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.

翻译：个体化治疗规则是根据患者特征推荐个体化治疗的确定性决策规则。尽管在医学领域广泛应用，但个体化治疗规则几乎从未在随机对照试验中得到评估。为从观测数据中评估个体化治疗规则，我们引入了一个新的概率模型，并区分两种情形：i) 新开发个体化治疗规则的情形，此时数据来自无患者实施该规则的人群；ii) 部分实施个体化治疗规则的情形，此时数据来自部分未知患者已实施该规则的人群。对于前者，我们提出了一种探索不同实施方案下个体化治疗规则影响的程序。对于后者，在因果推断基本问题的基础上，还需处理一个表征实施状态的额外潜变量。为评估此情形下的个体化治疗规则，我们提出了一种基于期望最大化算法的估计程序。蒙特卡洛模拟显示，我们的估计量无偏，且置信区间达到名义覆盖率。我们通过MIMIC-III数据库对急性肾损伤患者透析启动的个体化治疗规则进行了实例分析。