Bayesian Bias-Adjustment Models in Network Meta-Analysis of COVID-19 trials
Elaona Lemoto, Qiao Wang, PhD., Susan Halabi, PhD., Hwanhee Hong, PhD.
Abstract
Clinical trials involving treatments for COVID-19 have shown varying levels of rigor and consistency, but very few studies have addressed the potential bias in estimating the treatment effect from these trials. A large body of literature have shown that including trials at risk in a network-meta analysis (NMA) could result in biased treatment effect estimates. Network meta-analysis combines multiple trials to create evidence about the comparative effectiveness of multiple treatments. In this presentation, we will introduce Bayesian bias-adjustment methods in NMA under contrast-based and arm-based frameworks to estimate bias-corrected treatment effects. The risk of bias of a trial is classified into three groups: low, some, or high concerns using the Cochrane risk-of-bias tool. Our method proposes a probabilistic model to incorporate uncertainty from studies given ‘some concerns’. We present an extensive simulation study to evaluate model performance and illustrate our methods using NMA of COVID-19 trials. The results present the impact of including studies with risk of bias in NMA and how they should be interpreted.
Date
March 12, 2024
Time
8:15 AM – 8:45 AM
Location
Baltimore, Maryland, USA