With increasing amount of attention being paid to single case randomized controlled trial (N-of-1 trials), sample size estimation has become an important issue for clinical researchers. This paper mainly introduces the model and hypothesis of N-of-1 trials. Based on the hypothetical model, sample size estimation methods of fixed model and random model are proposed. The premises of the model application, formulas and examples are then given. It is expected in case of conduction N-of-1 trials, the correct methods are used to estimate sample size and improve the research quality of N-of-1 trials.
Objective To review the current application of sample size estimation in real-world studies (RWS), analyse parameter settings and commonly used methods, and provide methodological guidance for researchers conducting RWS. Methods First, ClinicalTrials.gov was searched to identify RWS with documented sample size calculations. Key information was extracted for descriptive analysis. Secondly, critical parameters and common estimation methods for RWS sample size calculations were systematically reviewed, and strategies were proposed for addressing common challenges. Finally, relevant international reporting standards were interpreted. Results The literature review included 44 clinical trials with a wide range of sample sizes (30 to 30 400 cases). While most studies detailed the sample size estimation process, the parameter settings were often incomplete and many failed to adequately consider the characteristics of real-world data. Therefore, we proposed key parameters for RWS sample size estimation, including effect size, significance level and statistical power. Researchers should also consider issues such as heterogeneity, confounding factors and data quality. This study clarified the essential elements of reporting sample size estimation. Conclusion Methodological guidance for real-world evidence sample size estimation is lacking. We advise researchers to standardise reporting procedures for sample size estimation in future studies and to set parameters reasonably based on research objectives, study design types and data characteristics. This will enhance the transparency and scientific rigour of real-world evidence.
As a novel research model that can address multiple research questions within an overall trial structure, master protocol design shares similarities with the clinical research on syndrome-based traditional Chinese medicine in terms of study design. The sample size estimation in master protocol design is characterized by analyzing the subtrials separately and re-estimation at interim analyses. Specific methods include the combination of Simon’s two-stage design and Bayesian hierarchical design that facilitates information borrowing. By drawing on these methods to estimate dynamically and adjust the sample size for each subtrial in a targeted manner, it is expected to provide a feasible approach for the methodological development of sample size estimation in the field of clinical research on syndrome-based traditional Chinese medicine.
Objective To improve the sensitivity and broaden the applicability of N-of-1 trials in traditional Chinese medicine (TCM), the clinical application and methodology of single-case experimental designs (N-of-1trials, multiple-baseline designs; MBDs) were expounded, compared, and discussed. Methods This paper introduced the current utility of N-of-1 trials in TCM research, introduced MBDs, and compared the methodologies of N-of-1 trials, MBDs and crossover design. Finally, two design schemes to improve the sensitivity and applicability of N-of-1 trials were illustrated. Results N-of-1 trials conformed to the TCM concept of treatment based on syndrome differentiation; however, due to the complex composition of TCM, the results were easily affected by carryover effect. In MBDs, the intervention was introduced in a staggered way, no washout period was needed, and the required sample size was small. MBDs were generally used to preliminarily indicate the effect of intervention; however, the statistical analysis was relatively complicated, and there were few MBDs used in clinical trials of TCM at present. Compared with crossover trials, single-case experimental designs had advantages and disadvantages. N-of-1 trials might best reflect the individualized treatment of TCM and a suitable statistical model (e.g., hierarchical Bayesian statistical method) was expected to improve the sensitivity and applicability of N-of-1 trials in TCM. Combining clinical trial designs (e.g., the combination of N-of-1 trials and MBDs) would complement the limitations of N-of-1 trials, and expand the scope of conditions applicable for study. Conclusion N-of-1 trials have both advantages and disadvantages in TCM research. Improved statistical models or combined study designs will improve the sensitivity and broaden the applicability of N-of-1 trials in TCM.
In recent years, clinical research in traditional Chinese medicine (TCM) has witnessed vigorous development, with increasingly close integration with clinical epidemiological methodologies. However, certain controversies persist, such as the difficulty in aligning epidemiology’s population-based perspective with TCM’s principle of syndrome differentiation and treatment and the characteristics of individualized diagnosis and treatment. This paper reviews the development and current status of TCM clinical research, integrating the practice rules of TCM, and analyzes the manifestations and applicability of the basic characteristics of epidemiology in TCM clinical research. The study shows that epidemiological concepts and characteristics are fully compatible with the practical features of TCM clinical practice and its research needs. Moreover, epidemiological techniques can effectively uncover and elucidate the scientific basis of TCM clinical practice. Building on these analyses, we propose future directions for TCM clinical research, aiming to promote the integration of epidemiology and TCM clinical research and advance TCM clinical research to a higher level.
