Predicting Treatment Effect from Surrogate Endpoints and Historical Trials: An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time

Stuart G. Baker, Daniel J. Sargent, Marc Buyse, Tomasz Burzykowski

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Using multiple historical trials with surrogate and true endpoints, we consider various models to predict the effect of treatment on a true endpoint in a target trial in which only a surrogate endpoint is observed. This predicted result is computed using (1) a prediction model (mixture, linear, or principal stratification) estimated from historical trials and the surrogate endpoint of the target trial and (2) a random extrapolation error estimated from successively leaving out each trial among the historical trials. The method applies to either binary outcomes or survival to a particular time that is computed from censored survival data. We compute a 95% confidence interval for the predicted result and validate its coverage using simulation. To summarize the additional uncertainty from using a predicted instead of true result for the estimated treatment effect, we compute its multiplier of standard error. Software is available for download.

Original languageEnglish (US)
Pages (from-to)248-257
Number of pages10
JournalBiometrics
Volume68
Issue number1
DOIs
StatePublished - Mar 2012

Keywords

  • Principal stratification
  • Randomized trials
  • Reproducibility

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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