Abstract
Background. Our previous studies showed that N-of-1 trials could reflect the individualized characteristics of traditional Chinese medicine (TCM) syndrome differentiation with good feasibility, but the sensitivity was low. Therefore, this study will use hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM. Methods/Design. This is a randomized, double-blind, placebo-controlled, three-pair crossover trial for a single subject, including 4–8 weeks of run-in period and 24 weeks of formal trial. In this study, we will recruit a total of 30 participants who are in the stable stage of bronchiectasis. The trial will be divided into three pairs (cycles), and one cycle contains two observation periods. The medications will be taken for three weeks and stopped for one week in the last week of each observation period. The order of syndrome differentiation decoction and placebo will be randomly determined. Patient self-reported symptom score (on a 7-point Likert scale) is the primary outcome. Discussion. Some confounding variables (such as TCM syndrome type and potential carryover effect of TCM) will be introduced into hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM, and the use of prior available information (e.g., “borrowing from strength” of previous trial results) within the analysis may improve the sensitivity of the results of a series of N-of-1 trials, from both the individual and population level to study the efficacy of TCM syndrome differentiation. It is the exploration of improving the objective evaluation method of the clinical efficacy of TCM and may provide reference value for clinical trials of TCM in other chronic diseases. This trial is registered with ClinicalTrials.gov (ID: NCT04601792).
Highlights
Our previous studies showed that N-of-1 trials could reflect the individualized characteristics of traditional Chinese medicine (TCM) syndrome differentiation with good feasibility, but the sensitivity was low. erefore, this study will use hierarchical Bayesian statistical method to improve the sensitivity and applicability of N-of-1 trials of TCM
It is difficult to carry out a standard form of population-based RCTs due to the individualized TCM intervention. e shortage of a reliable and evidencebased clinical efficacy evaluation method of TCM has impeded its globalization and further development [1]. erefore, it is of great significance to explore and establish a clinical trial method that can fully reflect the individualized characteristics of TCM [2]
We mainly faced the following two problems: (1) because of the long period of N-of-1 trials of TCM, and generally only three cycles were conducted, in terms of individual statistics, its power of statistical analysis was insufficient, which made it difficult for some patients to draw a clear conclusion [10]; (2) through a series of N-of-1 trials, we have proved that the syndrome differentiation decoction was statistically better than the fixed decoction on symptom scores (P < 0.05), but the result was not clinically significant
Summary
If the factor of carryover effect is introduced into statistical models, it may improve the efficiency of data analysis of N-of-1 clinical trials for TCM. Hierarchical Bayesian statistical method has been one of the main statistical methods for N-of-1 trials due to its significant advantages [14, 15]: (1) both individual and aggregate analyses to be simultaneously and coherently undertaken, even when the number of completed cycles between patients is variable; (2) introducing confounding variables, such as different patient’s constitution, different TCM syndrome type, and potential carryover effect; and (3) in addition, Bayesian methods enable the use of prior available information (e.g., “borrowing from strength” of previous trial results) within the analysis, which may improve the sensitivity of N-of-1 trials [16, 17]. Introducing carryover effect of TCM as confounding variables into hierarchical Bayesian statistics, and using the “borrowing from strength” [16, 17] function of prior information, can improve the reliability and sensitivity of N-of-1 trials of TCM
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