Georgia Salanti1, A E Ades, John P A Ioannidis. 1. Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece. gsalanti@cc.uoi.gr
Abstract
OBJECTIVE: To present some simple graphical and quantitative ways to assist interpretation and improve presentation of results from multiple-treatment meta-analysis (MTM). STUDY DESIGN AND SETTING: We reanalyze a published network of trials comparing various antiplatelet interventions regarding the incidence of serious vascular events using Bayesian approaches for random effects MTM, and we explore the advantages and drawbacks of various traditional and new forms of quantitative displays and graphical presentations of results. RESULTS: We present the results under various forms, conventionally based on the mean of the distribution of the effect sizes; based on predictions; based on ranking probabilities; and finally, based on probabilities to be within an acceptable range from a reference. We show how to obtain and present results on ranking of all treatments and how to appraise the overall ranks. CONCLUSIONS: Bayesian methodology offers a multitude of ways to present results from MTM models, as it enables a natural and easy estimation of all measures based on probabilities, ranks, or predictions.
OBJECTIVE: To present some simple graphical and quantitative ways to assist interpretation and improve presentation of results from multiple-treatment meta-analysis (MTM). STUDY DESIGN AND SETTING: We reanalyze a published network of trials comparing various antiplatelet interventions regarding the incidence of serious vascular events using Bayesian approaches for random effects MTM, and we explore the advantages and drawbacks of various traditional and new forms of quantitative displays and graphical presentations of results. RESULTS: We present the results under various forms, conventionally based on the mean of the distribution of the effect sizes; based on predictions; based on ranking probabilities; and finally, based on probabilities to be within an acceptable range from a reference. We show how to obtain and present results on ranking of all treatments and how to appraise the overall ranks. CONCLUSIONS: Bayesian methodology offers a multitude of ways to present results from MTM models, as it enables a natural and easy estimation of all measures based on probabilities, ranks, or predictions.
Authors: Brian Hutton; Mona Hersi; Wei Cheng; Misty Pratt; Pauline Barbeau; Sasha Mazzarello; Nadera Ahmadzai; Becky Skidmore; Scott C Morgan; Louise Bordeleau; Pamela K Ginex; Behnam Sadeghirad; Rebecca L Morgan; Katherine Marie Cole; Mark Clemons Journal: Oncol Nurs Forum Date: 2020-07-01 Impact factor: 2.172
Authors: Jing Zhang; Bradley P Carlin; James D Neaton; Guoxing G Soon; Lei Nie; Robert Kane; Beth A Virnig; Haitao Chu Journal: Clin Trials Date: 2013-10-03 Impact factor: 2.486