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Stat Med. 2019 Mar 30;38(7):1135-1146. doi: 10.1002/sim.7996. Epub 2018 Oct 10.

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

Author information

1
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
2
Clinical Epidemiology Division, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
3
Department of Statistics, University of Florida, Gainesville, FL.
4
Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA.
5
Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA.
6
Center for Inference and Dynamics of Infectious Diseases, Seattle, WA.

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.

KEYWORDS:

causal inference; nonparametric Bayesian; prediction; rotavirus; surrogacy

PMID:
30306600
PMCID:
PMC6399061
[Available on 2020-03-30]
DOI:
10.1002/sim.7996

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