Recently dynamic treatment regimens (DTRs) have drawn considerable attention, as an effective tool for personalizing medicine. Sequential Multiple Assignment Randomized Trials (SMARTs) are often used to gather data for making inference on DTRs. In this article, we focus on regression analysis of DTRs from a two-stage SMART for competing risk outcomes based on cumulative incidence functions (CIFs). Even though there are extensive works on the regression problem for DTRs, no research has been done on modeling the CIF for SMART trials. We extend existing CIF regression models to handle covariate effects for DTRs. Asymptotic properties are established for our proposed estimators. The models can be implemented using existing software by an augmented-data approximation. We show the improvement provided by our proposed methods by simulation and illustrate its practical utility through an analysis of a SMART neuroblastoma study, where disease progression cannot be observed after death.

中文翻译：

---

动态治疗方案的累积发病率回归。

最近，动态治疗方案（DTR）作为一种个性化医学的有效工具已引起了广泛关注。顺序多重分配随机试验（SMART）通常用于收集数据以推断DTR。在本文中，我们专注于基于累积发生率函数（CIF）的两阶段SMART的DTR回归分析，以获取竞争风险结果。即使在DTR的回归问题上进行了大量研究，也没有为SMART试验的CIF建模进行任何研究。我们扩展了现有的CIF回归模型，以处理DTR的协变量效应。为我们提出的估计量建立了渐近性质。可以使用现有软件通过增强数据近似来实现模型。