Design and Challenges of Real World Data as Control Group in A Randomized Controlled Trial of Chinese Medicine

HU Jing; LI Bo; ZHANG Huina; FENG Shuo; LIAO Xing

doi:10.13422/j.cnki.syfjx.20230194

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Design and Challenges of Real World Data as Control Group in A Randomized Controlled Trial of Chinese Medicine

更新时间：2022-12-08

- Design and Challenges of Real World Data as Control Group in A Randomized Controlled Trial of Chinese Medicine
- Chinese Journal of Experimental Traditional Medical Formulae Vol. 29, Issue 1, Pages: 181-187(2023)
- 作者机构：
  
  1.首都医科大学附属北京中医医院，北京市中医药研究所，北京 100010
  2.北京中医药循证医学中心，北京 100010
  3.中国中医科学院中医临床基础医学研究所，循证医学基础研究室，北京 100700
- 作者简介：
- 基金信息：
- DOI：10.13422/j.cnki.syfjx.20230194
  CLC： R242;R2-031;R965.3;R969.4
- Received：26 April 2022，
  
  Published Online：01 September 2022，
  
  Published：05 January 2023
- 稿件说明：
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胡晶,李博,张会娜等.中医药随机对照试验中采用真实世界数据作为对照组的研究设计及挑战[J].中国实验方剂学杂志,2023,29(01):181-187.

HU Jing,LI Bo,ZHANG Huina,et al.Design and Challenges of Real World Data as Control Group in A Randomized Controlled Trial of Chinese Medicine[J].Chinese Journal of Experimental Traditional Medical Formulae,2023,29(01):181-187.
胡晶,李博,张会娜等.中医药随机对照试验中采用真实世界数据作为对照组的研究设计及挑战[J].中国实验方剂学杂志,2023,29(01):181-187. DOI： 10.13422/j.cnki.syfjx.20230194.

HU Jing,LI Bo,ZHANG Huina,et al.Design and Challenges of Real World Data as Control Group in A Randomized Controlled Trial of Chinese Medicine[J].Chinese Journal of Experimental Traditional Medical Formulae,2023,29(01):181-187. DOI： 10.13422/j.cnki.syfjx.20230194.

摘要

在设计随机对照试验（RCT）时，如果对照组存在患者招募和入组困难的情况，就会影响试验整体实施。近年来，真实世界数据（RWD）作为除RCT之外的数据来源，在医疗领域中发挥着越来越重要的作用。中医药RCT中可以尝试采用将RWD作为对照组的研究设计，不仅可以有效解决中医药RCT西医对照组患者入组困难的问题，同时能提供有力证据来评价中医药的疗效。倾向评分法目前已广泛应用于真实世界研究中混杂因素的处理，该文对RCT采用RWD作为对照组的这类设计中，基于倾向评分法常见的4种研究设计形式以实例分别进行了介绍，包括不对称随机分配、基于倾向评分分层法的两阶段设计、倾向评分联合复合似然法及倾向评分多种方法的联合。同时，这种设计类型也存在着方法学的挑战，包括RWD数据源必须是高质量且关键信息需要规范收集、RCT和RWD患者基线特征应该具有可比性、协变量选择时需要把所有已知与干预措施和结局相关的协变量都纳入进行分析等。在中医药领域采用这种设计时，还存在着有些RWD中医证型信息缺失、中医结局指标缺失等问题，在使用RWD时，需要根据数据实际情况决定如何分析。该文对以RWD作为RCT对照组的设计类型及面临的方法学挑战进行了介绍，期望能为研究者今后使用这类设计提供方法学借鉴。

Abstract

In the design of randomized controlled trials （RCT）， difficulties in patient recruitment and enrollment of control group would limit the overall implementation of the trials. In recent years， as a data source， real-world data （RWD） plays an increasingly important role in the medical field. In RCT of Chinese medicine， RWD could be designed as control group. This design can effectively solve the problem of inclusion difficulty for the patients in the western medicine control group of traditional Chinese medicine（TCM） RCT， it also can provide high quality evidence to evaluate the efficacy of TCM. In recent years， propensity score method has been widely used to deal with confounding factors in real world study. In this paper， four common research designs based on propensity score method were introduced with examples， including propensity score matching and data augmentation， two-stage design of propensity score stratification method， propensity score-integrated composite likelihood approach， and combination of different propensity score methods. However， there are some methodological challenges in this type of design， including the RWD data sources must be of high quality and the key information needs to be collected in a standardized method， the baseline characteristics of RCT and RWD patients should be comparable， and all known covariates related to the intervention and outcome need to be included for analysis. When adopting this design in the field of TCM， there are still some problems such as the lack of TCM syndrome information and TCM outcomes in RWD. When using RWD， it is necessary to decide how to analyze according to the data condition. This paper discussed the design types and methodological challenges of using RWD as control group in RCT， hoping to provide methodological ideas for researchers to use this type of design in the future.

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