Randomized controlled trials (RCTs) remain the gold standard for assessing the efficacy or safety of a given medication or intervention, with RCT data at the top of the hierarchy of evidence. By design, the randomization process eliminates the risk of selection bias and ensures that RCTs are unconfounded, thereby avoiding important sources of bias that are particularly challenging to control for in observational studies. However, conducting an RCT might not always be feasible owing to various constraints — the study might be unethical, too expensive, or too complex to design and monitor. RCTs might also lack generalizability, which is an important barrier in nephrology because patients with reduced kidney function are often excluded from trials. In these situations, observational studies represent an alternative, making use of the increasing amount of routinely collected administrative and health-care data and benefiting from the advantage of being implemented in real-world settings, thereby increasing their generalization potential. However, observational studies are more susceptible to confounding effects and bias than RCTs.

By applying the design of an RCT to observational data, the target trial emulation (TTE) framework aims to get the best of both worlds and limit the introduction of biases that often affect observational research, such as the prevalent-user bias and immortal-time biases. This approach is being increasingly and successfully used, including in kidney research^1,2. Emulating a trial from observational data essentially involves writing the protocol of a hypothetical trial that would answer the research question, explicitly specifying each one of its components (for instance, eligibility criteria, treatment strategies and modalities of treatment assignment). This protocol is useful to guide the study design and emulate each component of the trial using observational data. The key feature is the alignment of eligibility criteria, treatment assignment and start of follow-up, as would be the case in a RCT at randomization³.

随机对照试验 (RCT) 仍然是评估特定药物或干预措施的有效性或安全性的黄金标准，RCT 数据位于证据层次的顶部。通过设计，随机化过程消除了选择偏倚的风险，并确保随机对照试验不混杂，从而避免观察性研究中特别难以控制的重要偏倚来源。然而，由于各种限制，进行随机对照试验可能并不总是可行——研究可能不道德、过于昂贵或过于复杂而难以设计和监测。随机对照试验也可能缺乏普遍性，这是肾病学的一个重要障碍，因为肾功能下降的患者经常被排除在试验之外。在这些情况下，观察性研究代表了一种替代方案，利用越来越多的常规收集的行政和医疗保健数据，并受益于在现实环境中实施的优势，从而提高其普遍化潜力。然而，观察性研究比随机对照试验更容易受到混杂效应和偏倚的影响。

通过将 RCT 的设计应用于观察数据，目标试验模拟 (TTE) 框架旨在实现两全其美，并限制经常影响观察研究的偏差的引入，例如流行用户偏差和不朽时间偏见。这种方法正在得到越来越多的成功应用，包括在肾脏研究中^1,2 。从观察数据模拟试验本质上涉及编写一项假设试验的方案来回答研究问题，明确指定其每个组成部分（例如，资格标准、治疗策略和治疗分配方式）。该协议可用于指导研究设计并使用观察数据模拟试验的每个组成部分。关键特征是资格标准、治疗分配和随访开始的一致性，就像随机对照试验中的情况一样³ 。