netgraph.crossnma function

Produce a network plot

Create a network plot of the cross network meta-analysis or meta-regression

## S3 method for class 'crossnma'
netgraph(
  x,
  labels,
  adj = NULL,
  offset = if (!is.null(adj) && all(unique(adj) == 0.5)) 0 else 0.0175,
  points = !missing(cex.points),
  cex.points = 1,
  ...
)

Arguments

• x: An object produced by crossnma.
• labels: An optional vector with treatment labels.
• adj: One, two, or three values in [0, 1] (or a vector / matrix with length / number of rows equal to the number of treatments) specifying the x (and optionally y and z) adjustment for treatment labels.
• offset: Distance between edges (i.e. treatments) in graph and treatment labels for 2-D plots (value of 0.0175 corresponds to a difference of 1.75% of the range on x- and y-axis).
• points: A logical indicating whether points should be printed at nodes (i.e. treatments) of the network graph.
• cex.points: Corresponding size for points. Can be a vector with length equal to the number of treatments.
• ...: ... Additional arguments (passed on to netgraph.netmeta).

Returns

A data frame containing the following columns: - labels: Treatment labels.

• seq: Sequence of treatment labels.

• xpos: Position of treatment / edge on x-axis.

• ypos: Position of treatment / edge on y-axis.

• zpos: Position of treatment / edge on z-axis (for 3-D plots).

• xpos.labels: Position of treatment labels on x-axis (for 2-D plots).

• ypos.labels: Position of treatment labels on y-axis (for 2-D plots).

• adj.x: Adjustment for treatment label on x-axis.

• adj.y: Adjustment for treatment label on y-axis.

• adj.z: Adjustment for treatment label on z-axis (for 3-D plots).

Examples

## Not run:

# 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
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
set.seed(1909)
fit <- crossnma(mod)

# Create network plot
netgraph(fit, plastic = FALSE, cex.points = 7, adj = 0.5)
## End(Not run)

See Also

netgraph.netmeta

Author(s)

Tasnim Hamza hamza.a.tasnim@gmail.com