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 source 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 traditional Chinese medicine 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 traditional Chinese medicine 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.