R package mtrank enables the production of clinically relevant 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). 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.
Theodoros Evrenoglou <theodoros.evrenoglou@uniklinik-freiburg.de>, Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>
The R package mtrank provides the following functions:
Function tcc defines the TCC and transforms the
study-specific relative treatment effects into a preference format.
Function mtrank synthesizes the output of the
tcc function and estimates the final treatment ability.
Forest plots are created either for the results of the
TCC (forest.tcc) or the final ability estimates
(forest.mtrank).
Function paired_pref uses the ability estimates
obtained from mtrank to calculate pairwise probabilities
that any treatment 'A' can be better, equal, or worse than any other
treatment 'B' in the network.
Type help(package = "mtrank") for a listing of R functions
available in mtrank.
Type citation("mtrank") on how to cite mtrank
in publications.
To report problems and bugs, please send an email to Theodoros Evrenoglou <theodoros.evrenoglou@uniklinik-freiburg.de>.
The development version of mtrank is available on GitHub https://github.com/TEvrenoglou/mtrank.
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
Useful links: