Optimizing and evaluating biomarker combinations as trial-level general surrogates.

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 Med

journal_title

Statistics in medicine

authors

Gabriel EE,Sachs MC,Daniels MJ,Halloran ME

doi

10.1002/sim.7996

subject

Has Abstract

pub_date

2019-03-30 00:00:00

pages

1135-1146

issue

7

eissn

0277-6715

issn

1097-0258

journal_volume

38

pub_type

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