Survival data including potentially cured subjects are common in clinical studies and mixture cure rate models are often used for analysis. The non-cured probabilities are often predicted by non-parametric, high-dimensional, or even unstructured (e.g. image) predictors, which is a challenging task for traditional nonparametric methods such as spline and local kernel. We propose to use the neural network to model the nonparametric or unstructured predictors’ effect in cure rate models and retain the proportional hazards structure due to its explanatory ability. We estimate the parameters by Expectation–Maximization algorithm. Estimators are showed to be consistent. Simulation studies show good performance in both prediction and estimation. Finally, we analyze Open Access Series of Imaging Studies data to illustrate the practical use of our methods.

包括可能治愈的受试者在内的生存数据在临床研究中很常见，并且混合治愈率模型经常用于分析。非治愈概率通常由非参数，高维甚至是非结构化（例如图像）的预测变量预测，这对于诸如样条和局部核等传统非参数方法而言是一项艰巨的任务。我们建议使用神经网络对治愈率模型中的非参数或非结构化预测变量的效果进行建模，并由于其解释能力而保留比例风险结构。我们通过期望最大化算法估计参数。估计量被证明是一致的。仿真研究表明，在预测和估计方面都具有良好的性能。最后，我们分析了影像研究的开放获取系列数据，以说明我们方法的实际使用。