-Controlled Trials: Robust to Confounding and

Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med. 2016;375(23):2293–7.

Article  PubMed  Google Scholar 

Corrigan-Curay J, Sacks L, Woodcock J. Real-world evidence and real-world data for evaluating drug safety and effectiveness. JAMA. 2018;320(9):867–8.

Article  PubMed  Google Scholar 

FDA. Considerations for the Use of Real-World Data and Real-World Evidence To Support Regulatory Decision-Making for Drug and Biological Products. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-real-world-data-and-real-world-evidence-support-regulatory-decision-making-drug: Food and Drug Administration; 2023.

FDA. Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products. Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory: Food and Drug Administration; 2024.

ICH E10 I. Choice of Control Group and Related Issues in Clinical Trials. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e10-choice-control-group-and-related-issues-clinical-trials: Food and Drug Administration; 2001.

ICH E9(R1) I. Statistical Principles for Clinical Trials: Addendum: Estimands and Sensitivity Analysis in Clinical Trials. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e9r1-statistical-principles-clinical-trials-addendum-estimands-and-sensitivity-analysis-clinical: Food and Drug Administration; 2019.

EMA. Guideline on registry-based studies. https://www.ema.europa.eu/en/guideline-registry-based-studies-scientific-guideline: European Medicines Agency; 2021.

Jahanshahi M, Gregg K, Davis G, et al. The use of external controls in FDA regulatory decision making. Ther Innov Regul Sci. 2021;55(5):1019–35.

Article  PubMed  PubMed Central  Google Scholar 

Wang X, Dormont F, Lorenzato C, et al. Current perspectives for external control arms in oncology clinical trials: analysis of EMA approvals 2016-2021. J Cancer Policy. 2023;35:100403.

Article  PubMed  Google Scholar 

FDA. Considerations for the design and conduct of externally controlled trials for drug and biological products guidance for industry. Accessed: 2023–02–23: Food and Drug Administration. 2023.

EMA. Reflection paper on use of real-world data in non-interventional studies to generate real-world evidence—Scientific guideline. https://www.ema.europa.eu/en/reflection-paper-use-real-world-data-non-interventional-studies-generate-real-world-evidence-scientific-guideline: European Medicines Agency; 2025.

Burger HU, Gerlinger C, Harbron C, et al. The use of external controls: to what extent can it currently be recommended? Pharm Stat. 2021;20(6):1002–16.

Article  PubMed  Google Scholar 

Pocock SJ. The combination of randomized and historical controls in clinical trials. J Chronic Dis. 1976;29(3):175–88.

Article  CAS  PubMed  Google Scholar 

FDA. Rare Diseases: Natural History Studies for Drug Development Draft Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/rare-diseases-natural-history-studies-drug-development: Food and Drug Administration; 2019.

Wu J, Wang C, Toh S, et al. Use of real-world evidence in regulatory decisions for rare diseases in the United States-current status and future directions. Pharmacoepidemiol Drug Saf. 2020;29(10):1213–8.

Article  PubMed  Google Scholar 

Xu Y, Lu N, Yue L, et al. A study design for augmenting the control group in a randomized controlled trial: a quality process for interaction among stakeholders. Ther Innov Regul Sci. 2020;54(2):269–74.

Article  PubMed  Google Scholar 

Sengupta S, Ntambwe I, Tan K, et al. Emulating randomized controlled trials with hybrid control arms in oncology: a case study. Clin Pharmacol Ther. 2023;113(4):867–77.

Article  CAS  PubMed  Google Scholar 

Mulberg AE, Bucci-Rechtweg C, Giuliano J, et al. Regulatory strategies for rare diseases under current global regulatory statutes: a discussion with stakeholders. Orphanet J Rare Dis. 2019;14(1):36.

Article  PubMed  PubMed Central  Google Scholar 

van Eijk RPA, van den Berg LH, Roes KCB, et al. Hybrid Controlled Clinical Trials Using Concurrent Registries in Amyotrophic Lateral Sclerosis: A Feasibility Study. Clin Pharmacol Ther. 2023;114(4):883–92.

Article  PubMed  Google Scholar 

Spanakis E, Kron M, Bereswill M, et al. Addressing statistical issues when leveraging external control data in pediatric clinical trials using Bayesian dynamic borrowing. J Biopharm Stat. 2023;33(6):752–69.

