Department of Biostatistics and Computing, Yonsei University Graduate School, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.
Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea.
Sci Rep. 2023 Mar 30;13(1):5207. doi: 10.1038/s41598-023-32517-4.
Stability selection is a variable selection algorithm based on resampling a dataset. Based on stability selection, we propose weighted stability selection to select variables by weighing them using the area under the receiver operating characteristic curve (AUC) from additional modelling. Through an extensive simulation study, we evaluated the performance of the proposed method in terms of the true positive rate (TPR), positive predictive value (PPV), and stability of variable selection. We also assessed the predictive ability of the method using a validation set. The proposed method performed similarly to stability selection in terms of the TPR, PPV, and stability. The AUC of the model fitted on the validation set with the selected variables of the proposed method was consistently higher in specific scenarios. Moreover, when applied to radiomics and speech signal datasets, the proposed method had a higher AUC with fewer variables selected. A major advantage of the proposed method is that it enables researchers to select variables intuitively using relatively simple parameter settings.
稳定性选择是一种基于对数据集进行重采样的变量选择算法。基于稳定性选择，我们提出了加权稳定性选择，通过使用额外建模的接收者操作特征曲线（ROC）下面积（AUC）对变量进行加权选择。通过广泛的模拟研究，我们从真阳性率（TPR）、阳性预测值（PPV）和变量选择稳定性方面评估了该方法的性能。我们还使用验证集评估了该方法的预测能力。在所提出的方法中，模型拟合在验证集上的 AUC 值在特定场景下始终高于稳定性选择。此外，当应用于放射组学和语音信号数据集时，所提出的方法选择的变量较少，但 AUC 更高。所提出的方法的一个主要优势是，它允许研究人员使用相对简单的参数设置直观地选择变量。
