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Ranking using Probabilistic Models and Treatment Choice Criteria

v0.2-0 · Jun 4, 2025 · GPL (>= 2)

Description

Estimation of treatment hierarchies in network meta-analysis using a novel frequentist approach 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 smallest worthwhile difference (SWD). Using the defined TCC, the NMA estimates (i.e., treatment effects and standard errors) are first transformed into treatment preferences, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). These treatment preferences 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 treatments ability to outperform all the other competing treatments in the network. Here the terms ability to outperform indicates the propensity of each treatment to yield clinically important and beneficial effects when compared to all the other treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list.

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Dependency Network

Dependencies Reverse dependencies meta netmeta PlackettLuce dplyr magrittr ggplot2 mtrank

Version History

4 tracked
new 0.2-0 Mar 10, 2026
updated 0.2-0 ← 0.1-1 diff Jun 3, 2025
updated 0.1-1 ← 0.1-0 diff Feb 25, 2025
new 0.1-0 Jan 30, 2025