Aiming at the problems of uneven reporting quality and lack of unified standards in SMART in the context of personalized medicine, this paper analyzes the application and reporting status of SMART, and summarizes the key problems of current research reporting. These include unclear description of core design elements, missing information on randomization and blinding, insufficient reporting of phase transition rules, opaque sample size estimation and multiple comparisons, and lack of detailed description of data processing procedures. Based on the CONSORT standard, this paper proposed to expand the reporting specifications for SMART design methodology. By adding and elaborating items, the standardized description of multi-stage randomization process, decision nodes, embedded dynamic treatment regimes comparison framework and dedicated flow chart are emphasized to enhance the transparency, reproducibility and reliability of the results of SMART study. This study aims to provide structured and standardized reporting recommendations for SMART trials, and promote the output of high-quality evidence and clinical transformation of dynamic treatment strategies.
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.