The method of evaluating clinical efficacy of traditional Chinese medicine is one of the hotspots in the field of traditional Chinese medicine in recent years. How to dynamically evaluate individual efficacy is one of the key scientific problems to explain the clinical efficacy of traditional Chinese medicine. At present, there are no recognized methods of evaluating individual efficacy of traditional Chinese medicine. In this study, we provided a method of dynamically evaluating individual efficacy of traditional Chinese medicine based on Bayesian N-of-1 trials after analyzing the current status of researches on methods of evaluating individual efficacy of traditional Chinese medicine. This method has the advantages of both N-of-1 trials and Bayesian multilevel models. It is feasible to evaluate individual efficacy of traditional Chinese medicine from the perspective of the design and analysis method. This study can provide an important basis for enriching and improving the methodology of evaluating individual efficacy of traditional Chinese medicine.
The study appeared the comparison between CONSORT and CENT, and promoted the combination with GRADE and N-of-1 trial. Our objective is to further develop the method of N-of-1 trial and to widely use it in clinical researches of some diseases.
N-of-1 trials are prospective clinical randomized cross-over controlled trials with multiple rounds of trial phase alternation designed with regard to a single patient. N-of-1 trials can provide clinical decision-makers with high-level evidence of the comparison of effect of intervention measures. Recently, an international team composed of many scholars published a SPIRIT extension for N-of-1 trials list (SPENT 2019) on the BMJ, with the purposes of clarifying the content design and improving the integrity and transparency of N-of-1 trial protocols. This article showed a detailed interpretation of the 14 main extension sub-items of the SPENT 2019 list with specific cases, aiming to further standardize the publication of domestic N-of-1 trials.
Objective A series of N-of-1 trials were conducted to evaluate the effects of traditional Chinese medicine (TCM) individualized syndrome differentiation on stable bronchiectasis, and to explore a clinical trial method that is consistent with the characteristics of TCM. Methods The original plan consisted of 3 cycles, with each cycle consisting of two observation periods: experimental and control. Take the medication for 3 weeks each period and then stop for 1 week. Because the results were not as expected, another cycle of trials was added (a total of 4 cycles). The trial period was treated with individualized syndrome differentiation prescription and the control period was treated with placebo. The outcome measures were Likert scale score of general symptoms (primary outcome), Likert scale score of respiratory symptoms, CAT score, 24h sputum volume and TCM symptom score. Data analysis (including residual effects and stage effects analysis) used group-designed independent sample t tests, paired t tests or non-parametric tests, mixed effects models, and Bayesian analysis. Results A total of 31 participants were formally enrolled, with 24 completing all four cycles. Independent sample t-tests and mixed-effects models showed no significant period or carryover effects. Bayesian analysis showed that there were residual effects on some outcome measures of some individuals. Six participants showed statistically significant differences in overall symptom Likert scale scores (P<0.05). Bayesian analysis found that TCM was more effective than placebo in more individuals. No significant differences were found between individualized TCM and placebo at the group level for any of the outcome measures. Conclusion This study method highly simulates the clinical practice of TCM, with good operability and patient compliance, and has no obvious residual effect of TCM on the whole, which can provide the best individualized evidence-based medicine evidence of short-term efficacy of TCM. Bayesian analysis can improve the sensitivity of individual statistics.
ObjectiveA simulation study was used to generate the multivariate normal distribution data with a residual effect based on series of N-of-1 trials. The statistical performance of paired t-test, mixed effect model and Bayesian mixed effect model were compared.MethodsThree-cycles N-of-1 trials were set, and the participants were randomly assigned to 2 different treatments in each cycle. The simulation study included the following procedures: producing six-dimensional normal distribution data, randomly allocating intervention methods and patients, adding residual effects, constructing and evaluating 3 models, and setting the parameters. The sample sizes were set as 3, 5, 8 and 10, and the correlation coefficients among different times were set as 0.0, 0.5 and 0.8. Different proportions of residual effects for the 2 groups were set. Type I error, power, mean error (ME), and mean square error (MSE) were used to compare the 3 models.ResultsWhen there was no residual effect in the 2 groups, type I errors of 3 models were approximately 0.05, and their MEs were approximately 0. Paired t-test had the highest power and the lowest MSE. When the residual effect existed in the 2 groups, the type I error of paired t-test increased, and its estimated value deviated from the true value (ME≠0). Type I errors of the mixed effect model and Bayesian mixed-effect model were approximately 0.05, and they had the same power. The estimated values of the two models were close to the true value (ME was approximately 0).ConclusionsWhen there is no residual effect (0% vs. 0%), paired t-test is suitable for data analysis of N-of-1 trials. When there is a residual effect, the mixed effect model and Bayesian mixed-effect model are suitable for data analysis of N-of-1 trials.
This paper mainly introduces the design, advantages, disadvantages and its application of single case of randomized controlled trials (N-of-1 trials), and also introduces the commonly used data analysis methods of N-of-1 trials including nonparametric test and parameter test methods (t-test, paired t-test, analysis of variance), mixed-effects model, and meta-analysis. N-of-1 trials are suitable for individualized treatment, and could be expected to be widely used in the research of modern medicine and traditional Chinese medicine.
N-of-1 trial is globally making rapid development as a patient-oriented individualized method with the development and improvement of methodology in clinical trial. The paper described the origin and development of N-of-1trial, so as to help improve awareness among medical researchers and clinicians, and improve the clinical medical quality and level of clinical diagnosis and treatment.
The CONSORT extension for reporting N-of-1 trials (CENT 2015) is designed to guide N-of-1 and series N-of-1 reporting. This study introduced the terminology (period, block or pair, sequence, washout period, and run-in period), the scope, the checklist and the diagram of CENT 2015 and demonstrated the complete guide frame for N-of-1, and thus to provide reference for relevant studies and improve the reporting quality of N-of-1 in China.
Bayesian N-of-1 trials is increasingly popular in recent years. This study introduced the principle, statistical requirements, application status, advantages and disadvantages of Bayesian N-of-1 trials. Although the application of Bayesian N-of-1 trials is still limited in small scale and some problems remain to be solved, but it can provide more posterior information, and it can be the most important type of N-of 1 trial in future.
An N-of-1 trial was conducted in a single patient. Statistical analysis is one of the most important parts of N-of-1 trials. The methods of statistical analysis for N-of-1 trials were reported in some reviews. However, there was still a lack of comparative analysis of these methods. In this study, we introduced the characteristics of statistical methods commonly used as well as some statistical problems which should be paid attention in N-of-1 trials. It is useful to provide some reference for statistical methods in order to high quality N-of-1 trials.