Data synthesis
The characteristics of the included studies were tabulated and reviewed
to exclude those studies that might result in intransitivity. Network
meta-analysis was done by Bayesian approach using a random effects model
with Markov chain Monte Carlo simulation with vague priors (GEMTC,
BUGSnet) using the R-software (Version-R 3.6.2)17,18.
Generalised linear models with 4 chains, burn-in of 50,000 iterations
followed by 100,000 iterations with 10,000 adaptations was
used18.The geometry of the networks were assessed
using network plots with the size of the nodes being proportional to the
number of subjects included in the intervention and the thickness of the
arms connecting the different intervention nodes corresponding to the
number of studies included in the comparison. Model convergence was
assessed using Gelman-Rubin plots as well as by analysing the trace and
density plots19. Inconsistency was assessed by
node-splitting20. Pair-wise meta-analysis evaluating
the direct evidence for the different NIV modalities was also done and
heterogeneity was assessed using I2 statistic and
Cochran Q test. The results of the network meta-analysis were expressed
as risk ratios (RR) with 95% credible intervals in league matrix tables
and forest plots. The league matrix tables display the RR of the outcome
parameter for the intervention in the row versus that in the column in
the lower triangle and vice versa in the upper triangle. The comparison
of direct and indirect evidence using node-splitting are expressed as
odds ratios (ORs) with 95% credible intervals. Surface under the
cumulative ranking curve (SUCRA) was used to rank interventions for all
the outcomes. SUCRA is an index with values from 0 (least effective
intervention) to 1 (best intervention)21. SUCRA should
always be interpreted with 95% credible intervals as well as the
quality of the evidence. The confidence in the final estimates for all
the outcomes were assessed using GRADE approach as recommended by the
GRADE working group22.