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 observed factors (confounders) affect both observed treatment and time-to-event outcomes. The estimated treatment effect from DeepSDRF enables us to develop recommender algorithms with the correction for selection bias. 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 the eICU Research Institute (eRI) database. To the best of our knowledge, this is the first time that causal models are used to address 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提法,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(CADR)功能的问题,在历史数据中,观察到的因素(召集人)既影响观察治疗结果,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建议人算法并纠正选择偏差。我们比较了基于随机搜索和强化学习的两种推荐人算法,发现病人的结果相似。我们测试了深海生存剂量反应和相应的建议人的广泛模拟研究和eICU研究所数据库。据我们所知,这是首次使用因果模型在医学环境中用观察数据处理持续治疗效应。