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Abstract：
Sequences of treatments that adapt to a patient\'s changing condition over time are often needed for the management of chronic diseases. An adaptive treatment strategy (ATS) consists of personalized treatment rules to be applied through the course of a disease that input the patient\'s characteristics at the time of decision-making and output a recommended treatment. An optimal ATS is the sequence of tailored treatments that yields the best clinical outcome for patients sharing similar characteristics. Methods for estimating optimal adaptive treatment strategies, which must disentangle short- and long-term treatment effects, can be theoretically involved and hard to explain to clinicians, especially when the outcome to be optimized is a survival time subject to right-censoring. In this paper, we describe dynamic weighted survival modeling, a method for estimating an optimal ATS with survival outcomes. Using data from the Clinical Practice Research Datalink, a large primary-care database, we illustrate how it can answer an important clinical question about the treatment of type 2 diabetes. We identify an ATS pertaining to which drug add-ons to recommend when metformin in monotherapy does not achieve the therapeutic goals.
摘要：
慢性疾病的管理通常需要适应患者随时间变化的病情的治疗顺序。适应性治疗策略（ATS）由个性化治疗规则组成，该规则将在整个疾病过程中应用，该规则在决策时输入患者的特征并输出推荐的治疗。最佳ATS是定制治疗的序列,其对于具有相似特征的患者产生最佳临床结果。估计最佳适应性治疗策略的方法，必须解开短期和长期治疗效果，从理论上讲可能涉及并且很难向临床医生解释，特别是当要优化的结果是受权利审查的生存时间时。在本文中,我们描述了动态加权生存建模，一种估计具有生存结果的最佳苯丙胺类兴奋剂的方法。使用来自临床实践研究数据链的数据，一个大型的初级保健数据库，我们说明了它如何回答有关2型糖尿病治疗的重要临床问题。我们确定了当二甲双胍单药治疗未达到治疗目标时推荐的ATS药物。
