Analysis of multiple survival events in generalized case-cohort designs.

Pubmed ID: 29992545

Pubmed Central ID: PMC6328348

Journal: Biometrics

Publication Date: Dec. 1, 2018

Affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, U.S.A.

Link: https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.12923

MeSH Terms: Humans, Case-Control Studies, Cohort Studies, Survival Analysis, Computer Simulation, Atherosclerosis, Research Design, Biometry, Outcome Assessment, Health Care

Grants: P01 CA142538, R01 GM047845, R01 ES021900

Authors: Kim S, Zeng D, Cai J

Cite As: Kim S, Zeng D, Cai J. Analysis of multiple survival events in generalized case-cohort designs. Biometrics 2018 Dec;74(4):1250-1260. Epub 2018 Jul 10.

Studies:

Abstract

Generalized case-cohort design has been proposed to assess the effects of exposures on survival outcomes when measuring exposures is expensive and events are not rare in the cohort. In such design, expensive exposure information is collected from both a (stratified) randomly selected subcohort and a subset of individuals with events. In this article, we consider extension of such design to study multiple types of survival events by selecting a proportion of cases for each type of event. We propose a general weighting scheme to analyze data. Furthermore, we examine the optimal choice of weights and show that this optimal weighting yields much improved efficiency gain both asymptotically and in simulation studies. Finally, we apply our proposed methods to data from the Atherosclerosis Risk in Communities study.