Acceptability of Using Real-World Data to Estimate Relative Treatment Effects in Health Technology Assessments: Barriers and Future Steps [Editor's Choice]

May 1, 2024, 00:00 AM
Section Order : 623
First Page : 623


Evidence about the comparative effects of new treatments is typically collected in randomized controlled trials (RCTs). In some instances, RCTs are not possible, or their value is limited by an inability to capture treatment effects over the longer term or in all relevant population subgroups. In these cases, nonrandomized studies (NRS) using real-world data (RWD) are increasingly used to complement trial evidence on treatment effects for health technology assessment (HTA). However, there have been concerns over a lack of acceptability of this evidence by HTA agencies. This article aims to identify the barriers to the acceptance of NRS and steps that may facilitate increases in the acceptability of NRS in the future.


Opinions of the authorship team based on their experience in real-world evidence research in academic, HTA, and industry settings, supported by a critical assessment of existing studies.


Barriers were identified that are applicable to key stakeholder groups, including HTA agencies (eg, the lack of comprehensive methodological guidelines for using RWD), evidence generators (eg, avoidable deviations from best practices), and external stakeholders (eg, data controllers providing timely access to high-quality RWD). Future steps that may facilitate future acceptability of NRS include improvements in the quality, integration, and accessibility of RWD, wider use of demonstration projects to highlight the value and applicability of nonrandomized designs, living, and more detailed HTA guidelines, and improvements in HTA infrastructure relating to RWD.


NRS can represent a crucial source of evidence on treatment effects for use in HTA when RCT evidence is limited.
HEOR Topics :
  • Decision & Deliberative Processes
  • Health Technology Assessment
  • Methodological & Statistical Research
  • Modeling and simulation
  • Real World Data & Information Systems
  • Reproducibility & Replicability
  • Study Approaches
  • Surveys & Expert Panels
Tags :
  • causal inference
  • comparative effectiveness
  • health technology assessment
  • nonrandomized studies
  • real-world data
Regions :
  • Global