online: 16 JUN 2010
© 2010, The International Biometric Society
Volume 67, Issue 1, pages 18–28, March 2011
How to Cite
Zhang, H., Schaubel, D. E. and Kalbfleisch, J. D. (2011), Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case–Cohort Studies. Biometrics, 67: 18–28. doi: 10.1111/j.1541-0420.2010.01445.x
- Issue published online: 14 MAR 2011
- Article first published online: 16 JUN 2010
- Received November 2008. Revised January 2010. Accepted March 2010.
- Case–cohort study;
- Clustered data;
- Cox model;
- Estimating equation;
- Robust variance;
- Survival analysis
Summary Case–cohort sampling is a commonly used and efficient method for studying large cohorts. Most existing methods of analysis for case–cohort data have concerned the analysis of univariate failure time data. However, clustered failure time data are commonly encountered in public health studies. For example, patients treated at the same center are unlikely to be independent. In this article, we consider methods based on estimating equations for case–cohort designs for clustered failure time data. We assume a marginal hazards model, with a common baseline hazard and common regression coefficient across clusters. The proposed estimators of the regression parameter and cumulative baseline hazard are shown to be consistent and asymptotically normal, and consistent estimators of the asymptotic covariance matrices are derived. The regression parameter estimator is easily computed using any standard Cox regression software that allows for offset terms. The proposed estimators are investigated in simulation studies, and demonstrated empirically to have increased efficiency relative to some existing methods. The proposed methods are applied to a study of mortality among Canadian dialysis patients.