netmeta (version 3.2-0)

hasse.netposet: Hasse diagram

Description

This function generates a Hasse diagram for a partial order of treatment ranks in a network meta-analysis.

Usage

# S3 method for netposet
hasse(
  x,
  pooled = ifelse(x$random, "random", "common"),
  newpage = TRUE,
  shape = "roundrect",
  col.lines = "black",
  col.nodes = "black",
  lwd = 1,
  ...
)
hasse(x, ...)

Arguments

x

An object of class netposet (mandatory).

pooled

A character string indicating whether Hasse diagram show be drawn for common ("common") or random effects model ("random"). Can be abbreviated.

newpage

A logical value indicating whether a new figure should be printed in an existing graphics window. Otherwise, the Hasse diagram is added to the existing figure.

shape

Shape of node borders, either "roundrect", "rect", or "none", can be abbreviated.

col.lines

Line colour.

col.nodes

Colour for treatment node borders.

lwd

Width of lines and node borders.

...

Additional arguments (ignored).

Author

Gerta Rücker , Guido Schwarzer

Details

Generate a Hasse diagram (Carlsen & Bruggemann, 2014) for a partial order of treatment ranks in a network meta-analysis (Rücker & Schwarzer, 2017).

This R function is a wrapper function for a modified version of R function hasse in R package hasseDiagram (Krzysztof Ciomek, https://github.com/kciomek/hasseDiagram) which is available under the MIT license.

References

Carlsen L, Bruggemann R (2014): Partial order methodology: a valuable tool in chemometrics. Journal of Chemometrics, 28, 226--34

Rücker G, Schwarzer G (2017): Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments. Research Synthesis Methods, 8, 526--36

See Also

netmeta, netposet, netrank, plot.netrank, dat.linde2015

Examples

if (FALSE) {
# Only run example if R package 'Rgraphviz' from Bioconductor is available
#
if (requireNamespace("Rgraphviz", quietly = TRUE)) {
# Define order of treatments in depression dataset dat.linde2015
#
trts <- c("TCA", "SSRI", "SNRI", "NRI",
  "Low-dose SARI", "NaSSa", "rMAO-A", "Hypericum", "Placebo")

# Outcome labels
#
outcomes <- c("Early response", "Early remission")

# (1) Early response
#
pw1 <- pairwise(treat = list(treatment1, treatment2, treatment3),
  event = list(resp1, resp2, resp3),
  n = list(n1, n2, n3),
  studlab = id, data = dat.linde2015, sm = "OR")
#
net1 <- netmeta(pw1, common = FALSE,
  seq = trts, ref = "Placebo", small.values = "undesirable")

# (2) Early remission
#
pw2 <- pairwise(treat = list(treatment1, treatment2, treatment3),
  event = list(remi1, remi2, remi3),
  n = list(n1, n2, n3),
  studlab = id, data = dat.linde2015, sm = "OR")
#
net2 <- netmeta(pw2, common = FALSE,
  seq = trts, ref = "Placebo", small.values = "undesirable")

# Partial order of treatment rankings
#
po <- netposet(netrank(net1), netrank(net2), outcomes = outcomes)

# Hasse diagram
#
hasse(po)
}
}