TY - JOUR
T1 - Graphical tools for visualizing the results of network meta-analysis of multicomponent interventions
AU - Seitidis, Georgios
AU - Tsokani, Sofia
AU - Christogiannis, Christos
AU - Kontouli, Katerina Maria
AU - Fyraridis, Alexandros
AU - Nikolakopoulos, Stavros
AU - Veroniki, Areti Angeliki
AU - Mavridis, Dimitris
N1 - Publisher Copyright:
© 2022 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
PY - 2023/5
Y1 - 2023/5
N2 - Network meta-analysis (NMA) is an established method for assessing the comparative efficacy and safety of competing interventions. It is often the case that we deal with interventions that consist of multiple, possibly interacting, components. Examples of interventions' components include characteristics of the intervention, mode (face-to-face, remotely etc.), location (hospital, home etc.), provider (physician, nurse etc.), time of communication (synchronous, asynchronous etc.) and other context related components. Networks of multicomponent interventions are typically sparse and classical NMA inference is not straightforward and prone to confounding. Ideally, we would like to disentangle the effect of each component to find out what works (or does not work). To this aim, we propose novel ways of visualizing the NMA results, describe their use, and illustrate their application in real-life examples. We developed an R package viscomp to produce all the suggested figures.
AB - Network meta-analysis (NMA) is an established method for assessing the comparative efficacy and safety of competing interventions. It is often the case that we deal with interventions that consist of multiple, possibly interacting, components. Examples of interventions' components include characteristics of the intervention, mode (face-to-face, remotely etc.), location (hospital, home etc.), provider (physician, nurse etc.), time of communication (synchronous, asynchronous etc.) and other context related components. Networks of multicomponent interventions are typically sparse and classical NMA inference is not straightforward and prone to confounding. Ideally, we would like to disentangle the effect of each component to find out what works (or does not work). To this aim, we propose novel ways of visualizing the NMA results, describe their use, and illustrate their application in real-life examples. We developed an R package viscomp to produce all the suggested figures.
KW - multicomponent
KW - network meta-analysis
KW - sparse network
KW - transitivity
KW - visualization
UR - http://www.scopus.com/inward/record.url?scp=85145681104&partnerID=8YFLogxK
U2 - 10.1002/jrsm.1617
DO - 10.1002/jrsm.1617
M3 - Article
C2 - 36541111
AN - SCOPUS:85145681104
SN - 1759-2879
VL - 14
SP - 382
EP - 395
JO - Research Synthesis Methods
JF - Research Synthesis Methods
IS - 3
M1 - doi.org/10.1002/jrsm.1617
ER -