Nonparametric Biomarker Based Treatment Selection With Reproducibility Data.

ABSTRACT: 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 nonparame

SUBMITTER: Byers S 

PROVIDER: S-EPMC11583955 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature