Proportional Hazards Regression for the Analysis of Clustered Survival Data from Case – Cohort Studies

by Hui Zhang, Douglas E Schaubel, John D Kalbfleisch, Ann Arbor
Biometrics ()
Get full text at journal


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.

Cite this document (BETA)

Readership Statistics

7 Readers on Mendeley
by Discipline
57% Mathematics
43% Medicine
by Academic Status
29% Researcher (at an Academic Institution)
29% Ph.D. Student
14% Post Doc
by Country
14% Spain

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Sign up & Download

Already have an account? Sign in