We propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data, which we call the Deep Survival Dose Response Function (DeepSDRF). That is, we consider the problem of learning the conditional average dose response (CADR) function solely from historical data in which unobserved factors (confounders) affect both observed treatment and time-to-event outcomes. The estimated treatment effect from DeepSDRF enables us to develop recommender algorithms with explanatory insights. We compared two recommender approaches based on random search and reinforcement learning and found similar performance in terms of patient outcome. We tested the DeepSDRF and the corresponding recommender on extensive simulation studies and two empirical databases: 1) the Clinical Practice Research Datalink (CPRD) and 2) the eICU Research Institute (eRI) database. To the best of our knowledge, this is the first time that confounders are taken into consideration for addressing the continuous treatment effect with observational data in a medical context.
翻译:我们建议对临床生存数据环境中的持续治疗建议问题作一般性的表述,我们称之为深海生存剂量反应功能(DepSDRF),也就是说,我们考虑的只是从历史数据中学习有条件平均剂量反应功能的问题,在这些数据中,未观察到的因素(召集人)既影响观察的治疗结果,也影响时间到活动的结果。深海生存反应系统的估计治疗效应使我们得以制定带有解释性洞察力的推荐算法。我们比较了基于随机搜索和强化学习的两种建议性方法,发现病人结果的相似性。我们测试了深海生存剂量反应和相应的建议,进行了广泛的模拟研究和两个经验数据库:1)临床实践研究数据链接(CPD)和2),电子疾病分类研究所(eRI)数据库。我们最了解的是,这是第一次考虑将连续治疗效应与医学方面的观察数据结合起来。