The value of surrogate endpoints for predicting real-world survival across five XXXX types.

@entity1 @entity43 @entity5 @entity1054 @entity1349
Objective It is unclear how well different outcome measures in randomized controlled trials (RCTs) perform in predicting @entity5 survival. We assess the ability of RCT @entity1349 ( @entity1349 ) and surrogate endpoints - progression-free survival (PFS) and @entity1054 ( @entity1054 ) - to predict @entity5 . Methods We identified 20 treatments and 31 indications for breast, @entity43 that had a phase III RCT reporting median @entity1349 and median PFS or @entity1054 . Median @entity1349 was determined using a Kaplan-Meier estimator applied to @entity1 in the Surveillance and Epidemiology End Results (SEER)-Medicare database (1991-2010). Performance of RCT @entity1349 and PFS/ @entity1054 in predicting @entity1349 was measured using t-tests, median absolute prediction error, and R(2) from linear regressions. Results Among 72,600 SEER-Medicare @entity1 similar to RCT @entity1 , median survival was 5.9 months for trial surrogates, 14.1 months for @entity1349 , and 13.4 months for @entity1349 . For this sample, regression models using clinical @entity1349 and trial surrogates as independent variables predicted @entity1349 significantly better than models using surrogates alone (P   =   0.026). Among all real-world @entity1 using sample treatments (N   =   309,182), however, adding @entity1349 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 @entity5 types investigated, @entity1349 and surrogates were each independently valuable in predicting @entity1349 outcomes for @entity1 similar to trial @entity1 . In broader real-world populations, however, @entity1349 added little incremental value over surrogates alone.
