title: The value of surrogate endpoints for predicting real-world survival across five cancer types.
abstract: OBJECTIVE
  It is unclear how well different outcome measures in randomized controlled trials (RCTs) perform in predicting real-world cancer survival. We assess the ability of RCT overall survival (OS) and surrogate endpoints - progression-free survival (PFS) and time to progression (TTP) - to predict real-world OS across five cancers.
  
  
  METHODS
  We identified 20 treatments and 31 indications for breast, colorectal, lung, ovarian, and pancreatic cancer that had a phase III RCT reporting median OS and median PFS or TTP. Median real-world OS was determined using a Kaplan-Meier estimator applied to patients in the Surveillance and Epidemiology End Results (SEER)-Medicare database (1991-2010). Performance of RCT OS and PFS/TTP in predicting real-world OS was measured using t-tests, median absolute prediction error, and R(2) from linear regressions.
  
  
  RESULTS
  Among 72,600 SEER-Medicare patients similar to RCT participants, median survival was 5.9 months for trial surrogates, 14.1 months for trial OS, and 13.4 months for real-world OS. For this sample, regression models using clinical trial OS and trial surrogates as independent variables predicted real-world OS significantly better than models using surrogates alone (P = 0.026). Among all real-world patients using sample treatments (N = 309,182), however, adding trial OS did not improve predictive power over predictions based on surrogates alone (P = 0.194). Results were qualitatively similar using median absolute prediction error and R(2) metrics.
  
  
  CONCLUSIONS
  Among the five tumor types investigated, trial OS and surrogates were each independently valuable in predicting real-world OS outcomes for patients similar to trial participants. In broader real-world populations, however, trial OS added little incremental value over surrogates alone.
authors.affiliation: a Precision Health Economics , Los Angeles , CA , USA ;
authors.forename: Jason
authors.initials: J
authors.lastname: Shafrin
authors.affiliation: b Fielding School of Public Health, University of California Los Angeles , Los Angeles , CA , USA ;
authors.forename: Ron
authors.initials: R
authors.lastname: Brookmeyer
authors.affiliation: a Precision Health Economics , Los Angeles , CA , USA ;
authors.forename: Desi
authors.initials: D
authors.lastname: Peneva
authors.affiliation: c Worldwide Health Outcomes, Value & Access, Novartis Pharmaceuticals , East Hanover , NJ , USA ;
authors.forename: Jinhee
authors.initials: J
authors.lastname: Park
authors.affiliation: c Worldwide Health Outcomes, Value & Access, Novartis Pharmaceuticals , East Hanover , NJ , USA ;
authors.forename: Jie
authors.initials: J
authors.lastname: Zhang
authors.affiliation: d Division of Hematology/Oncology , Cedars-Sinai Medical Center , Los Angeles , CA , USA ;
authors.forename: Robert A
authors.initials: RA
authors.lastname: Figlin
authors.affiliation: e Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California , Los Angeles , CA , USA.
authors.forename: Darius N
authors.initials: DN
authors.lastname: Lakdawalla
mesh_terms: D000368:Aged; D015415:Biomarkers; D017326:Clinical Trials, Phase III as Topic; D016208:Databases, Factual; D018450:Disease Progression; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D016014:Linear Models; D008297:Male; D006278:Medicare; D008875:Middle Aged; D009369:Neoplasms / Q000453:epidemiology* / Q000401:mortality*; D016017:Odds Ratio; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic; D018426:SEER Program; D016019:Survival Analysis; D016896:Treatment Outcome; D014481:United States
chemical_list: D015415:Biomarkers
keywords: Comparative effectiveness research; neoplasms; observational study; progression-free survival; surrogate endpoints; survival