Retrospective likelihood-based methods for analyzing case-cohort genetic association studies

Yuanyuan Shen, Tianxi Cai, Yu Chen, Ying Yang, Jinbo Chen

Research output: Contribution to journalArticle

Abstract

The case cohort (CCH) design is a cost-effective design for assessing genetic susceptibility with time-to-event data especially when the event rate is low. In this work, we propose a powerful pseudo-score test for assessing the association between a single nucleotide polymorphism (SNP) and the event time under the CCH design. The pseudo-score is derived from a pseudo-likelihood which is an estimated retrospective likelihood that treats the SNP genotype as the dependent variable and time-to-event outcome and other covariates as independent variables. It exploits the fact that the genetic variable is often distributed independent of covariates or only related to a low-dimensional subset. Estimates of hazard ratio parameters for association can be obtained by maximizing the pseudo-likelihood. A unique advantage of our method is that it allows the censoring distribution to depend on covariates that are only measured for the CCH sample while not requiring the knowledge of follow-up or covariate information on subjects not selected into the CCH sample. In addition to these flexibilities, the proposed method has high relative efficiency compared with commonly used alternative approaches. We study large sample properties of this method and assess its finite sample performance using both simulated and real data examples.

Original languageEnglish (US)
Pages (from-to)960-968
Number of pages9
JournalBiometrics
Volume71
Issue number4
DOIs
StatePublished - Dec 1 2015

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Keywords

  • Case-cohort design
  • Cox proportional hazards model
  • Genetic association
  • Inverse probability weighting
  • Polytomous regression
  • Pseudo-likelihood

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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