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 and consistent while confidence intervals achieve nominal coverage. We illustrate our approach on the MIMIC-III database, focusing on ITRs for initiation of renal replacement therapy in patients with acute kidney injury.
翻译:个人化治疗规则(ITRs)是依据个人特点向个人建议治疗的决定性决定规则。虽然在医学中无处不在,但在随机控制的试验中很难对ITRs进行评价。为了从观察数据中评估ITRs,我们采用了一个新的概率模型,区分两种情况:(1) 新开发的ITR(数据来自没有病人执行ITR的人群)的情况,即数据来自部分执行的ITR(数据来自一些身份不明的病人实施ITR的人口)的情况。在前一种情况下,我们提议了一个程序,以探讨各种执行计划下ITR的影响。在后一种情况下,除了因果关系推断的根本问题之外,我们需要处理另一个潜在的变异性说明执行情况。为了评估ITRs(这种情况下的数据来自没有病人执行ITRs)的情况,我们建议了一个依赖预期-最大程度算法的估计程序。在蒙特卡洛模拟我们的估计者看起来是公正和一致的,而信任间隔则达到名义覆盖。我们用MIICIII数据库来说明我们的方法,重点是急性肾上损伤的ITRs 和肾上重置疗程的ITRs。