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mtrank  

Ranking using Probabilistic Models and Treatment Choice Criteria
View on CRAN: Click here


Download and install mtrank package within the R console
Install from CRAN:
install.packages("mtrank")

Install from Github:
library("remotes")
install_github("cran/mtrank")

Install by package version:
library("remotes")
install_version("mtrank", "0.2-0")



Attach the package and use:
library("mtrank")
Maintained by
Theodoros Evrenoglou
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-01-31
Latest Update: 2025-06-04
Description:
Implementation of a novel frequentist approach to produce clinically relevant treatment hierarchies in network meta-analysis. The method is based on treatment choice criteria (TCC) and probabilistic ranking models, as described by Evrenoglou et al. (2024) <doi:10.48550/arXiv.2406.10612>. The TCC are defined using a rule based on the minimal clinically important difference. Using the defined TCC, the study-level data (i.e., treatment effects and standard errors) are first transformed into a preference format, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). The preference data are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatment’s ability to outperform all the other competing treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list.
How to cite:
Theodoros Evrenoglou (2025). mtrank: Ranking using Probabilistic Models and Treatment Choice Criteria. R package version 0.2-0, https://cran.r-project.org/web/packages/mtrank. Accessed 18 Sep. 2026.
Previous versions and publish date:
(2026-07-09 06:33), 0.1-0 (2025-01-31 16:50), 0.1-1 (2025-02-26 12:30)
Other packages that cited mtrank R package
View mtrank citation profile
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Complete documentation for mtrank
Functions, R codes and Examples using the mtrank R package
Full mtrank package functions and examples
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