tcc: Apply a treatment-choice criterion (TCC) to get treatment...
In mtrank: Ranking using Probabilistic Models and Treatment Choice Criteria

View source: R/tcc.R

tcc	R Documentation

Apply a treatment-choice criterion (TCC) to get treatment preferences based on network meta-analysis estimates.

Description

This function uses a treatment choice criterion defined by the user and transforms the network meta-analysis estimates into a preference format that indicates either a treatment preference or a tie. In this setting, a treatment preference implies that the respective NMA estimate represents a clinically important result (i.e. that fulfills the TCC) while a tie indicates that the respective NMA estimate lacks enough evidence to represent a treatment preference. The resulting preference format is then used as input to mtrank.

Usage

tcc(
  x,
  pooled = if (x$random) "random" else "common",
  swd = NULL,
  swd.below.null = NULL,
  swd.above.null = NULL,
  small.values = x$small.values,
  relax = TRUE,
  level = x$level.ma
)

## S3 method for class 'tcc'
print(x, ...)

Arguments

`x`	A `netmeta` object.
`pooled`	A character string indicating whether results for the common (`"common"`) or random effects model (`"random"`) should be used. Can be abbreviated. If not specified the results from the random effects model will be used by default.
`swd`	A numeric value specifying the smallest worthwhile difference value (SWD); see Details.
`swd.below.null`	A numeric value specifying the SWD below the null effect (see Details).
`swd.above.null`	A numeric value specifying the SWD above the null effect (see Details).
`small.values`	A character string specifying whether small treatment effects indicate a beneficial (`"desirable"`) or harmful (`"undesirable"`) effect.
`relax`	A logical optional argument. If TRUE (default), the treatment choice criterion is based solely on the SWD bounds, emphasizing only the clinical importance of the results. If set to FALSE, the criterion incorporates both statistical significance and clinical importance. We recommend using the default setting (see Details).
`level`	The level used to calculate confidence intervals for log-abilities.
`...`	Additional arguments (ignored).

Details

R function mtrank expects data in a preference format, where a treatment preference or tie is indicated for each network meta-analysis (NMA) estimate. For example, for the comparison between treatments A and B the potential outcomes are:

A > B
A < B
A = B

The transformation takes place based on the NMA estimates and the treatment choice criterion which has the form of a decision rule.

This function implements treatment choice criteria based on the range of equivalence (ROE) which are specified by

argument swd. Then the limits of the ROE will be defined based on the values (i) swd, 1 / swd for ratio measures and (ii) swd and -swd for difference measures.
arguments swd.below.null and swd.above.null. These arguments allow the users to define their own limits of the ROE, given the restriction that the lower limit will always be smaller than the upper limit.

Note that when the argument swd is specified, the arguments swd.below.null and swd.above.null are ignored. Either only the swd or both of the swd.below.null and swd.above.null must be specified for the proper definition of the ROE.

After setting the ROE, each NMA treatment effect will be categorised as a treatment preference or a tie. The argument relax controls the amount of conservatism of the treatment choice criterion. If set to FALSE, a TCC will be built requiring both clinical importance as statistical significance of the results. If set to TRUE (default), the criterion uses only the ROE bounds and therefore the NMA treatment effects need to be only clinically important to indicate a treatment preference.

Value

NMA estimates in a preference format.

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")
#
pw1 <- pairwise(studlab = studyid, treat = drug_name,
  n = ntotal, event = responders,
  data = antidepressants, sm = "OR")
# Use subset to reduce runtime
pw0 <- subset(pw1, studyid < 60)
#
net0 <- netmeta(pw0, reference.group = "tra")

ranks0 <- tcc(net0, swd = 1.20, small.values = "undesirable")

# Comparison other drugs vs trazodone
forest(ranks0,
  label.left = "Favours trazodone",
  label.right = "Favours other drug")

# Comparison escitalopram vs other drugs
forest(ranks0, reference.group = "esc", baseline = FALSE,
  label.left = "Favours other drug",
  label.right = "Favours escitalopram")

## Not run: 
# Store a PDF file in the current working directory showing all results
# (this is the default, i.e., if argument 'reference.group' is missing)
forest(ranks0, baseline = FALSE, reference.group = trts,
  file = "forest_tcc_antidepressants.pdf")

# Run analysis with full data set
net1 <- netmeta(pw1, reference.group = "tra")

ranks1 <- tcc(net1, swd = 1.20, small.values = "undesirable")

# Comparison other drugs vs trazodone
forest(ranks1,
  label.left = "Favours trazodone",
  label.right = "Favours other drug")

# Comparison escitalopram vs other drugs
forest(ranks1, reference.group = "esc", baseline = FALSE,
  label.left = "Favours other drug",
  label.right = "Favours escitalopram")

## End(Not run)

mtrank documentation built on June 8, 2025, 11:12 a.m.