Article  PubMed  Google Scholar 

Li C, Ferro A, Mhatre SK, et al. Hybrid-control arm construction using historical trial data for an early-phase, randomized controlled trial in metastatic colorectal cancer. Communications Medicine. 2022;2(1):90.

Article  PubMed  PubMed Central  Google Scholar 

Tan WK, Segal BD, Curtis MD, et al. Augmenting control arms with real-world data for cancer trials: Hybrid control arm methods and considerations. Contemp Clin Trials Commun. 2022;30:101000.

Article  PubMed  PubMed Central  Google Scholar 

Ventz S, Khozin S, Louv B, et al. The design and evaluation of hybrid controlled trials that leverage external data and randomization. Nat Commun. 2022;13(1):5783.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Davi R, Majumdar A, Bexon M, et al. CLRM-09 Incorporating external control arm In Mdna55 recurrent glioblastoma registration trial. Neuro-Oncology Adv. 2021;3(4):3.

Article  Google Scholar 

Sampson JH, Singh Achrol A, Aghi MK, et al. Targeting the IL4 receptor with MDNA55 in patients with recurrent glioblastoma: Results of a phase IIb trial. Neuro Oncol. 2023;25(6):1085–97.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Research FoC. 2019. Characterizing the use of external controls for augmenting randomized control arms and confirming benefit. White paper. Friends of Cancer Research

McMurray JJ, Solomon SD, Inzucchi SE, et al. Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med. 2019;381(21):1995–2008.

Article  CAS  PubMed  Google Scholar 

Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.

Article  Google Scholar 

Lin J, Gamalo-Siebers M, Tiwari R. Propensity score matched augmented controls in randomized clinical trials: A case study. Pharm Stat. 2018;17(5):629–47.

Article  PubMed  Google Scholar 

Wu L. Mixed effects models for complex data. Chapman and Hall/CRC; 2009.

Book  Google Scholar 

Chen WC, Wang C, Li H, et al. Propensity score-integrated composite likelihood approach for augmenting the control arm of a randomized controlled trial by incorporating real-world data. J Biopharm Stat. 2020;30(3):508–20.

Article  PubMed  Google Scholar 

Neuenschwander B, Capkun-Niggli G, Branson M, et al. Summarizing historical information on controls in clinical trials. Clin Trials. 2010;7(1):5–18.

Article  PubMed  Google Scholar 

van Rosmalen J, Dejardin D, van Norden Y, et al. Including historical data in the analysis of clinical trials: Is it worth the effort? Stat Methods Med Res. 2018;27(10):3167–82.

Article  PubMed  Google Scholar 

Su L, Chen X, Zhang J, et al. Comparative study of Bayesian information borrowing methods in oncology clinical trials. JCO Precis Oncol. 2022;6:e2100394.

Article  PubMed  PubMed Central  Google Scholar 

Wang C, Li H, Chen WC, et al. Propensity score-integrated power prior approach for incorporating real-world evidence in single-arm clinical studies. J Biopharm Stat. 2019;29(5):731–48.

Article  PubMed  Google Scholar 

Lu N, Wang C, Chen WC, et al. Propensity score-integrated power prior approach for augmenting the control arm of a randomized controlled trial by incorporating multiple external data sources. J Biopharm Stat. 2022;32(1):158–69.

Article  PubMed  Google Scholar 

Broglio K, Di R, Fanni Z, et al. Relative performance of frequentist and bayesian methods for incorporating external controls: a case study with patient level data from the DapaHF trial. Stat Biopharm Res. 2025. https://doi.org/10.1080/19466315.2025.2455178.

Article  Google Scholar 

Hobbs BP, Carlin BP, Sargent DJ. Adaptive adjustment of the randomization ratio using historical control data. Clin Trials. 2013;10(3):430–40.

Article  PubMed  PubMed Central  Google Scholar 

Liu M, Bunn V, Hupf B, et al. Propensity-score-based meta-analytic predictive prior for incorporating real-world and historical data. Stat Med. 2021;40(22):4794–808.

Article  PubMed  Google Scholar 

Lin J, Yu G, Gamalo M. Matching within a hybrid RCT/RWD: framework on associated causal estimands. J Biopharm Stat. 2023;33(4):439–51.

Article  PubMed 

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