| nma.compare | R Documentation | 
Plots the posterior mean deviance of a consistency model vs an inconsistency model. This plot can help identify loops where inconsistency is present. Ideally, both models will contribute approximately 1 to the posterior mean deviance.
nma.compare(consistency.model.fit, inconsistency.model.fit, ...)
consistency.model.fit | 
 Results of   | 
inconsistency.model.fit | 
 Results of   | 
... | 
 Graphical arguments such as main=, ylab=, and xlab= may be passed in   | 
nma.run
data(diabetes.sim)
diabetes.slr <- data.prep(
  arm.data = diabetes.sim, 
  varname.t = "Treatment", 
  varname.s = "Study"
)
#Random effects, consistency model.
#Binomial family, cloglog link. This implies that the scale will be the Hazard Ratio.
diabetes.re.c <- nma.model(
  data = diabetes.slr,
  outcome = "diabetes",
  N = "n",
  reference = "Placebo",
  family = "binomial",
  link = "cloglog",
  effects = "random",
  type = "consistency",
  time = "followup"
)
 
diabetes.re.c.res <- nma.run(
  model = diabetes.re.c,
  n.adapt = 100,
  n.burnin = 0,
  n.iter = 100
)
#Random effects, inconsistency model.
#Binomial family, cloglog link. This implies that the scale will be the Hazard Ratio.
diabetes.re.i <- nma.model(
  data = diabetes.slr,
  outcome = "diabetes", 
  N = "n",
  reference = "Placebo",
  family = "binomial",
  link = "cloglog",
  effects = "random",
  type = "inconsistency",
  time = "followup"
)
 
diabetes.re.i.res <- nma.run(
  model = diabetes.re.i,
  n.adapt = 100,
  n.burnin = 0,
  n.iter = 100
)  
# Assess model fit for a both an inconsistency model and consistency model using nma.fit()
assess.consistency <- nma.fit(diabetes.re.c.res)
assess.inconsistency <- nma.fit(diabetes.re.i.res)
#Plot the results against each other to assess inconsistency
nma.compare(assess.consistency, assess.inconsistency)
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