Additive Hazard Regression for the Analysis of Clustered Survival Data from Case-Cohort Studies

June Liu,Yi Zhang

doi:10.1155/2020/9587870

Abstract

The case-cohort design is an effective and economical method in large cohort studies, especially when the disease rate is low. Case-cohort design in most of the existing literature is mainly used to analyze the univariate failure time data. But in practice, multivariate failure time data are commonly encountered in biomedical research. In this paper, we will propose methods based on estimating equation method for case-cohort designs for clustered survival data. By introducing the event failure rate, three different weight functions are constructed. Then, three estimating equations and parameter estimators are presented. Furthermore, consistency and asymptotic normality of the proposed estimators are established. Finally, the simulation results show that the proposed estimation procedure has reasonable finite sample behaviors.

Highlights

In many failure time studies, there is a natural clustering of study subjects such that failure times within the same cluster may be correlated
One example of clinical trial is that the failure times of patients within a famlily may be correlated because they have common genetic characteristics and environmental factors
Design C: randomly sample clusters from the full cohort with Bernoulli sampling and randomly sample subjects with Bernoulli sampling from the selected clusters

Summary

Introduction

In many failure time studies, there is a natural clustering of study subjects such that failure times within the same cluster may be correlated. One may consider the case-cohort design, proposed by Prentice [5], which is widely used for the large cohort studies It entails collecting covariate data for all subjects who experienced the event of interest in the full cohort and for a random sample from the entire cohort. Lu and Shih [12] applied the proportional risk model to clustered failure time data under case cohort and proposed three design methods of case cohort to extract subcolumns. Zhang et al [13] added the failure information out of the subcolumns to the proportional risk model and proposed three kinds of different estimation equations and parameter estimates. Erefore, in this paper, an additive risk model will be applied to clustered data in casecohort design.

Model and Estimation Procedures

Asymptotic Properties

Numerical Studies