league.crossnma {crossnma}R Documentation

League Table

Description

Produces a league table that contains point estimates of relative effects for all possible pairs of treatments along with 95% credible intervals obtained with the quantile method.

Usage

## S3 method for class 'crossnma'
league(
  x,
  median = TRUE,
  exp = FALSE,
  order = NULL,
  cov1.value = NULL,
  cov2.value = NULL,
  cov3.value = NULL,
  digits = 2,
  direction = "wide",
  ...
)

league(x, ...)

## S3 method for class 'league.crossnma'
print(x, ...)

Arguments

x

An object created with crossnma.

median

A logical indicating whether to use the median (default) or mean to measure relative treatment effects.

exp

If TRUE (default), odds ratios are displayed. If FALSE, log odds ratios will be presented.

order

A vector of treatment names (character) representing the order in which to display these treatments.

cov1.value

The participant covariate value of cov1 for which to report the results. Must be specified for network meta-regression and when individual participant dataset is used in the analysis. For dichotomous covariates, a character of the level (used in the data) should be indicated.

cov2.value

The participant covariate value of cov2 for which to report the results. Must be specified for network meta-regression and when individual participant dataset is used in the analysis. For dichotomous covariates, a character of the level (used in the data) should be indicated.

cov3.value

The participant covariate value of cov3 for which to report the results. Must be specified for network meta-regression and when individual participant dataset is used in the analysis. For dichotomous covariates, a character of the level (used in the data) should be indicated.

digits

The number of digits to be used when displaying the results.

direction

The format to display the league table. Two options "wide" (default) and "long".

...

Additional arguments (ignored at the moment).

Value

A league table. Row names indicate comparator treatments. The table will be displayed in a long or wide formatting.

Author(s)

Tasnim Hamza tasnim.hamza@ispm.unibe.ch

See Also

crossnma

Examples

# We conduct a network meta-analysis assuming a random-effects
# model.
# The data comes from randomized-controlled trials and
# non-randomized studies (combined naively)
head(ipddata) # participant-level data
head(stddata) # study-level data

# Create a JAGS model
mod <- crossnma.model(treat, id, relapse, n, design,
  prt.data = ipddata, std.data = stddata,
  reference = "A", trt.effect = "random", method.bias = "naive")

# Fit JAGS model
# (suppress warning 'Adaptation incomplete' due to n.adapt = 20)
fit <-
  suppressWarnings(crossnma(mod, n.adapt = 20,
    n.iter = 50, thin = 1, n.chains = 3))

# Create league tables
league(fit, exp = TRUE)                     #  wide format
league(fit, exp = TRUE, direction = "long") #  long format


[Package crossnma version 1.0.1 Index]