Precision medicine leverages patient heterogeneity to estimate individualized treatment regimens, formalized, data-driven approaches designed to match patients with optimal treatments. In the presence of competing events, where multiple causes of failure can occur and one cause precludes others, it is crucial to assess the risk of the specific outcome of interest, such as one type of failure over another. This helps clinicians tailor interventions based on the factors driving that particular cause, leading to more precise treatment strategies. Currently, no precision medicine methods simultaneously account for both survival and competing risk endpoints. To address this gap, we develop a nonparametric individualized treatment regime estimator. Our two-phase method accounts for both overall survival from all events as well as the cumulative incidence of a main event of interest. Additionally, we introduce a multi-utility value function that incorporates both outcomes. We develop random survival and random cumulative incidence forests to construct individual survival and cumulative incidence curves. Simulation studies demonstrated that our proposed method performs well, which we applied to a cohort of peripheral artery disease patients at high risk for limb loss and mortality.

翻译：精准医学利用患者异质性来估计个体化治疗方案，这些方案是形式化的、数据驱动的方法，旨在为患者匹配最优治疗。在存在竞争事件（即多种失效原因可能发生，且一种原因会排除其他原因）的情况下，评估特定关注结局（例如一种失效类型相对于另一种）的风险至关重要。这有助于临床医生根据驱动该特定原因的因素来定制干预措施，从而制定更精确的治疗策略。目前，尚无精准医学方法能同时考虑生存结局和竞争风险终点。为填补这一空白，我们开发了一种非参数个体化治疗策略估计器。我们的两阶段方法同时考虑了所有事件的总生存期以及主要关注事件的累积发生率。此外，我们引入了一个结合了两种结局的多效用价值函数。我们开发了随机生存森林和随机累积发生率森林来构建个体生存曲线和累积发生率曲线。模拟研究表明，我们提出的方法表现良好，我们将其应用于一组面临截肢和死亡高风险的外周动脉疾病患者队列。