Objective To evaluate the efficacy and safety of mifepristone for perimenopause dysfunctional uterine bleeding (PDUB). Methods Such databases as VIP, CNKI, Wanfang and CBM were retrieved for collecting randomized controlled trials (RCTs) on mifepristone for PDUB. The quality of included studies was evaluated and Meta-analysis was performed according to the Cochrane methods. Results Forty RCTs involving 3 850 PDUB patients were included. The control group was divided into two sub-groups according to the features of intervention drugs: the sub-group of diagnostic curettage plus progestational hormone, and the sub-group of diagnostic curettage plus antiestrogenic drugs. The Meta-analysis indicated that compared with the sub-group of diagnostic curettage plus progestational hormone, the diagnostic curettage plus mifepristone group was more effective to increase the total effective rate, such as improving symptoms and signs of PDUB (RR=1.11, 95%CI 1.06 to 1.16, Plt;0.000 01), and to reduce recurrence (RR=0.44, 95%CI 0.36 to 0.52, Plt;0.000 01). But no differences were found between the two groups in the change of endometrial thickness, contents of hemoglobin, and serum level of FSH, LH, E2 and P hormone. Both the intervention and control groups appeared mild adverse reactions, such as rashes, tidal fever, nausea, anorexia, vomiting and breast distending, but with no liver and kidney damages. The long-term safety failed to be evaluated due to short follow-up time. Conclusion Based on this review, diagnostic curettage plus mifepristone shows certain advantage in the treatment of PDUB including the total effective rate and reducing recurrence. But there is no difference in regulating sex hormone level, inhibiting endometrial proliferation and improving anemia compared with the group of diagnostic curettage plus progestational hormone. However, this evidence is not b enough due to the low quality of included trials, possible bias risk, and failure of evaluating its long-term safety.
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.
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.