title: Comparing biomarkers as trial level general surrogates.
abstract: An intermediate response measure that accurately predicts efficacy in a new setting can reduce trial cost and time to product licensure. In this article, we define a trial level general surrogate, which is an intermediate response that can be used to accurately predict efficacy in a new setting. Methods for evaluating general surrogates have been developed previously. Many methods in the literature use trial level intermediate responses for prediction. However, all existing methods focus on surrogate evaluation and prediction in new settings, rather than comparison of candidate general surrogates, and few formalize the use of cross validation to quantify the expected prediction error. Our proposed method uses Bayesian non-parametric modeling and cross-validation to estimate the absolute prediction error for use in evaluating and comparing candidate trial level general surrogates. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate trial level general surrogates in several multi-national trials of a pentavalent rotavirus vaccine. We identify at least one immune measure that has potential value as a trial level general surrogate and use it to predict efficacy in a new trial where the clinical outcome was not measured.
authors.affiliation: Biostatistics Research Branch, Division of Clinical Research, NIAID/NIH, Bethesda, Maryland, U.S.A.
authors.forename: Erin E
authors.initials: EE
authors.lastname: Gabriel
authors.affiliation: Department of Statistics and Data Sciences, The University of Texas at Austin, U.S.A.|Department of Integrative Biology, The University of Texas at Austin, U.S.A.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Daniels
authors.affiliation: Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, U.S.A.|Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.|Center for Inference and Dynamics of Infectious Diseases, Seattle, Washington, U.S.A.
authors.forename: M Elizabeth
authors.initials: ME
authors.lastname: Halloran
mesh_terms: D000818:Animals; D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis*; D001699:Biometry / Q000379:methods; D002986:Clinical Trials as Topic; D006801:Humans; D011379:Prognosis; D022243:Rotavirus Vaccines; D016896:Treatment Outcome
chemical_list: D015415:Biomarkers; D022243:Rotavirus Vaccines
keywords: Bayesian non-parametrics; Cross-Validation; Meta-analysis; Surrogate markers; Vaccines