In randomized controlled trials with treatment non-compliance, instrumental variable approaches are used to estimate complier average causal effects. We extend these approaches to cost-effectiveness analyses, where methods need to recognize the correlation between cost and health outcomes. We propose a Bayesian full likelihood approach, which jointly models the effects of random assignment on treatment received and the outcomes, and a three-stage least squares method, which acknowledges the correlation between the end points and the endogeneity of the treatment received. This investigation is motivated by the REFLUX study, which exemplifies the setting where compliance differs between the randomized controlled trial and routine practice. A simulation is used to compare the methods' performance. We find that failure to model the correlation between the outcomes and treatment received correctly can result in poor confidence interval coverage and biased estimates. By contrast, Bayesian full likelihood and three-stage least squares methods provide unbiased estimates with good coverage.

中文翻译：

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估计成本效益分析的编译器平均因果效应的方法。

在治疗不依从性的随机对照试验中，工具变量方法用于估计依从者的平均因果效应。我们将这些方法扩展到成本效益分析，其中方法需要认识到成本和健康结果之间的相关性。我们提出了一种贝叶斯全似然方法，它联合模拟随机分配对接受的治疗和结果的影响，以及一个三阶段最小二乘法，它承认终点和接受的治疗的内生性之间的相关性。这项调查的动机是 REFLUX 研究，该研究举例说明了随机对照试验和常规实践之间依从性不同的情况。模拟用于比较这些方法的性能。我们发现，未能对结果和正确接受的治疗之间的相关性进行建模可能会导致置信区间覆盖率低和估计有偏差。相比之下，贝叶斯完全似然法和三阶段最小二乘法提供了具有良好覆盖率的无偏估计。