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.
ObjectiveA series of single-case randomized controlled trials (N-of-1 trials), with placebo Chinese herbs used as a control, were conducted to observe the efficacy of the syndrome differentiation treatment formula in the stable phase of bronchiectasis by using a modified mixed-effects model (MEM) to detect the "carryover effects" of Chinese herbs, and to explore the establishment of an N-of-1 trial method that reflects the characteristics of syndrome differentiation treatment in traditional Chinese medicine (TCM). MethodsA single-center clinical trial was conducted in which a single case was studied in a multiple crossover, randomized controlled, and blinded manner. There were three rounds of the trial, each with two observation periods (treatment period and control period) of 4 weeks each. In the treatment period, an individualized formula based on syndrome differentiation was given, and in the control period, a placebo formula was administered. The primary indicator was the patients’ self-rated 7-point symptom Likert scale score, and other indicators included chronic obstructive pulmonary disease assessment test (CAT) score, 24 h sputum volume, TCM syndrome score, and safety index. Paired t test was used to analyze single case data and MEM designed for "carryover effects" was used to analyze group data. ResultsA total of 21 subjects were formally enrolled, and 15 (75%) completed three rounds of N-of-1 trials. Three of the cases showed statistically significant differences in overall symptom Likert scale score. At the group level, the MEM designed for "carryover effects" found statistically significant residual effects on three indicators (overall symptom score, respiratory symptom score, and CAT score). After excluding the "carryover effects", the model analyzed the statistically significant differences between the intervention effects of the two formulas on the overall symptom score, respiratory symptom score, CAT score and TCM syndrome score. The sensitivity of the MEM was higher than that of the meta-analysis when residual effects existed in the N-of-1 trials. ConclusionThe N-of-1 trials of Chinese herbs designed in this study can well demonstrate the characteristics of TCM syndrome differentiation and treatment. The modified MEM can detect the residual effects of TCM and improve the sensitivity of data statistics. However, due to the inherent nature of N-of-1 trials, the sensitivity of this study method at the individual level is low and more cases and diseases need to be studied for further improvement.
In recent years, investment in new drug development in China has surged; however, challenges such as difficulties in efficacy validation, high failure rates, and lengthy, costly clinical trials have been faced. The traditional model is insufficient for addressing these issues, necessitating innovation. Adaptive design (AD), particularly sequential multiple assignment randomized trials (SMART), has emerged as a flexible and efficient new pathway for drug development. This study focused on the two-stage design of SMART, analyzed its principles, and contrasted it with randomized controlled trials, group sequential designs, and crossover designs. The advantages of SMART are highlighted in terms of its precision in evaluating treatment strategies, minimizing sample waste, and enhancing the exploration of complex treatment pathways. Through case analyses, we demonstrated that SMART significantly improved clinical trial efficiency and the quality of treatment decisions, representing an innovative solution to the challenges of new drug development. This study aims to provide strategic references for clinical researchers and promote the adoption of adaptive designs in China, facilitating the efficient advancement of new drug development.
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 sources 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 TCM 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 TCM formula granules.
ObjectiveTo introduce the theoretical foundation, application scenarios, and implementation in R language of covariate-adaptive randomization (CAR) and restricted randomization (RR). MethodsThis article initially expounds the significance of CAR and RR in clinical trials, particularly in balancing covariates between treatment groups on the basis of dynamic adjustment and pre-defined rules, in order to enhance the accuracy and reliability of trial outcomes. ResultsRR is applicable to large-scale trials, ensuring balance between groups but potentially inducing selection bias. CAR is suitable for small-sample and complex covariate trials, improving accuracy yet having complex implementation. In clinical trials of traditional Chinese medicine, CAR enables personalized group allocation, and RR ensures baseline balance. Dynamic randomization strategies enhance the flexibility of trials. ConclusionThrough code examples in R language, this study offers practical guidance for researchers to implement these randomization methods, ensuring the scientificity and rigor of data processing and analysis.
