A review and comparison of arm‐based versus contrast‐based network meta‐analysis for binary outcomes—Understanding their differences and limitations

H Chu, L Lin, Z Wang, Z Wang, Y Chen… - Wiley …, 2024 - Wiley Online Library
Network meta‐analysis (NMA) is a statistical procedure to simultaneously compare multiple
interventions. Despite the added complexity of performing an NMA compared with the …

[HTML][HTML] Bridging randomized controlled trials and single-arm trials using commensurate priors in arm-based network meta-analysis

Z Wang, L Lin, T Murray, JS Hodges… - The annals of applied …, 2021 - ncbi.nlm.nih.gov
Network meta-analysis (NMA) is a powerful tool to compare multiple treatments directly and
indirectly by combining and contrasting multiple independent clinical trials. Because many …

Bayesian meta‐analysis using SAS PROC BGLIMM

KW Rott, L Lin, JS Hodges, L Siegel… - Research synthesis …, 2021 - Wiley Online Library
Meta‐analysis is commonly used to compare two treatments. Network meta‐analysis (NMA)
is a powerful extension for comparing and contrasting multiple treatments simultaneously in …

Network meta-analysis made simple: a composite likelihood approach

YL Liu, B Zhang, H Chu, Y Chen - medRxiv, 2024 - medrxiv.org
Network meta-analysis, also known as mixed treatments comparison meta-analysis or
multiple treatments meta-analysis, extends conventional pairwise meta-analysis by …

Shrinking the Variance in Experts'“Classical” Weights Used in Expert Judgment Aggregation

G Dharmarathne, GF Nane, A Robinson, AM Hanea - Forecasting, 2023 - mdpi.com
Mathematical aggregation of probabilistic expert judgments often involves weighted linear
combinations of experts' elicited probability distributions of uncertain quantities. Experts' …

[图书][B] Bayesian Meta-analysis Methods for Improving Accuracy and Adjusting for Publication Bias

T Gibson - 2022 - search.proquest.com
Meta-analysis (MA) combines multiple studies to estimate a quantity of interest. Some
existing MA models have shortcomings in the form of 1) inappropriate inference targets, 2) …