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.

中文翻译：

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具有重现性数据的基于非参数生物标志物的治疗选择


我们考虑评估生物标志物，以根据测定修改进行治疗选择。从癌症患者中获得生存结果、治疗和 Affymetrix 基因表达数据。考虑将基因表达生物标志物迁移到 Illumina 平台。最近的一种新方法可以快速评估迁移的生物标志物，只需进行可重复性研究来比较两个平台，这是通过将原始生物标志物视为迁移生物标志物的错误污染观察来实现的。然而，其对经典测量误差模型和结果的线性预测器的假设可能不成立。忽略此类模型偏差可能会导致治疗选择次优或无法识别有效的生物标志物。为了克服这些限制，我们采用非参数逻辑回归来模拟事件发生率和生物标志物之间的关系，并推断出基于标志物的治疗选择是最佳的。我们进一步假设迁移的生物标志物和原始生物标志物之间存在非参数关系，并表明与无错误的生物标志物相比，错误污染的生物标志物导致次优的治疗选择。我们通过 B 样条近似获得估计。该方法通过模拟研究进行评估，并通过应用于肺癌数据进行论证。