Inference for Surrogate Endpoint Validation in the Binary Case

Ionut Bebu*, Thomas Mathew, Brian Agan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Surrogate endpoint validation for a binary surrogate endpoint and a binary true endpoint is investigated using the criteria of proportion explained (PE) and the relative effect (RE). The concepts of generalized confidence intervals and fiducial intervals are used for computing confidence intervals for PE and RE. The numerical results indicate that the proposed confidence intervals are satisfactory in terms of coverage probability, whereas the intervals based on Fiellers theorem and the delta method fall short in this regard. Our methodology can also be applied to interval estimation problems in a causal inference-based approach to surrogate endpoint validation.

Original languageEnglish
Pages (from-to)1272-1284
Number of pages13
JournalJournal of Biopharmaceutical Statistics
Issue number6
StatePublished - 2 Nov 2015


  • Causal effects
  • Fiducial interval
  • Generalized confidence interval
  • Proportion explained
  • Relative effect


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