The modern clinical research evaluation systems have increasingly emphasized the evaluation of individual patients' clinical characteristics, diagnosis and treatment plans, and complex intervention measures. Traditional randomized controlled trials evaluate fixed interventions and non-adaptive treatment plans, which cannot meet the needs of evaluating adaptive interventions. This has made researchers more inclined to explore an individualized and adaptive clinical trial design, and sequential multiple assignment randomized trial (SMART) has emerged as needed. This article introduces the principles, key elements, and implementation points of SMART design, further explores the limitations of the mismatch between traditional Chinese medicine clinical trial design and syndrome differentiation treatment, and proposes that SMART design can meet the needs of traditional Chinese medicine clinical trials to inspire researchers in designing their plans.
In the context of increasingly stringent clinical trial quality control, the establishment of Data Monitoring Committees (DMCs) 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.
Sample size re-estimation (SSR) refers to the recalculation of the sample size using the existing trial data as original planned to ensure that the final statistical test achieved the pre-defined goals. SSR can enhance research efficiency, save trial costs, and accelerate the research process. Depending on whether the group assignment of the patients is known, SSR is divided into blinded sample size re-estimation and unblinded sample size re-estimation. Blinded sample size re-estimation can estimate the variance of the primary evaluation index through the EM algorithm or single sample variance re-estimation method, and then calculate the sample size. Unblinded sample size re-estimation can calculate the sample size by estimating the overall variance or therapeutic effect difference, but it needs to control the family wise type I error (FWER) rate. Cui-Hung-Wang method, conditional rejection probability method, P-value combination method, conditional error function, and promising zone are common methods used to control FWER. Currently, there are application examples of SSR methods. With the maturation of related theories and the popularization of methods, it is expected to be widely applied in clinical trials, especially in traditional Chinese medicine clinical trials in the future.