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Network meta-analysis (NMA) allow combining efficacy information from multiple comparisons from trials assessing different therapeutic interventions for a given disease and to estimate unobserved comparisons from a network of observed…

Methodology · Statistics 2016-04-08 Victoria Nyaga , Marc Aerts , Marc Arbyn

Network meta-analysis (NMA) is a technique used in medical statistics to combine evidence from multiple medical trials. NMA defines an inference and information processing problem on a network of treatment options and trials connecting the…

Statistical Mechanics · Physics 2022-11-30 Annabel L. Davies , Tobias Galla

In recent years, there has been growing research interest in addressing treatment hierarchy questions within network meta-analysis (NMA). In NMAs involving many treatments, the number of possible hierarchy questions becomes prohibitively…

Methodology · Statistics 2025-10-13 Caitlin H. Daly , Chloe Tan , Audrey Béliveau

Meta-analyses of diagnostic test accuracy (DTA) studies have been gathering attention in research in clinical epidemiology and health technology development, and bivariate random-effects model is becoming a standard tool. However, standard…

Methodology · Statistics 2020-06-19 Tsubasa Ito , Shonosuke Sugasawa

Network Meta-Analysis (NMA) plays a pivotal role in synthesizing evidence from various sources and comparing multiple interventions. At its core, NMA relies on integrating both direct evidence from head-to-head comparisons and indirect…

Network meta-analysis (NMA) is a statistical technique for the comparison of treatment options. The nodes of the network are the competing treatments and edges represent comparisons of treatments in trials. Outcomes of Bayesian NMA include…

Methodology · Statistics 2024-01-04 Annabel L Davies , Tobias Galla

Background: Subgroup analyses and meta-regression are commonly used to investigate heterogeneity in diagnostic test accuracy (DTA) meta-analyses (MA), but adherence to methodological guidance is unclear. This methodological review…

In network meta-analysis (NMA), we synthesize all relevant evidence about health outcomes with competing treatments. The evidence may come from randomized controlled trials (RCT) or non-randomized studies (NRS) as individual participant…

Meta-analysis of diagnostic test accuracy (DTA) is the powerful statistical method for synthesizing and evaluating the diagnostic capacity of the medical tests and has been extensively used by clinical physicians and healthcare…

Applications · Statistics 2023-03-07 Shosuke Mizutani , Yi Zhou , Yu-Shi Tian , Tatsuya Takagi , Tadayasu Ohkubo , Satoshi Hattori

A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for…

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here we provide a new model and…

Methodology · Statistics 2017-08-16 Dan Jackson , Sylwia Bujkiewicz , Martin Law , Richard D Riley , Ian White

There has been a transition from broad to more specific research questions in the practice of network meta-analysis (NMA). Such convergence is also taking place in the context of individual registrational trials, following the recent…

Applications · Statistics 2026-05-07 Antonio Remiro-Azócar , Anders Gorst-Rasmussen

Component network meta-analysis (CNMA) is a statistical methodology that enables estimation of relative effects for multi-component treatments, such as combinations of antidepressants, and individual components, such as single…

Methodology · Statistics 2026-05-15 Augustine Wigle , Audrey Béliveau , Adriani Nikolakopoulou , Lifeng Lin

Meta-analysis represents a widely accepted approach for evaluating the accuracy of diagnostic tools in clinical and psychological investigations. This paper investigates the applicability of multinomial tree models recently suggested in the…

Methodology · Statistics 2024-05-29 Annamaria Guolo

Network meta-analysis (NMA) is widely used to compare multiple interventions simultaneously by synthesizing direct and indirect evidence. The general fixed or random effects contrast-based NMA model can be applied to different outcomes and…

Methodology · Statistics 2026-03-03 Harlan Campbell , Jeroen P. Jansen

Accurate prediction of Drug-Target Affinity (DTA) is crucial for reducing experimental costs and accelerating early screening in computational drug discovery. While sequence-based deep learning methods avoid reliance on costly 3D…

Machine Learning · Computer Science 2025-11-03 Minghui Li , Yuanhang Wang , Peijin Guo , Wei Wan , Shengshan Hu , Shengqing Hu

Component network meta-analysis (CNMA) models are an extension of standard network meta-analysis (NMA) models which account for the use of multicomponent treatments in the network. This article contributes innovatively to several…

Methodology · Statistics 2025-07-23 Augustine Wigle , Audrey Béliveau

Network meta-analysis (NMA) is a useful tool to compare multiple interventions simultaneously in a single meta-analysis, it can be very helpful for medical decision making when the study aims to find the best therapy among several active…

Methodology · Statistics 2024-02-02 Ao Huang , Yi Zhou , Satoshi Hattori

For many conditions, it is of clinical importance to know not just the ability of a test to distinguish between those with and without the disease, but also the sensitivity to detect disease at different stages: in particular, the test's…

Methodology · Statistics 2026-02-10 Efthymia Derezea , Nicky J Welton , Gabriel Rogers , Hayley E Jones

Network meta-analysis (NMA) is widely used in evidence synthesis to estimate the effects of several competing interventions for a given clinical condition. One of the challenges is that it is not possible in disconnected networks. Component…

Methodology · Statistics 2022-05-24 Maria Petropoulou , Gerta Rücker , Stephanie Weibel , Peter Kranke , Guido Schwarzer
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