A Framework for Accelerating Clinical Development With Real-World External Control Arms (ECA) in Phase 2 Trials
Author(s)
Cherrishe Brown-Bickerstaff, MPH, PhD1, Paul R. Conkling, MD2, Zhaohui Su, PhD3, Jessica Paulus, ScD4, Malcolm Charles, MS1, Daniel Schlauch, PhD1.
1Ontada, Boston, MA, USA, 2Ontada (McKesson), Boston, MA, USA, 3McKesson, Boston, MA, USA, 4Senior Director, Ontada, Boston, MA, USA.
1Ontada, Boston, MA, USA, 2Ontada (McKesson), Boston, MA, USA, 3McKesson, Boston, MA, USA, 4Senior Director, Ontada, Boston, MA, USA.
OBJECTIVES: External control arms (ECAs) are an established tool to provide valuable supplementary evidence in Phase 3 trials but their potential to offer critical context for in the Phase 2 trial setting is underexplored. We therefore sought to develop a framework for generating an ECA for the Phase 2 trial setting that addresses the inherent challenges of these trials, including their relatively low sample sizes.
METHODS: A literature review of best practices in ECA design for Phase 3 trials was conducted and used to develop a protocol framework for an ECA for a Phase 2 single arm trial. To address the challenge of limited trial patient data for matching, we implemented a simulation approach to facilitate the matching process with the RWD ECA. This framework was applied to a pilot RWD ECA for a newly launched Phase 2 trial of Tucatinib and Doxil in HER2+ metastatic breast cancer (mBC).
RESULTS: This framework establishes key principles for ECA development in the Phase 2 setting, including (1) systematic RWD source identification and curation, leveraging both structured data and supplemental unstructured data; (2) rigorous emulation and application of trial eligibility criteria to define a comparable real-world cohort; and (3) implementation of adaptive matching strategies to address limited trial patient data. To navigate the challenge of small trial cohorts, the framework's adaptive matching component involved creating a simulated dataset via multiple imputation based on enrolled patients.
CONCLUSIONS: Successful implementation of an ECA in a Phase 2 trial setting necessitates a robust understanding of RWD availability and proactive strategies, such as data simulation, to address the complexities of patient matching in small, slow-accruing trials. Further exploration into adaptive ECA methodologies, including advanced simulation techniques, for such trials is warranted.
METHODS: A literature review of best practices in ECA design for Phase 3 trials was conducted and used to develop a protocol framework for an ECA for a Phase 2 single arm trial. To address the challenge of limited trial patient data for matching, we implemented a simulation approach to facilitate the matching process with the RWD ECA. This framework was applied to a pilot RWD ECA for a newly launched Phase 2 trial of Tucatinib and Doxil in HER2+ metastatic breast cancer (mBC).
RESULTS: This framework establishes key principles for ECA development in the Phase 2 setting, including (1) systematic RWD source identification and curation, leveraging both structured data and supplemental unstructured data; (2) rigorous emulation and application of trial eligibility criteria to define a comparable real-world cohort; and (3) implementation of adaptive matching strategies to address limited trial patient data. To navigate the challenge of small trial cohorts, the framework's adaptive matching component involved creating a simulated dataset via multiple imputation based on enrolled patients.
CONCLUSIONS: Successful implementation of an ECA in a Phase 2 trial setting necessitates a robust understanding of RWD availability and proactive strategies, such as data simulation, to address the complexities of patient matching in small, slow-accruing trials. Further exploration into adaptive ECA methodologies, including advanced simulation techniques, for such trials is warranted.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
P24
Topic
Methodological & Statistical Research, Real World Data & Information Systems, Study Approaches
Disease
Oncology