hasse: 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$comb.random, "random", "fixed"), newpage = TRUE)

Arguments

An object of class netposet (mandatory).

pooled

A character string indicating whether Hasse diagram show be drawn for fixed effect ("fixed") 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 (R<U+00FC>cker & 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.

References

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

R<U+00FC>cker G, Schwarzer G (2017): Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments. Research Synthesis Methods, 8, 526--36

Examples

Run this code

# 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, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# (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, comb.fixed = FALSE,
                seq = trts, ref = "Placebo")

# Partial order of treatment rankings
#
po <- netposet(netrank(net1, small.values = "bad"),
               netrank(net2, small.values = "bad"),
               outcomes = outcomes)

# Hasse diagram
#
hasse(po)
# }
# NOT RUN {
# }

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