Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, USA.
Stat Med. 2024 Nov 30;43(27):5077-5087. doi: 10.1002/sim.10218. Epub 2024 Sep 18.
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.
我们考虑在检测方法修改的情况下,评估治疗选择的生物标志物。从癌症患者那里获得了生存结果、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 平台上。最近提出了一种新方法,只需进行重现性研究即可快速评估迁移的生物标志物,即将原始生物标志物视为迁移生物标志物的受污染观察值,从而比较两个平台。但是,它对经典测量误差模型和结果的线性预测的假设可能不成立。忽略这种模型偏差可能会导致治疗选择不理想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建立事件率与生物标志物之间的关系,并且推导出来的基于标志物的治疗选择是最优的。我们进一步假设迁移和原始生物标志物之间存在非参数关系,并表明与无误差生物标志物相比,受污染的生物标志物会导致治疗选择不理想。我们通过 B 样条逼近获得估计值。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演示。