Precision medicine is an approach to health care in which treatment decisions are tailored to patient-level information. Statistical methods for the estimation of dynamic treatment regimes (DTRs) allow to uncover a sequence of personalized treatment rules for patients with chronic diseases. Of particular interest is the identification of an optimal DTR, that is, the sequence of treatment rules that yields the best expected outcome. This is a challenging task, especially when the outcome is a survival time subject to right censoring or when available data are from observational studies. Dynamic weighted survival modelling (DWSurv) has been demonstrated to be theoretically robust and is accessible to users. We describe its implementation using the DWSurv function in the R package DTRreg. We review on the theory underlying DWSurv and demonstrate its use with hypothetical, and real-life inspired, simulated data sets.

中文翻译：

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具有生存终点的最佳动态治疗方案：在 R 包 DTRreg 中引入 DWSurv

精准医疗是一种医疗保健方法，其中治疗决策是根据患者级别的信息量身定制的。用于估计动态治疗方案 (DTR) 的统计方法可以揭示一系列针对慢性病患者的个性化治疗规则。特别令人感兴趣的是确定最佳 DTR，即产生最佳预期结果的治疗规则序列。这是一项具有挑战性的任务，尤其是当结果是受右删失的生存时间或可用数据来自观察性研究时。动态加权生存建模 (DWSurv) 已被证明在理论上是稳健的，并且可供用户使用。我们使用 R 包 DTRreg 中的 DWSurv 函数描述其实现。