mtrank (version 0.1-1)

forest.mtrank: Forest plot of ability estimates produced with mtrank

Description

This function produces a forest plot that visualizes the ability estimates calculated with mtrank.

Usage

# S3 method for mtrank
forest(
  x,
  sorting = "ability",
  backtransf = FALSE,
  xlab = "",
  leftcols = "studlab",
  leftlabs = "Treatment",
  rightcols = c("effect", "ci"),
  rightlabs = c(paste0(if (!backtransf) "log-", "Abilities"), NA),
  label.left = "Favors average treatment",
  label.right = "Favors treatment",
  header.line = TRUE,
  ...
)

Value

A forest plot is plotted in the active graphics device.

Arguments

x

An object of class mtrank.

sorting

An argument specifying the criterion to sort the ability estimates in the forest plot (see Details).

backtransf

A logical argument specifying whether to show log-ability estimates (FALSE, default) or ability estimates on the natural scale (TRUE).

xlab

A label for the x-axis.

leftcols

A character vector specifying columns to be printed on the left side of the forest plot (see forest.meta).

leftlabs

A character vector specifying labels for columns on left side of the forest plot.

rightcols

A character vector specifying columns to be printed on the right side of the forest plot (see forest.meta).

rightlabs

A character vector specifying labels for columns on right side of the forest plot.

label.left

Graph label on left side of null effect.

label.right

Graph label on right side of null effect.

header.line

A logical value indicating whether to print a header line or a character string ("both", "below", "").

...

Additional arguments (passed on to forest.meta).

Details

The function produces a forest plot and visualizes the ability estimates obtained from mtrank. The order of the estimates in the forest plot (argument sorting) can be one of the following:

• "ability": sort by descending ability estimates (default),

• "se": sort by descending precision, i.e., increasing standard errors,

• "none": use order from data set.

References

Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612

Examples

data(antidepressants)
#
ranks <- tcc(treat = drug_name, studlab = studyid,
  event = responders, n = ntotal, data = antidepressants,
  mcid = 1.25, sm = "OR", small.values = "undesirable")
#
fit <- mtrank(ranks)

forest(fit, treat = "escitalopram")