Meta-analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling

Debashis Ghosh, Jeremy M.G. Taylor, Daniel J. Sargent

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

There has been great recent interest in the medical and statistical literature in the assessment and validation of surrogate endpoints as proxies for clinical endpoints in medical studies. More recently, authors have focused on using metaanalytical methods for quantification of surrogacy. In this article, we extend existing procedures for analysis based on the accelerated failure time model to this setting. An advantage of this approach relative to proportional hazards model is that it allows for analysis in the semicompeting risks setting, where we model the region where the surrogate endpoint occurs before the true endpoint. Several estimation methods and attendant inferential procedures are presented. In addition, between- and within-trial methods for evaluating surrogacy are developed; a novel principal components procedure is developed for quantifying trial-level surrogacy. The methods are illustrated by application to data from several studies in colorectal cancer.

Original languageEnglish (US)
Pages (from-to)226-232
Number of pages7
JournalBiometrics
Volume68
Issue number1
DOIs
StatePublished - Mar 2012

Keywords

  • Copula
  • Dependent censoring
  • Latent factor
  • Linear regression
  • Multivariate failure time data
  • Singular value decomposition

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Meta-analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling'. Together they form a unique fingerprint.

Cite this