The evaluation of multiple surrogate endpoints

Research output: Contribution to journalArticlepeer-review

Abstract

Surrogate endpoints are desirable because they typically result in smaller, faster efficacy studies compared with the ones using the clinical endpoints. Research on surrogate endpoints has received substantial attention lately, but most investigations have focused on the validity of using a single biomarker as a surrogate. Our paper studies whether the use of multiple markers can improve inferences about a treatment's effects on a clinical endpoint. We propose a joint model for a time to clinical event and for repeated measures over time on multiple biomarkers that are potential surrogates. This model extends the formulation of Xu and Zeger (2001, in press) and Fawcett and Thomas (1996, Statistics in Medicine 15, 1663-1685). We propose two complementary measures of the relative benefit of multiple surrogates as opposed to a single one. Markov chain Monte Carlo is implemented to estimate model parameters. The methodology is illustrated with an analysis of data from a schizophrenia clinical trial.

Original languageEnglish (US)
Pages (from-to)81-87
Number of pages7
JournalBiometrics
Volume57
Issue number1
DOIs
StatePublished - 2001

Keywords

  • Longitudinal data
  • Markov chain Monte Carlo
  • Multivariate
  • Regression
  • Surrogate end-points
  • Survival analysis
  • Time to event

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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