A dynamic treatment regimen (DTR) is a sequence of decision rules that can alter treatments or doses based on outcomes from prior treatment. In the case of two lines of treatment, a DTR specifies first-line treatment, and second-line treatment for responders and treatment for non-responders to the first-line treatment. A sequential, multiple assignment, randomized trial (SMART) is one such type of trial that has been designed to assess DTRs. The primary goal of our project is to identify the treatments, covariates, and their interactions result in the best overall survival rate. Many previously proposed methods to analyze data with survival outcomes from a SMART use inverse probability weighting and provide non-parametric estimation of survival rates, but no other information. Other methods have been proposed to identify and estimate the optimal DTR, but inference issues were seldom addressed. We apply a joint modeling approach to provide unbiased survival estimates as a mechanism to quantify baseline and time-varying covariate effects, treatment effects, and their interactions within regimens. The issue of multiple comparisons at specific time points is addressed using multiple comparisons with the best method.

中文翻译：

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与生存结果 SMART 的最佳数据进行联合建模和多重比较。

动态治疗方案 (DTR) 是一系列决策规则，可以根据先前治疗的结果改变治疗或剂量。在双线治疗的情况下，DTR 指定一线治疗、对一线治疗有反应者的二线治疗以及对一线治疗无反应者的治疗。序贯、多重分配、随机试验 (SMART) 就是此类试验的一种，旨在评估 DTR。我们项目的主要目标是确定治疗方法、协变量及其相互作用，从而实现最佳的总体生存率。许多先前提出的方法来分析来自 SMART 的生存结果数据，使用逆概率加权并提供生存率的非参数估计，但没有其他信息。已经提出了其他方法来识别和估计最佳 DTR，但推理问题很少得到解决。我们应用联合建模方法来提供无偏生存估计，作为量化基线和时变协变量效应、治疗效果及其在治疗方案中的相互作用的机制。使用最佳方法进行多重比较来解决特定时间点的多重比较问题。