The role of quantitative bias analysis for nonrandomized comparisons in health technology assessment: recommendations from an expert workshop

Abstract

The use of treatment effects derived from nonrandomized studies (NRS) in health technology assessment (HTA) is growing. NRS carry an inherently greater risk of bias than randomized controlled trials (RCTs). Although bias can be mitigated to some extent through appropriate approaches to study design and analysis, concerns around data availability and quality and the absence of randomization mean residual biases typically render the interpretation of NRS challenging. Quantitative bias analysis (QBA) methods are a range of methods that use additional, typically external, data to understand the potential impact that unmeasured confounding and other biases including selection bias and time biases can have on the results (i.e., treatment effects) from an NRS. QBA has the potential to support HTA bodies in using NRS to support decision-making by quantifying the magnitude, direction, and uncertainty of biases. However, there are a number of key aspects of the use of QBA in HTA which have received limited discussion. This paper presents recommendations for the use of QBA in HTA developed using a multi-stakeholder workshop of experts in HTA with a focus on QBA for unmeasured confounding.