Fits the additive component network meta-analysis (cNMA) model of Rücker
et al. (2020) to the aggregate contrast data, using
netmeta::discomb(). When the network is disconnected but its
sub-networks share components, the additive model estimates component
effects and so reconstructs relative effects across sub-networks. This
is the "connect first" step; population adjustment is layered on by
cmaic() / cstc(), which replace unadjusted contrasts with adjusted
ones before calling this function.
Arguments
- network
A
cpaic_network()object.- common, random
Fit common- and/or random-effects models.
- ...
Additional arguments passed to
netmeta::discomb()(e.g.tau.preset).
Value
An object of class cpaic_bridge wrapping the
netmeta::discomb() fit, with tidied component and treatment effects.
Details
Estimability is checked per contrast, not by a single global rank test: a
relative effect is uniquely estimable if and only if its contrast vector
lies in the row space of the component design matrix X = B C (Wigle et
al. 2026). A rank-deficient network is therefore not rejected outright;
the contrasts that remain estimable are still reported, and those that are
not are returned as NA rather than as pseudoinverse artefacts. See
estimable_effects().
References
Rücker G, Petropoulou M, Schwarzer G (2020). Network meta-analysis of multicomponent interventions. Biometrical Journal, 62(3), 808–821.
Wigle A, Beliveau A, Nikolakopoulou A, Lin L (2026). Creating Treatment and Component Hierarchies in Component Network Meta-Analysis.
Examples
net <- cpaic_network(cpaic_bin_agd, sm = "OR", inactive = "Placebo")
br <- cnma_bridge(net)
component_effects(br)
#> component estimate se lower upper statistic pval
#> 1 A 0.5000000 1.1922140 -1.836697 2.836697 0.4193878 0.6749328
#> 2 B 0.4000000 1.1922140 -1.936697 2.736697 0.3355102 0.7372402
#> 3 C 0.7170248 0.9734562 -1.190914 2.624964 0.7365763 0.4613800
#> 4 D 0.3250136 0.9728622 -1.581761 2.231788 0.3340798 0.7383193
relative_effects(br)
#> Relative effects (OR, back-transformed)
#> treatment comparator estimate se lower upper z p
#> A Placebo 1.649 1.192 0.159 17.059 0.419 0.675
#> A+B Placebo 2.460 1.686 0.090 66.993 0.534 0.593
#> A+B+C Placebo 5.038 1.947 0.111 228.801 0.831 0.406
#> A+B+D Placebo 3.404 1.947 0.075 154.510 0.629 0.529
#> B Placebo 1.492 1.192 0.144 15.436 0.336 0.737