53 citations · 187 across the 11 of their papers we have counts for
12 papers
Estimating heterogeneous treatment effects from randomised trials: a comparison of the risk modelling and treatment effect modelling approaches
Frederik Luca Philipona, Marta Mainetti, Orestis Efthimiou +1
Introduction Risk modelling (RM) and treatment effect modelling (EM) are two approaches to build models to estimate heterogeneous treatment effects from randomized control trials (…
Precision of Treatment Hierarchy: A Metric for Quantifying Certainty in Treatment Hierarchies from Network Meta-Analysis
Augustine Wigle, Audrey Béliveau, Georgia Salanti +4
Network meta-analysis (NMA) is an extension of pairwise meta-analysis which facilitates the estimation of relative effects for multiple competing treatments. A hierarchy of treatme…
Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments
Konstantina Chalkou, Tasnim Hamza, Pascal Benkert +7
Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that…
Synthesizing cross-design evidence and cross-format data using network meta-regression
Tasnim Hamza, Konstantina Chalkou, Fabio Pellegrini +9
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) o…
Decision curve analysis for personalized treatment choice between multiple options
Konstantina Chalkou, Andrew J. Vickers, Fabio Pellegrini +2
Decision curve analysis can be used to determine whether a personalized model for treatment benefit would lead to better clinical decisions. Decision curve analysis methods have be…
Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis
Konstantina Chalkou, Ewout Steyerberg, Patrick Bossuyt +7
Prognosis on the occurrence of relapses in individuals with Relapsing-Remitting Multiple Sclerosis (RRMS), the most common subtype of Multiple Sclerosis (MS), could support individ…
A dose-effect network meta-analysis model: an application in antidepressants
Tasnim Hamza, Toshi A. Furukawa, Nicola Orsini +3
Network meta-analysis (NMA) has been used to answer a range of clinical questions about the preferable intervention for a given condition. Although the effectiveness and safety of…
Rethinking the Funding Line at the Swiss National Science Foundation: Bayesian Ranking and Lottery
Rachel Heyard, Manuela Ott, Georgia Salanti +1
Funding agencies rely on peer review and expert panels to select the research deserving funding. Peer review has limitations, including bias against risky proposals or interdiscipl…
Introducing the treatment hierarchy question in network meta-analysis
Georgia Salanti, Adriani Nikolakopoulou, Orestis Efthimou +3
Background: Comparative effectiveness research using network meta-analysis can present a hierarchy of competing treatments, from the least to most preferable option. However, the r…
A Bayesian dose-response meta-analysis model: simulation study and application
Tasnim Hamza, Andrea Cipriani, Toshi A. Furukawa +3
Dose-response models express the effect of different dose or exposure levels on a specific outcome. In meta-analysis, where aggregated-level data is available, dose-response eviden…
A two-stage prediction model for heterogeneous effects of many treatment options: application to drugs for Multiple Sclerosis
Konstantina Chalkou, Ewout Steyerberg, Matthias Egger +3
Treatment effects vary across different patients and estimation of this variability is important for clinical decisions. The aim is to develop a model to estimate the benefit of al…
Testing small study effects in multivariate meta-analysis
Chuan Hong, Georgia Salanti, Sally Morton +4
Small study effects occur when smaller studies show different, often larger, treatment effects than large ones, which may threaten the validity of systematic reviews and meta-analy…