mtrank: Estimate the treatment hierarchy in network meta-analysis...

In mtrank: Ranking using Probabilistic Models and Treatment Choice Criteria

Estimate the treatment hierarchy in network meta-analysis using a probabilistic ranking model

Description

This function fits the Davidson-Bradley-Terry ranking model and produces a treatment hierarchy based on the method described by Evrenoglou et al. (2024) for network meta-analysis.

Usage

mtrank(x, level = x$level, ...)

## S3 method for class 'mtrank'
print(
  x,
  sorting = "ability",
  backtransf = FALSE,
  digits = gs("digits"),
  digits.prop = gs("digits.prop"),
  ...
)

Arguments

x	An object of class tcc or mtrank (print function).
level	The level used to calculate confidence intervals for ability estimates.
...	Additional arguments (passed on to PlackettLuce or to prmatrix).
sorting	An argument specifying the criterion to sort the ability estimates in the printout (see Details).
backtransf	A logical argument specifying whether to show log-ability estimates (FALSE, default) or on the natural scale (TRUE).
digits	Minimal number of significant digits for ability estimates, see print.default.
digits.prop	Minimal number of significant digits for proportions, see print.default.

Details

This function fits a Davidson-Bradley-Terry model to the treatment preferences tcc function. It estimates the ability of each treatment to outperform the other treatments in the network, along with the respective standard errors, using a maximum likelihood approach. The term 'ability to outperform' refers to a latent characteristic that indicates the propensity of each treatment in the network to yield clinically relevant and beneficial treatment effects, in the context of the defined treatment choice criterion, when compared to the rest of the treatments. Consequently, treatments with larger ability estimates are ranked more prominently in the treatment hierarchy.

To retain identifiability, the maximization of the log-likelihood takes place subject to the constrain that the ability estimates sum to 1. Then, the maximum likelihood estimates (MLEs) are calculated iteratively. Note that the final estimates of the ability parameters are not necessarily needed to sum to 1 as after the first iteration of the algorithm the ability estimates are not normalized. However, by normalizing the final ability estimates to sum up to 1 these can be interpreted as "the probability that each treatment is having the highest ability".

Finally, a parameter "v" controlling the prevalence of ties in the network is also estimated. Although the estimated values of this parameter do not have a direct interpretation they are useful for estimating the fitted pairwise probabilities (see fitted.mtrank).

Value

• A data frame containing the resulting log-ability estimates, their standard errors and their confidence intervals.

• The estimate of the tie prevalence parameter v, on the log-scale.

• The normalized ability estimates for each treatment.

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

ranks <- tcc(net, swd = 1.20, small.values = "undesirable")
#
fit <- mtrank(ranks)

# Print log-ability estimates
fit
#
# Print ability estimates
print(fit, backtransf = TRUE)

# Visualize results
forest(fit)
 

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