Super-efficient estimation of future conditional hazards based on time-homogeneous high-quality marker information
提出一种在标记信息高质量且时间齐性假设下的非参数计数过程模型，实现参数级收敛速度的超高效估计，用于构建风险率的同时置信带，并通过模拟和肝硬化数据验证其稳健性。
Summary We introduce a new concept for forecasting future events based on marker information. The model is developed in the nonparametric counting process setting under the assumptions that the marker is of so-called high quality and with a time-homogeneous conditional distribution. Despite the model having nonparametric parts, it is established herein that it attains a parametric rate of uniform consistency and uniform asymptotic normality. In usual nonparametric scenarios, reaching such a fast convergence rate is not possible, so one can say that the proposed approach is super-efficient. These theoretical results are employed in the construction of simultaneous confidence bands directly for the hazard rate. Extensive simulation studies validate and compare the proposed methodology with the joint modelling approach and illustrate its robustness for mild violations of the assumptions. Its use in practice is illustrated in the computation of individual dynamic predictions in the context of primary biliary cirrhosis of the liver.