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Left panel: A disconnected network of three two‐arm studies with six treatments. Right panel: The CNMA model adds new joins between the treatments having common components, thus reconnecting the network

Journal: Biometrical Journal. Biometrische Zeitschrift

Article Title: Network meta‐analysis of multicomponent interventions

doi: 10.1002/bimj.201800167

Figure Lengend Snippet: Left panel: A disconnected network of three two‐arm studies with six treatments. Right panel: The CNMA model adds new joins between the treatments having common components, thus reconnecting the network

Article Snippet: A random effects CNMA model assuming a common between‐study variance τ 2 can be implemented similar to Rücker and Schwarzer ( ) by using a multivariate methods of moments estimate of τ 2 (Jackson, White, & Riley, ): τ ^ 2 = max Q a − df a t r ( I − H a ) U W , 0 with Q a , d f a , H a , W defined as above.

Techniques:

Left panel: A disconnected network of three two‐arm studies with six treatments. Right panel: The CNMA model adds new joins between the treatments having common components, thus reconnecting the network

Journal: Biometrical Journal. Biometrische Zeitschrift

Article Title: Network meta‐analysis of multicomponent interventions

doi: 10.1002/bimj.201800167

Figure Lengend Snippet: Left panel: A disconnected network of three two‐arm studies with six treatments. Right panel: The CNMA model adds new joins between the treatments having common components, thus reconnecting the network

Article Snippet: For sake of clarity, we here ignore that there may be multiarm studies, however, adjustment for multiarm studies in the CNMA model works as described in Rücker and Schwarzer ( ) for the standard NMA model. We now define three matrices.

Techniques: