genRCT: a statistical analysis framework for generalizing RCT findings to real-world population

When evaluating the real-world treatment effect, the analysis based on randomized clinical trials (RCTs) often introduces generalizability bias due to the difference in risk factors between the trial participants and the real-world patient population. This problem of lack of generalizability associated with the RCT-only analysis can be addressed by leveraging observational studies with large sample sizes that are representative of the real-world population. A set of novel statistical methods, termed “genRCT”, for improving the generalizability of the trial has been developed using calibration weighting, which enforces the covariates balance between the RCT and observational study. This paper aims to review statistical methods for generalizing the RCT findings by harnessing information from large observational studies that represent real-world patients. Specifically, we discuss the choices of data sources and variables to meet key theoretical assumptions and principles. We introduce and compare estimation methods for continuous, binary, and survival endpoints. We showcase the use of the R package genRCT through a case study that estimates the average treatment effect of adjuvant chemotherapy for the stage 1B non-small cell lung patients represented by a large cancer registry.在评估真实世界治疗效果时，基于随机对照试验（randomized clinical trials, RCTs）的分析常会因试验受试者与真实世界患者群体在风险因素上的差异，引入推广性偏倚（generalizability bias）。仅基于随机对照试验的分析所存在的推广性不足问题，可通过利用具有大样本量且能代表真实世界人群的观察性研究予以解决。研究人员采用校准加权法（calibration weighting）开发出一套名为"genRCT"的新型统计方法，用于提升试验的推广性；该方法可实现随机对照试验与观察性研究间的协变量平衡（covariates balance）。本文旨在综述通过利用可代表真实世界患者的大型观察性研究信息，以推广随机对照试验研究结果的统计方法。具体而言，本文将讨论为满足关键理论假设与原则所需的数据来源与变量选择方案。本文将介绍并对比针对连续型、二分类及生存结局指标（survival endpoints）的估计方法。本文将通过一项案例研究展示R包genRCT的应用方法，该案例基于大型癌症登记数据库所代表的1B期非小细胞肺癌患者群体，估算辅助化疗的平均治疗效果。