Chatterjee Saptarshi, Chowdhury Shrabanti, Basu Sanjib
Eli Lilly and Company, Indianapolis, IN.
Icahn School of Medicine at Mount Sinai, New York, NY.
J R Stat Soc Ser C Appl Stat. 2021 Jun;70(3):511-531. doi: 10.1111/rssc.12467. Epub 2021 Jun 4.
The question of association between outcome and feature is generally framed in the context of a model based on functional and distributional forms. Our motivating application is that of identifying serum biomarkers of angiogenesis, energy metabolism, apoptosis, and inflammation, predictive of recurrence after lung resection in node-negative non-small cell lung cancer patients with tumor stage T2a or less. We propose an omnibus approach for testing association that is free of assumptions on functional forms and distributions and can be used as a general method. This proposed maximal permutation test is based on the idea of thresholding, is readily implementable and is computationally efficient. We demonstrate that the proposed omnibus tests maintain their levels and have strong power for detecting linear, nonlinear and quantile-based associations, even with outlier-prone and heavy-tailed error distributions and under nonparametric setting. We additionally illustrate the use of this approach in model-free feature screening and further examine the level and power of these tests for binary outcome. We compare the performance of the proposed omnibus tests with comparator methods in our motivating application to identify preoperative serum biomarkers associated with non-small cell lung cancer recurrence in early stage patients.
结局与特征之间的关联问题通常是在基于函数形式和分布形式的模型背景下提出的。我们的激励性应用是识别血管生成、能量代谢、细胞凋亡和炎症的血清生物标志物，这些标志物可预测肿瘤分期为T2a或更低的淋巴结阴性非小细胞肺癌患者肺切除术后的复发情况。我们提出了一种综合检验关联的方法，该方法无需对函数形式和分布做假设，并且可以用作通用方法。所提出的最大置换检验基于阈值化思想，易于实现且计算效率高。我们证明，即使在误差分布容易出现异常值和重尾分布的情况下以及在非参数设置下，所提出的综合检验仍能保持其显著性水平，并且在检测线性、非线性和基于分位数的关联方面具有强大的功效。我们还说明了这种方法在无模型特征筛选中的应用，并进一步检验了这些检验对于二元结局的显著性水平和功效。在我们的激励性应用中，我们将所提出的综合检验的性能与比较方法进行比较，以识别与早期患者非小细胞肺癌复发相关的术前血清生物标志物。
