Similarity
Included trials are similar clinically and methodologically
Network meta-analysis (NMA)
Comparing efficacy of dual therapies among patients with COPD
New evidence, using the established, robust statistical methodology of a Network Meta-Analysis (NMA), has extended the evidence base for use of fixed dose LAMA/LABA dual therapies in patients with COPD.1
Head-to-head randomised controlled trials comparing outcomes between LAMA/LABA dual therapies are limited, and recent evidence may help clarify relative benefits of different LAMA/LABA combinations.1 Previous meta-analyses of clinical trial data comparing dual and mono-bronchodilator therapies have shown a gradient of effectiveness between LAMA/LABA fixed-dose combinations in patients with COPD based on data available in 2015.2,3 This new NMA presents the relative benefits of different LAMA/LABA combinations.1
An NMA allows simultaneous comparison of multiple treatment options in the absence of head-to-head studies. This is made possible by establishing a linked network of evidence. During an NMA, a systematic literature review (SLR) is conducted, and a network of evidence is linked. This allows both direct and indirect comparisons (through direct RCT evidence and indirect evidence via a common comparator).4,5
The similarity assumption holds for this NMA
Heterogeneity was seen across multiple studies but was within acceptable limits to allow study comparisons
In the fixed effect approach, the assumption is that all studies assume one true effect size, and observed differences in estimates between studies are due to sampling error only
In the random effect approach, the assumption is that sampling error exists but with the expectation that true effect size varies both across studies and across comparisons (direct and indirect)
COPD, chronic obstructive pulmonary disease; LABA, long-acting β2-agonist; LAMA, long-acting muscarinic antagonist; NMA, network meta-analysis; RCT, randomised controlled trial; SLR, systematic literature review.
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NX-GBL-UCV-WCNT-220015 | Date of preparation: February 2023