Predicting Treatment Effect from Surrogate Endpoints and Historical Trials: An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time
Article first published online: 13 AUG 2011
© 2011, The International Biometric Society No claim to original US government works
Volume 68, Issue 1, pages 248–257, March 2012
How to Cite
Baker, S. G., Sargent, D. J., Buyse, M. and Burzykowski, T. (2012), Predicting Treatment Effect from Surrogate Endpoints and Historical Trials: An Extrapolation Involving Probabilities of a Binary Outcome or Survival to a Specific Time. Biometrics, 68: 248–257. doi: 10.1111/j.1541-0420.2011.01646.x
- Issue published online: 23 MAR 2012
- Article first published online: 13 AUG 2011
- Received August 2011. Revised May 2011. Accepted May 2011.
- Principal stratification;
- Randomized trials;
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.