Abstract:
:We extend the method proposed in a recent work by the Authors for trial-level general surrogate evaluation to allow combinations of biomarkers and provide a procedure for finding the "best" combination of biomarkers based on the absolute prediction error summary of surrogate quality. We use a nonparametric Bayesian model that allows us to select an optimal subset of biomarkers without having to consider a large number of explicit model specifications for that subset. This dramatically reduces the number of model comparisons needed. Given the model's flexibility, complex nonlinear relationships can be fit when enough data are available. We evaluate the operating characteristics of our proposed method in simulations designed to be similar to our motivating example. We use our method to compare and evaluate combinations of biomarkers as trial-level general surrogates for the pentavalent rotavirus vaccine RotaTeq™ (RV5) (Merck & Co, Inc, Kenilworth, New Jersey, USA), finding that the same single biomarker identified in our previously published analysis is likely the optimal subset.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Gabriel EE,Sachs MC,Daniels MJ,Halloran MEdoi
10.1002/sim.7996subject
Has Abstractpub_date
2019-03-30 00:00:00pages
1135-1146issue
7eissn
0277-6715issn
1097-0258journal_volume
38pub_type
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