Nonparametric Biomarker Based Treatment Selection With Reproducibility Data.

Autor: Byers S; Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA., Song X; Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2024 Nov 30; Vol. 43 (27), pp. 5077-5087. Date of Electronic Publication: 2024 Sep 18.
DOI: 10.1002/sim.10218
Abstrakt: We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were attained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. A recent novel approach allows a quick evaluation of the migrated biomarker with only a reproducibility study needed to compare the two platforms, achieved by treating the original biomarker as an error-contaminated observation of the migrated biomarker. However, its assumptions of a classical measurement error model and a linear predictor for the outcome may not hold. Ignoring such model deviations may lead to sub-optimal treatment selection or failure to identify effective biomarkers. To overcome such limitations, we adopt a nonparametric logistic regression to model the relationship between the event rate and the biomarker, and the deduced marker-based treatment selection is optimal. We further assume a nonparametric relationship between the migrated and original biomarkers and show that the error-contaminated biomarker leads to sub-optimal treatment selection compared to the error-free biomarker. We obtain the estimation via B-spline approximation. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.
(© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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