Comparative effectiveness research frequently employs the instrumental variable design since randomized trials can be infeasible for many reasons. In this study, we investigate and compare treatments for emergency cholecystitis -- inflammation of the gallbladder. A standard treatment for cholecystitis is surgical removal of the gallbladder, while alternative non-surgical treatments include managed care and pharmaceutical options. As randomized trials are judged to violate the principle of equipoise, we consider an instrument for operative care: the surgeon's tendency to operate. Standard instrumental variable estimation methods, however, often rely on parametric models that are prone to bias from model misspecification. We outline instrumental variable estimation methods based on the doubly robust machine learning framework. These methods enable us to employ various machine learning techniques for nuisance parameter estimation and deliver consistent estimates and fast rates of convergence for valid inference. We use these methods to estimate the primary target causal estimand in an IV design. Additionally, we expand these methods to develop estimators for heterogeneous causal effects, profiling principal strata, and a sensitivity analyses for a key instrumental variable assumption. We conduct a simulation study to demonstrate scenarios where more flexible estimation methods outperform standard methods. Our findings indicate that operative care is generally more effective for cholecystitis patients, although the benefits of surgery can be less pronounced for key patient subgroups.

翻译：比较有效性研究常采用器械变量设计，因为随机试验可能因多种原因不可行。本研究探讨并比较了急诊胆囊炎（胆囊炎症）的治疗方案。胆囊炎的标准治疗是手术切除胆囊，而替代的非手术治疗包括保守治疗和药物选择。由于随机试验被认为违背了等价原则，我们考虑手术治疗的器械变量：外科医生进行手术的倾向性。然而，传统的器械变量估计方法通常依赖于参数模型，容易因模型设定错误而产生偏倚。我们概述了基于双重稳健机器学习框架的器械变量估计方法。这些方法能够利用多种机器学习技术进行干扰参数估计，提供一致的估计量和快速的收敛速度，从而实现有效的推断。我们使用这些方法估计器械变量设计中的主要目标因果估计量。此外，我们扩展了这些方法，开发了用于异质性因果效应估计、主层特征分析以及关键器械变量假设敏感性分析的估计器。我们进行了模拟研究，展示更灵活的估计方法优于传统方法的场景。研究结果表明，手术护理通常对胆囊炎患者更为有效，但关键患者亚组从手术中获益的程度可能较弱。