Objective: Randomised controlled trials (RCTs) are widely considered as gold standard for assessing the effectiveness of new health interventions. When treatment non-compliance is present in RCTs, the treatment effect in the subgroup of participants who complied with their original treatment allocation, the Complier Average Causal Effect (CACE), is a more representative measure of treatment efficacy than the average treatment effect. Through simulation we aim to compare the two most common methods employed in practice to estimate CACE. Methods: We considered the Per-Protocol and Instrumental Variables (IV) analyses. Based on a real study, we simulated hypothetical trials by varying factors related to non-compliance and compared the two methods by the bias of the estimate, mean squared error and $95\%$ coverage of the true value. Results: For binary compliance, the IV estimator was always unbiased for CACE, while the Per-Protocol estimator was unbiased for random non-compliance or when participants with good or bad conditions always received the treatment. For partial compliance, the IV estimator was less biased when participants with better conditions always received the treatment and those with worse conditions always received the control or vice versa, while the Per-Protocol estimator was less biased when participants with good or bad conditions never received the treatment.

翻译：目的：随机对照试验（RCTs）被广泛视为评估新健康干预措施有效性的金标准。当RCTs中出现治疗非依从情况时，在遵循原始治疗分配的参与者亚组中的治疗效果——编译器平均因果效应（CACE），比平均治疗效果更能代表治疗效力。通过模拟研究，我们旨在比较实践中用于估计CACE的两种最常用方法。方法：我们考虑了按方案分析和工具变量（IV）分析。基于一项真实研究，我们通过改变与非依从相关的因素来模拟假设试验，并通过估计偏差、均方误差和真实值的$95\%$覆盖范围来比较两种方法。结果：对于二值依从情况，IV估计量对CACE始终无偏，而按方案估计量在随机非依从情况下或在条件好或差的参与者始终接受治疗时无偏。对于部分依从情况，当条件较好的参与者始终接受治疗而条件较差的参与者始终接受对照（或反之）时，IV估计量偏差较小；而当条件好或差的参与者从未接受治疗时，按方案估计量偏差较小。