STATA 13.1 software (Stata Corp, College Station, TX, United States), WinBUGS 1.4.3 software (Medical Research Council Biostatistics Unit, Cambridge, United Kingdom), and R 4.0.3 software (Mathsoft, Cambridge, United States) were used for statistical analysis. In this research, the outcomes were all dichotomous variables, and the odds ratio (OR) and its 95% confidence intervals (95% CI) were used to describe the effect. If the 95% CI did not contain one, the differences between the compared groups were statistically significant. The quality of the included RCTs was evaluated by Review Manager 5.3, and the NMA was carried out by WinBUGS software, while the Markov chain Monte Carlo method with a random-effects model was performed for Bayesian inference. The random-effects model for outcomes was chosen in the NMA. In WinBUGS software, the number of simulation iterations was 200,000, and the first 10,000 iterations were used for burn-in to eliminate the impact of the initial value (Crainiceanu and Goldsmith, 2010). Additionally, Stata version 13.1 software was adopted to analyze the results and draw the graphs of the NMA (Chaimani et al., 2013). The lines thickness corresponded to the number of trials used for the comparisons and the node sizes were weighted according to the total sample sizes of each treatment in the network graph. The results of WinBUGS software calculations were employed by Stata software to calculate the surface under the cumulative ranking curve (SUCRA). The “gemtc” package in R 4.0.3 software was used to analyze and visualize the NMA results of the clinical effective rate because the WinBUGS code could not analyze the rate when it was 100%. An intervention with a larger SUCRA value was considered to be the more effective treatment (Trinquart et al., 2016). Therefore, SUCRA was used to evaluate the ranking probabilities for each treatment. Publication bias was described via a comparison-adjusted funnel plot by Stata software (Trinquart et al., 2012). Symmetric points in the graph indicate that there is no obvious publication bias. Cluster analysis was also performed to comprehensively compare the effect of CHIs on two different outcomes, and the interventions located in the upper-right corner were superior to others (Veroniki et al., 2015).

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