Progress in methodological research on bridging the efficacy-effectiveness gap of clinical interventions(2): to improve the extrapolation of efficacy
10.3760/cma.j.cn112338-20230925-00190
- VernacularTitle:临床干预措施效力-效果差距弥合的方法学研究进展（二）：增强临床试验结果的外推性
- Author:
Zuoxiang LIU
1
;
Zilin LONG
;
Zhirong YANG
;
Shuyuan SHI
;
Xinran XU
;
Houyu ZHAO
;
Zuyao YANG
;
Zhu FU
;
Haibo SONG
;
Tengfei LIN
;
Siyan ZHAN
;
Feng SUN
Author Information
1. 北京大学公共卫生学院流行病与卫生统计学系，北京 100191
- Keywords:
Randomized controlled trial;
Real-world data;
Efficacy-effectiveness gap;
Extrapolation
- From:
Chinese Journal of Epidemiology
2024;45(4):579-584
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Randomized controlled trials (RCT) usually have strict implementation criteria. The included subjects' characteristics of the conditions for the intervention implementation are quite different from the actual clinical environment, resulting in discrepancies between the risk-benefit of interventions in actual clinical use and the risk-benefit shown in RCT. Therefore, some methods are needed to enhance the extrapolation of RCT results to evaluate the real effects of drugs in real people and clinical practice settings.Methods:Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31 st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results:A total of 12 articles were included. Three methods in the included literature focused on: ①improving the design of traditional RCT to increase population representation; ②combining RCT Data with real-world data (RWD) for analysis;③calibrating RCT results according to real-world patient characteristics.Conclusions:Improving the design of RCT to enhance the population representation can improve the extrapolation of the results of RCT. Combining RCT data with RWD can give full play to the advantages of data from different sources; the results of the RCT were calibrated against real-world population characteristics so that the effects of interventions in real-world patient populations can be predicted.
