In recent years, the Data Monitoring Committee (DMC) has played a crucial role in clinical trials, especially in protecting the safety of subjects and ensuring the integrity and credibility of trial results. With the rise of real-world study, the application of DMC in real-world settings has also gradually attracted attention. In this paper, the application of DMC in real-world study is discussed, the functions of DMC in safety monitoring, efficacy monitoring, research operation quality monitoring, and recommendations for modifications of research designs are analyzed, and the differences in DMC's functions between real-world study and randomized controlled trials are compared. Through case analysis, this paper summarizes the best practices of DMC in real-world study, so as to provide references for future related research.
In the context of increasingly stringent clinical trial quality control, the establishment of Data Monitoring Committee (DMC) has become essential for ensuring scientific rigor and ethical compliance. As a key tool for DMC decision-making, interim analysis reports play a critical role in assessing trial safety and efficacy. However, current DMC reports often exhibit significant shortcomings, such as complexity, lack of logical structure, data redundancy, and limited practical utility. These issues hinder effective risk-benefit evaluations required by regulatory standards. This paper identifies and analyzes these deficiencies and their associated risks, aiming to provide actionable recommendations for developing systematic, concise, and accurate DMC reports. Such improvements will support DMCs in making informed, scientifically sound decisions while enhancing the overall quality of clinical trial oversight.
Randomization was the basis for the design and conduct of clinical trials. However, the traditional randomized controlled trials (RCTs) were often randomized in a fixed manner with unbalanced potential covariates, which spured researchers to develop a more flexible and practical randomization method. Thus, the adaptive randomization emerged as the time needed. In this paper, the application of adaptive randomization in clinical trials was introduced, and its key points of implementation, advantages and disadvantages were summarized. The development space of the adaptive randomization in clinical applications was also discussed, and it provided evidence for the development of the drug clinical trials in China.
Master protocol with adaptive design is a new complex innovative trial design that combines an adaptive treatment strategy and master protocol. It is more flexible and adjustable. In the complex clinical trial environment, the dynamics emphasized in this design are consistent with the idea of traditional Chinese medicine (TCM) syndrome differentiation and treatment. In this study, we summarized its concept, characteristics and advantages, and we also discussed its application in TCM clinical research. We hope this paper can provide more thinking and suggestions for TCM clinical trials.
The Institute for Clinical and Economic Review (ICER) has long been committed to promoting fair pricing and equitable access in U.S. healthcare, and has developed a value assessment framework to guide this work. ICER’s value assessment framework was first conceived in 2015 and subsequently updated in 2017 and 2020. This study introduces the ICER value assessment framework (2023). It focuses on elucidating the major revisions within three key areas: cost-effectiveness analysis, health technology assessment (HTA) methods related to health equity and patient engagement, and discusses these changes in light of their central methodological implications. The aim is to provide informative insights and methodological references that may be of value for the development and refinement of HTA practices in China.
Objective This study aims to systematically review the current application status of sequential multiple assignment randomized trials (SMART) in the past decade. The goal is to clarify the research fields, research objectives, design elements, and data analysis methods of SMART clinical reports, and to provide evidence-based references for the subsequent standardized design and reporting of SMART. Methods The PubMed, Embase, Web of Science, APA PsychInfo, Scopus, CNKI, WanFang Data, VIP databases were electronically searched to collect studies on SMART-related clinical studies published from 2015 to 2025. Descriptive statistics and inductive thematic analysis methods were used to summarize and analyse the extracted data. Results A total of 153 articles were included. The results showed that the number of publications has been increasing year by year; the research was mainly concentrated in the United States (n=133), followed by China (n=11). The research fields were mainly in psychology and psychiatry (42%), endocrinology (12%), and cancer (11%). The research goals were diverse, with the comparison of dynamic treatment strategies (14%) being the most common. In terms of trial design, the initial grouping was mostly two groups, with a 1: 1 ratio between groups being the most common; two-stage multiple randomizations were mostly used, ultimately forming 4-8 subgroups; sample sizes were mostly between 100 and 500 cases (48%). Data analysis methods were diverse, depending on the research purpose, data characteristics, and design type. Longitudinal data analysis mainly used linear mixed-effects models (66 times) and generalized estimating equations (31 times), and Q-learning (16 times) was the mainstream method for constructing optimal decision rules. Additionally, the study found that the detail related to data processing was generally underreported. Conclusion As a primary method for evaluating clinical dynamic treatment strategies, SMART has issues such as imbalanced geographical and disciplinary distribution, incomplete reporting of design elements, and insufficient standardization of data processing. In the future, it is necessary to promote the expansion of international reporting standards, strengthen methodological research, and encourage the validation of its extrapolation and clinical translation value in a wider range of disease fields and regions.
