A dynamic treatment regime is a sequence of medical decisions that adapts to the evolving clinical status of a patient over time. To facilitate personalized care, it is crucial to assess the probability of each available treatment option being optimal for a specific patient, while also identifying the key prognostic factors that determine the optimal sequence of treatments. This task has become increasingly challenging due to the growing number of individual prognostic factors typically available. In response to these challenges, we propose a Bayesian model for optimizing dynamic treatment regimes that addresses the uncertainty in identifying optimal decision sequences and incorporates dimensionality reduction to manage high-dimensional individual covariates. The first task is achieved through a suitable augmentation of the model to handle counterfactual variables. For the second, we introduce a novel class of spike-and-slab priors for the multi-stage selection of significant factors, to favor the sharing of information across stages. The effectiveness of the proposed approach is demonstrated through extensive simulation studies and illustrated using clinical trial data on severe acute arterial hypertension.

翻译：动态治疗方案是指根据患者随时间的临床状态演变而调整的一系列医疗决策。为促进个体化医疗，关键在于评估每种可用治疗方案对特定患者而言为最优的概率，同时识别确定最优治疗序列的关键预后因素。由于通常可获取的个体预后因素数量日益增多，这一任务变得更具挑战性。为应对这些难题，我们提出了一种用于优化动态治疗方案的贝叶斯模型，该模型能处理识别最优决策序列中的不确定性，并通过降维手段管理高维个体协变量。第一个目标通过引入适当的模型增强来处理反事实变量来实现。针对第二个目标，我们提出了一类新颖的多阶段显著因素选择尖峰-板状先验，以促进各阶段之间的信息共享。通过广泛的模拟研究验证了所提方法的有效性，并利用严重急性动脉高血压的临床试验数据进行了实例说明。