The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine. One of the most useful characteristics of the model for practitioners is the interpretability of the variables in the analysis. However, this comes at the price of introducing strong assumptions concerning the functional form of the regression model. To break this gap, this paper aims to exploit the explainability advantages of the classical Cox model in the setting of interval-censoring using a new Lasso neural network that simultaneously selects the most relevant variables while quantifying non-linear relations between predictors and survival times. The gain of the new method is illustrated empirically in an extensive simulation study with examples that involve linear and non-linear ground dependencies. We also demonstrate the performance of our strategy in the analysis of physiological, clinical and accelerometer data from the NHANES 2003-2006 waves to predict the effect of physical activity on the survival of patients. Our method outperforms the prior results in the literature that use the traditional Cox model.
翻译:1972年出现的古典考克斯模型在利用生物医学的时间到活动分析对病人的预测进行量化方面促进突破。该模型对从业者最有用的特征之一是分析中变量的可解释性。然而,这是以对回归模型的功能形式提出强有力的假设为代价的。为了打破这一差距,本文件旨在利用古典考克斯模型在使用新的Lasso神经网络进行间检查时的可解释性优势,该网络同时选择最相关的变量,同时量化预测者与生存时间之间的非线性关系。新方法的得益在广泛的模拟研究中以经验方式加以说明,并举例说明线性和非线性地基依赖性。我们还展示了我们在分析NHANES 2003-2006年波浪的生理、临床和加速计数据方面的战略绩效,以预测物理活动对病人生存的影响。我们的方法超过了使用传统考克斯模型的文献的先前结果。