-
Views
-
Cite
Cite
Stuart G. Baker, Daniel J. Sargent, Marc Buyse, Tomasz Burzykowski, Predicting Treatment Effect from Surrogate Endpoints and Historical Trials: An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time, Biometrics, Volume 68, Issue 1, March 2012, Pages 248–257, https://doi.org/10.1111/j.1541-0420.2011.01646.x
- Share Icon Share
Summary
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.