Quantitative Bias Analysis for Single-Arm Trials With External Control Arms
QBA was feasible and informative in ECA analyses in which residual confounding was expected to be the most important source of bias. These findings encourage further exploration of how QBA can help quantify the impact of bias in other settings and when using other data sources.
Authors
- Rolf H. H. Groenwold (ORCID: https://orcid.org/0000-0001-9238-6999)
- Evie Merinopoulou (ORCID: https://orcid.org/0000-0002-6677-2105)
- Sreeram V Ramagopalan (ORCID: https://orcid.org/0000-0002-4766-5160)
- Grace Hsu
- Sanjay Popat (ORCID: https://orcid.org/0000-0003-2087-4963)
- Alexandre Lockhart
- Samantha Wilkinson (ORCID: https://orcid.org/0000-0003-3897-6968)
- Alind Gupta (ORCID: https://orcid.org/0000-0003-1139-9769)
- Joshua Ray
- Paul Arora (ORCID: https://orcid.org/0000-0001-6617-3631)
- Nicolas Scheuer
- Miguel A. Hernán (ORCID: https://orcid.org/0000-0003-1619-8456)
- Stephen Duffield (ORCID: https://orcid.org/0000-0001-9906-1142)
- Seamus Kent
Institutions
- Roche (Switzerland) (CH)
- Harvard University (US)
- Institute of Cancer Research (GB)
- University of Toronto (CA)
- Leiden University Medical Center (NL)
- National Institute for Health and Care Excellence (GB)
- Merck Serono Ltd. (United Kingdom) (GB)
- Roche (United Kingdom) (GB)
- Royal Marsden Hospital (GB)
- Public Health Ontario (CA)
- Erasmus University Rotterdam (NL)
- Cytel (United States) (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.2152
- Citations
- 8
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 21.04