title: Predicting treatment effect from surrogate endpoints and historical trials: an extrapolation involving probabilities of a binary outcome or survival to a specific time.
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.
authors.affiliation: National Cancer Institute, EPN 3131, Bethesda, Maryland 20892-7354, USA. sb16i@nih.gov
authors.forename: Stuart G
authors.initials: SG
authors.lastname: Baker
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Sargent
authors.forename: Marc
authors.initials: M
authors.lastname: Buyse
authors.forename: Tomasz
authors.initials: T
authors.lastname: Burzykowski
mesh_terms: D015415:Biomarkers; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D016019:Survival Analysis; D015996:Survival Rate
chemical_list: D015415:Biomarkers