Analysis using mbnma.run() | Dose-response functions | Dose-response parameters | Output | Examples | Parameter interpretation | Additional arguments for mbnma.run() | Link functions for likelihood=="normal" | Class effects | User-defined dose-response function | Agent-specific dose-response functions | Incorporating agents/interventions without a dose-response | Splines and knots | Correlation between dose-response parameters | Priors | pD (effective number of parameters) | Arguments to be sent to JAGS | Connecting networks via the dose-response relationship | Non-parametric dose-response functions | Assessment of model fit | Deviance plots | Fitted values | Model Selection | MCMC Convergence | References

Performing standard NMA using mbnma.run() | Benefits of using mbnma.run() for standard NMA models

Estimating relative effects | Forest plots | Ranking | References

Prediction | Plotting predictions | Ranking predicted responses | References

Consistency Testing | Unrelated Mean Effects (UME) model at the treatment level | Node-splitting at the treatment level | References

Exploring the data | Examining network connectivity | Examining the dose-response relationship | References

Network meta-regression | Data preparation | Modelling | Independent, agent-specific interactions | Random effect interaction | Common effect interaction | Alternative assumptions | Aggregation bias | Prediction using effect modifying covariates | References

Consistency Testing | Unrelated Mean Effects (UME) models | Node-splitting | References

Inspecting the data | Network connectivity | Examining the time-course relationship | References

Estimating relatived effects between treatments at a specified time-point | Fitted values | Forest plots | rank(): Ranking | References

Prediction | E0 (time=0) | Baseline (reference treatment) time-course | Exploring predictions | Plotting "lumped" NMA results | Ranking | References

Analysis using mb.run() | Time-course functions | Splines and knots | Choosing a time-course function | Output | Parameters modelled using relative effects | Parameters modelled using absolute effects | Other model parameters | Examples | Additional model specification with mb.run() | Correlation between time points | Link function | Class effects | Additional arguments | Priors | pD (effective number of parameters) | Correlation between time-course parameters | Arguments to be sent to JAGS | Model Selection | MCMC Convergence | References

Introduction | Workflow within the package | Datasets Included in the Package | Pain relief in osteoarthritis | Alogliptin for lowering blood glucose concentration in type II diabetes | Tiotropium, Aclidinium and Placebo for maintenance treatment of moderate to severe chronic obstructive pulmonary disease | Body weight reduction in obesity patients | Serum uric acid concentration in gout | References

Introduction | Workflow within the package | Datasets Included in the Package | Triptans for migraine pain relief | Biologics for treatment of moderate-to-severe psoriasis | Selective Serotonin Reuptake Inhibitors (SSRIs) for major depression | Interventions for Serum Uric Acid (SUA) reduction in gout | Interventions for pain relief in osteoarthritis | Alogliptin for lowering blood glucose concentration in type II diabetes | Wound closure methods for reducing Surgical Site Infection (SSI) | References