论文标题
依赖审查的多阶段最佳动态治疗方案
Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring
论文作者
论文摘要
我们提出了一种增强学习方法，用于估计具有依赖审查的生存结果的最佳动态治疗方案。估计器允许故障时间有条件地独立于审查和依赖治疗决策时间，支持灵活数量的治疗组和治疗阶段，并且可以在特定时间点最大化平均生存时间或生存概率。估计量是使用广义随机生存林构建的，并且可以具有收敛的多项式速率。模拟和数据分析结果表明，在各种设置中，新估计器带来的预期结果比现有方法更高。 R套件DTRSURV可在Cran上找到。
We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time point. The estimator is constructed using generalized random survival forests and can have polynomial rates of convergence. Simulations and data analysis results suggest that the new estimator brings higher expected outcomes than existing methods in various settings. An R package dtrSurv is available on CRAN.
