ObjectiveTo evaluate the quality of randomized controlled trials (RCTs) of Chinese medicine (TCM) formulated granules published in core journals in China. MethodsComputerized searches were conducted in CNKI, VIP, WanFang Data and CBM databases. The publicly published RCTs of TCM formulated granules were collected, with sources from Peking University Core, CSSCI and EI. The following information was extracted: including title, the first author, the journals name, type of disease, year of publication, and source of drug. The included studies were evaluated using the CONSORT extension for CHM formulas (CONSORT-CHM formulas 2017), which included 25 items from title, abstract and keywords, introduction, research methods, steps, results, discussion, and other information. ResultsA total of 125 papers were included, which mainly included digestive system diseases (n=25), respiratory system diseases (n=17), and circulatory system diseases (n=17). The results showed that the overall reporting quality of RCTs of TCM formulated granules was poor. After the publication of the CONSORT-CHM formulas 2017, the reporting quality of RCTs of TCM formulated granules had no significant changes, while some items were still reported with poor quality. For example, 42.2% of RCTs did not adequately report how to generate allocation sequence, 93.3% of RCTs did not adequately report allocation concealment, and 62.2% of RCTs did not adequately report how to solve the missing data. ConclusionThe quality of RCTs reports on TCM formula granules published in Chinese journals still needs to be improved. It is recommended that researchers, journals and reviewers attach importance to the application of CONSORT-CHM formula throughout the whole process of paper writing. In the future, more scientific and detailed requirements should be put forward for trial design and reporting standards in line with the characteristics of clinical trials of TCM formula granules.
Response-adaptive randomization (RAR) dynamically adjusts the probability of assigning patients to different groups, optimizing treatment efficacy and participant welfare. It is particularly suitable for clinical studies involving multiple interventions or dose-finding and seamless phase II/III trials. This paper systematically introduces the concept, principles, and types of RAR, as well as its application in clinical trials (including traditional Chinese medicine research). It also provides R implementation code, offering researchers practical tools aimed at promoting the adoption of RAR in clinical practice.
The 14th Five-Year Plan for National Health explicitly proposes elevating the comprehensive prevention and control strategy for chronic diseases to a national strategy, aiming to address the growing demand for long-term management and individualized treatment of chronic diseases. In this context, the adaptive treatment strategy (ATS), as an innovative treatment model, offers new ideas and methods for the management and treatment of chronic diseases through its flexible, personalized, and scientific characteristics. To construct ATS, the sequential multiple assignment randomized trial (SMART) has emerged as a research method for multi-stage randomized controlled trials. The SMART design has been widely used in international clinical research, but there is a lack of systematic reports and studies in China. This paper first introduces the basic principles of ATS and SMART design, and then focuses on two key elements of the SMART design: re-randomization and intermediate outcomes. Based on these two elements, four major types of SMART designs are summarized, including: (1) SMART designs in which the intermediate outcome corresponds to a single re-randomization scheme (the classical type), (2) SMART designs in which no intermediate outcome is embedded, (3) SMART designs in which the intermediate outcome corresponds to a different re-randomization scheme, and (4) SMART designs in which the intermediate outcome and the previous interventions jointly determine the re-randomization. These different types of SMART designs are suited for solving different types of scientific problems. Using specific examples, this paper also analyzes the conditions under which SMART designs are applicable in clinical trials and predicts that the mainstream analysis methods for SMART designs in the future will combine frequentist statistics and Bayesian statistics. It is expected that the introduction and analysis in this paper will provide valuable references for researchers and promote the widespread application and innovative development of SMART design in the field of chronic disease prevention, control, and treatment strategies in China.