relative.effects: Find relative effects for base treatment and comparison...

In MikeJSeo/network-meta: Bayesian Network Meta-Analysis

Description

This function calculates relative effects for base treatment and comparison treatments.

Usage

relative.effects(result, base.treatment = NULL,
  comparison.treatments = NULL, base.category = NULL,
  comparison.categories = NULL, covariate = NULL)

Arguments

result	Object created by network.run function
base.treatment	Base treatment user wants for the relative effects. Base treatment is initially set by Treat.order parameter in network.data (first one in the list). If set to null, default is to use base treatment.
comparison.treatments	Treatments that user wants to compare against base treatment. If set to null, all the treatments besides base treatment is considered as comparison treatments.
base.category	Base category user wants for the relative effects. Only used for multinomial data.
comparison.categories	Category that user wants to compare against base.category. Only used for multinomial data.
covariate	Covariate value at which to compute relative effects. Only used if covariate value is specified in the model.

Value

This returns a mcmc.list sample of relative effects for the base treatment specified. This allows user to obtain relative effects of different base.treatment after the sampling has been done. For a simple summary, use relative.effects.table.

See Also

relative.effects.table

Examples

#We can fit two different models with different base treatment and we can
#obtain same relative effects estimate using this function
#parkinsons
network <- with(parkinsons, {
 network.data(Outcomes, Study, Treat, SE = SE, response = "normal")
})
result <- network.run(network) 
summary(result)

network2 <- with(parkinsons, {
 network.data(Outcomes, Study, Treat, SE = SE, response = "normal",
 Treat.order = c(2,1,3,4,5))
})
result2 <- network.run(network2)

summary(result)
summary(relative.effects(result2, base.treatment = 1))

#This also works for comparing different base.category for multinomial.
#We fit two different models and compare the estimates again.
#cardiovascular

network3 <- with(cardiovascular, {
 network.data(Outcomes, Study, Treat, N, response = "multinomial")
})
result3 <- network.run(network3)

network4 <- with(cardiovascular, {
 network.data(Outcomes[,c(2,1,3)], Study, Treat, N, response = "multinomial")
})
result4 <- network.run(network4)

summary(result3)
summary(relative.effects(result4, base.category = 2))

MikeJSeo/network-meta documentation built on May 3, 2019, 4:31 p.m.