Random Jump Forests：多状态模型条件转移率估计新方法
Random Jump Forests
统计学者看过来：这篇把随机森林思路搬进多状态生存分析，不依赖 Markov 假设也能算转移率，还有现成 R 包 JumpForests 可以直接上手。
Random Jump Forests 是一种非参数方法，用于估计多状态模型中的条件累积转移率与占用概率。单棵树同时对所有转移划分协变量空间，利用部分观测轨迹并在终端节点采用 Nelson-Aalen 估计器，再通过乘积积分恢复占用概率，无需 Markov 假设。在协变量取有限个值等条件下，可证明估计量的一致性和函数中心极限定理。模拟研究显示其预测精度接近正确设定的基准模型，在基准误设时明显更优，并在四个生物医学数据集上做了验证，方法已实现于 R 包 JumpForests。
Random Jump Forests
Random jump forests are introduced as a nonparametric tool for estimating conditional cumulative transition rates and occupation probabilities in multi-state models. The main idea is that a single tree partitions the covariate space for all transitions simultaneously, using partially observed trajectories, and with Nelson--Aalen estimators at the terminal nodes. Occupation probabilities are recovered through the product integral without requiring the Markov assumption. Uniform consistency and a functional central limit theorem for the estimators of cumulative rates can be established under the assumption that the covariate vector takes finitely many values, and under suitable splitting conditions. In particular, pointwise confidence intervals and simultaneous bands are available in the Markov case. Simulation studies show a predictive accuracy close to the correctly specified benchmark models and marked improvements when benchmarks are misspecified, with further illustration on four biomedical data sets. In the coverage study, confidence bands attain near-nominal coverage in several settings, with pooling of heterogeneous rates being the main source of possible undercoverage. The method, including prediction assessment and variable importance, is implemented in the R package JumpForests.