hasse {netmeta}

Hasse diagram

Description

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

Usage

hasse(x, pooled = ifelse(x$random, "random", "common"), newpage = TRUE)

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.

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 R function hasse in R package hasseDiagram (Krzysztof Ciomek, https://github.com/kciomek/hasseDiagram), i.e., function hasse can only be used if R package hasseDiagram is installed.

Author(s)

Gerta Rücker [e-mail], Guido Schwarzer [e-mail]

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

Examples

## Not run: 
# Use depression dataset
#
data(Linde2015)

# Define order of treatments
#
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
#
p1 <- pairwise(treat = list(treatment1, treatment2, treatment3),
  event = list(resp1, resp2, resp3),
  n = list(n1, n2, n3),
  studlab = id, data = Linde2015, sm = "OR")
#
net1 <- netmeta(p1, common = FALSE,
  seq = trts, ref = "Placebo", small.values = "undesirable")

# (2) Early remission
#
p2 <- pairwise(treat = list(treatment1, treatment2, treatment3),
  event = list(remi1, remi2, remi3),
  n = list(n1, n2, n3),
  studlab = id, data = Linde2015, sm = "OR")
#
net2 <- netmeta(p2, 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)

## End(Not run)