Real-world evidence (RWE), the understanding of treatment effectiveness in clinical practice generated from longitudinal patient-level data from the routine operation of the healthcare system, is thought to complement evidence on the efficacy of medications from randomized controlled trials (RCTs). RWE studies follow a structured approach. (1) A design layer decides on the study design, which is driven by the study question and refined by a medically informed target population, patient-informed outcomes, and biologically informed effect windows. Imagining the randomized trial we would ideally perform before designing an RWE study in its likeness reduces bias; the new-user active comparator cohort design has proven useful in many RWE studies of diabetes treatments. (2) A measurement layer transforms the longitudinal patient-level data stream into variables that identify the study population, the pre-exposure patient characteristics, the treatment, and the treatment-emergent outcomes. Working with secondary data increases the measurement complexity compared to primary data collection that we find in most RCTs. (3) An analysis layer focuses on the causal treatment effect estimation. Propensity score analyses have gained in popularity to minimize confounding in healthcare database analyses. Well-understood investigator errors, like immortal time bias, adjustment for causal intermediates, or reverse causation, should be avoided. To increase reproducibility of RWE findings, studies require full implementation transparency. This article integrates state-of-the-art knowledge on how to conduct and review RWE studies on diabetes treatments to maximize study validity and ultimately increased confidence in RWE-based decision making.

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对糖尿病治疗的临床结果进行真实世界证据研究

真实世界证据 (RWE)，即从医疗保健系统日常运作的纵向患者水平数据中得出的对临床实践中治疗效果的理解，被认为是对随机对照试验 (RCT) 药物疗效证据的补充。RWE 研究遵循结构化方法。(1) 设计层决定研究设计，该设计由研究问题驱动，并由医学知情的目标人群、患者知情的结果和生物学知情的效果窗口细化。想象一下我们在设计 RWE 研究之前最好进行的随机试验可以减少偏见；新用户主动比较器队列设计已在许多 RWE 糖尿病治疗研究中证明是有用的。(2) 测量层将纵向患者级数据流转换为变量，用于识别研究人群、暴露前患者特征、治疗和治疗出现的结果。与我们在大多数 RCT 中发现的主要数据收集相比，使用二手数据会增加测量的复杂性。(3) 分析层侧重于因果处理效果估计。倾向评分分析越来越受欢迎，以最大限度地减少医疗保健数据库分析中的混淆。应该避免众所周知的调查员错误，例如不朽的时间偏差、因果中间体的调整或反向因果关系。为了提高 RWE 研究结果的可重复性，研究需要完全的实施透明度。