Data analysis
Data analysis was performed using a random-effects model within a
frequentist method in Stata version 14.0 using the network
meta-analysis.12Multiarmtrials were divied into two
groups for comparisons and network meta-analyses to combine direct and
indirect evidence.13 Because our outcomes are
categorical data,so we estimated the pooled
odds ratios (ORs) and 95%
confidence interval(CI) of clinical efficacy and safety parameters,if
the CI for the ORs did not involve one,the comparisions were thought to
be with statistical sighificance.We evaluated the heterogeneity of all
control groups was assumed to be equal and the correlation of multi-arm
studies was considered and use node-splitting method using visual
inspection of the forest plots assess local inconsistency
.14If the treatments form a closed circle,loop
inconsistency should also be tested by using χ2-test
and inconsistency factors (IFs).14,15 We calculated
the probabilities of ranks and surface under the cumulative ranking
(SUCRA) of the treatments. The higher the treatment’s SUCRA value,value
ranges from 0 to 1, the higher the likelihood that the treatment was
better the curative effect. 16Under the
circumstances,hierarchical cluster analyses is the better choice to
achieve compare short-term topical teatment efficacy,safety and
tolerability of these topical treatments.17Besides,we
assessed publication bias using comparsion-adjusted funnel plot.