A0462
Title: Nonparametric biomarker based treatment selection with reproducibility data
Authors: Xiao Song - University of Georgia (United States) [presenting]
Abstract: Biomarkers for treatment selection are considered for evaluation 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, a nonparametric logistic regression is adopted to model the relationship between the event rate and the biomarker, and the deduced marker-based treatment selection is optimal. A nonparametric relationship is further assumed between the migrated and original biomarkers, and it is shown that the error-contaminated biomarker leads to sub-optimal treatment selection compared to the error-free biomarker. The estimation via B-spline approximation is obtained. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.
