论文标题
从观察数据评估个人治疗规则的综合框架
A Comprehensive Framework for the Evaluation of Individual Treatment Rules From Observational Data
论文作者
论文摘要
个性化的治疗规则（ITR）是确定性决策规则，根据其特征向个人推荐治疗。尽管无处不在，但在随机对照试验中几乎没有评估ITR。为了评估ITR与观察数据，我们介绍了一个新的概率模型，并区分了两种情况：i）新开发的ITR的情况，其中数据来自没有患者实施ITR的人群，ii）ii）部分实施了ITR的情况，其中数据是来自某些独立患者中ITR实施的人群中的数据。在前一种情况下，我们提出了一个程序，以探索在各种实施方案下探索ITR的影响。在后一种情况下，除了因果推断的基本问题之外，我们还需要处理其他表示实施的潜在变量。在这种情况下评估ITR，我们提出了一个依赖期望最大化算法的估计程序。在蒙特卡洛模拟中，我们的估计器似乎没有置信区间，以实现名义覆盖。我们说明了我们在模拟III数据库上的方法，重点介绍了急性肾脏损伤患者透析起始的ITR。
Individualized treatment rules (ITRs) are deterministic decision rules that recommend treatments to individuals based on their characteristics. Though ubiquitous in medicine, ITRs are hardly ever evaluated in randomized controlled trials. To evaluate ITRs from observational data, we introduce a new probabilistic model and distinguish two situations: i) the situation of a newly developed ITR, where data are from a population where no patient implements the ITR, and ii) the situation of a partially implemented ITR, where data are from a population where the ITR is implemented in some unidentified patients. In the former situation, we propose a procedure to explore the impact of an ITR under various implementation schemes. In the latter situation, on top of the fundamental problem of causal inference, we need to handle an additional latent variable denoting implementation. To evaluate ITRs in this situation, we propose an estimation procedure that relies on an expectation-maximization algorithm. In Monte Carlo simulations our estimators appear unbiased with confidence intervals achieving nominal coverage. We illustrate our approach on the MIMIC-III database, focusing on ITRs for dialysis initiation in patients with acute kidney injury.
