Regulatory agencies typically evaluate the efficacy and safety of new interventions and grant commercial approval based on randomized controlled trials (RCTs). Other major healthcare stakeholders, such as insurance companies and health technology assessment agencies, while basing initial access and reimbursement decisions on RCT results, are also keenly interested in whether results observed in idealized trial settings will translate into comparable outcomes in real world settings—that is, into so-called “real world” effectiveness. Unfortunately, evidence of real world effectiveness for new interventions is not available at the time of initial approval. To bridge this gap, statistical methods are available to extend the estimated treatment effect observed in a RCT to a target population. The generalization is done by weighting the subjects who participated in a RCT so that the weighted trial population resembles a target population. We evaluate a variety of alternative estimation and weight construction procedures using both simulations and a real world data example using two clinical trials of an investigational intervention for Alzheimer's disease. Our results suggest an optimal approach to estimation depends on the characteristics of source and target populations, including degree of selection bias and treatment effect heterogeneity.

中文翻译：

---

对现实世界人群中平均治疗效果的重新加权估计量的比较

监管机构通常会根据随机对照试验 (RCT) 评估新干预措施的有效性和安全性，并授予商业批准。其他主要的医疗保健利益相关者，例如保险公司和卫生技术评估机构，在根据 RCT 结果做出初始获取和报销决定的同时，也对理想化试验环境中观察到的结果是否会转化为现实环境中的可比结果非常感兴趣——即，进入所谓的“现实世界”效力。不幸的是，在最初批准时，尚无新干预措施在现实世界中的有效性的证据。为了弥补这一差距，可以使用统计方法将 RCT 中观察到的估计治疗效果扩展到目标人群。概括是通过对参与 RCT 的受试者进行加权来完成的，以便加权的试验人群类似于目标人群。我们使用模拟和真实世界数据示例评估各种替代估计和权重构建程序，使用两个临床试验对阿尔茨海默病进行研究干预。我们的结果表明，最佳估计方法取决于来源和目标人群的特征，包括选择偏差的程度和治疗效果的异质性。我们使用模拟和真实世界数据示例评估各种替代估计和权重构建程序，使用两个临床试验对阿尔茨海默病进行研究干预。我们的结果表明，最佳估计方法取决于来源和目标人群的特征，包括选择偏差的程度和治疗效果的异质性。我们使用模拟和真实世界数据示例评估各种替代估计和权重构建程序，使用两个临床试验对阿尔茨海默病进行研究干预。我们的结果表明，最佳估计方法取决于来源和目标人群的特征，包括选择偏差的程度和治疗效果的异质性。