Statistical analysis
The outcomes are summarized using mean difference (MD) and risk ratio
(RR) with 95 % confidence interval (CI). While our significant findings
were derived from a frequentist network meta-analysis, a conventional
meta-analysis was performed in advance to compare HIF-PHIs overall with
ESAs briefly. The overall heterogeneity of effect size was tested. If
there was significant between-study heterogeneity (\(I^{2}>50\%\)) in
the primary outcome, mean change in hemoglobin level from baseline, a
random-effects model would be used, and a fixed-effects model would be
used otherwise. In addition,
Cochran’s Q-statistic was calculated under the assumption of
design-by-treatment interaction random-effects models to assess the
consistency of networks[19-21]. Funnel plots evaluated publication
bias. Rankings of treatments were generated by estimating their surface
under the cumulative ranking (SUCRA) scores, which is a metric to assess
which treatment is likely to be the most efficacious (0: treatment is
certain to be the worst; 1: treatment is certain to be the best) in the
context of network meta-analyses[22, 23]. The SUCRA score is
calculated in the function using the formula:
\begin{equation}
\text{SUCRA}_{i}=\frac{\sum_{j=1}^{n-1}\text{cum}_{\text{ij}}}{n-1}\nonumber \\
\end{equation}Where i =1, 2, …, n is the index of some treatment,n is the number of all competing treatments, j =1, 2, …,
n−1 is the rank of best treatments, and cum represents the
cumulative probability of treatment i being among the jbest treatments. The influence of mean age, sex ratio, and duration of
treatment was investigated through subgroup analysis using the Bayesian
model. Finally, the network meta-analysis is repeated using the Bayesian
model for sensitivity analysis[24, 25]. All analyses were done with
R 4.2.0 via the packages net-meta
version 2.1-0 and gemtc version 1.0-1.