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An intermediate response measure that accurately predicts efficacy in a newsetting can reduce trial cost and time to product licensure. In this paper, wedefine a trial level general surrogate as a trial level intermediate responsethat accurately predicts trial level clinical responses. Methods for evaluatingtrial level general surrogates have been developed previously. Many methods inthe literature use trial level intermediate responses for prediction. However,all existing methods focus on surrogate evaluation and prediction in newsettings, rather than comparison of candidate trial level surrogates, and fewformalize the use of cross validation to quantify the expected predictionerror. Our proposed method uses Bayesian non-parametric modeling andcross-validation to estimate the absolute prediction error for use inevaluating and comparing candidate trial level general surrogates. Simulationsshow that our method performs well across a variety of scenarios. We use ourmethod to evaluate and to compare candidate trial level general surrogates inseveral multi-national trials of a pentavalent rotavirus vaccine. We identifytwo immune measures that have potential value as trial level general surrogatesand use the measures to predict efficacy in a trial with no clinical outcomesmeasured.

Comparing Biomarkers as Trial Level General Surrogates