Evaluating biomarkers for treatment selection from reproducibility studies

Abstract

We consider evaluating biomarkers for treatment selection under assay modification. Survival outcome, treatment, and Affymetrix gene expression data were obtained from cancer patients. Consider migrating a gene expression biomarker to the Illumina platform. We propose an approach that 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. We adopt a nonparametric regression model to characterize the relationship between the event rate and the biomarker and obtain an optimal marker-based treatment regime. Using B-spline approximation for nonparametric regression functions, we estimate the treatment regime via the SIMEX approach. SIMEX is a simple and general method that can handle measurement errors in complex cases. Through establishing the uniform convergence rate of the SIMEX estimator for the nonparametric regression functions, we derive the consistency and asymptotic normality of the empirical estimator of the biomarker performance measure. The approach is assessed by simulation studies and demonstrated through application to lung cancer data.

Biography

宋晓, 佐治亚大学流行病和生物统计系教授，本科和硕士毕业于北京大学数学系，博士毕业于北卡罗莱纳州立大学统计系，曾任华盛顿大学(University of Washington) 公共卫生学院生物统计系研究助理教授，主要研究兴趣包括生存分析、测量误差和缺失数据、非参数方法、高维数据和图像数据、以及基于生物标志物的医学检验预测与精准医疗。