학술논문

Exploring the Potential of External Control Arms created from Patient Level Data: A case study in non-small cell lung cancer.
Document Type
Article
Source
Journal of Biopharmaceutical Statistics. 2022, Vol. 32 Issue 1, p204-218. 15p. 4 Charts, 6 Graphs.
Subject
Language
ISSN
1054-3406
Abstract
Randomized controlled trials (RCTs) are the gold standard for evaluation of new medical products. However, RCTs may not always be ethical or feasible. In cases where the investigational product is available outside the trial (e.g., through accelerated approval), patients may fail to enroll in clinical trials or drop out early to take the investigational product. These challenges to enrolling or maintaining a concurrent control arm may compromise timely recruitment, retention, or compliance. This can threaten the study's integrity, including the validity of results. External control arms (ECAs) may be a promising augmentation to RCTs when encountered with challenges that threaten the feasibility and reliability of a randomized controlled clinical trial. Here, we propose the use of ECAs created from patient-level data from previously conducted clinical trials or real-world data in the same indication. Propensity score methods are used to balance observed disease characteristics and demographics in the previous clinical trial or real-world data with those of present-day trial participants assigned to receive the investigational product. These methods are explored in a case study in non-small cell lung cancer (NSCLC) derived from multiple previously conducted open label or blinded phase 2 and 3 multinational clinical trials initiated between 2004 and 2013. The case study indicated that when balanced for baseline characteristics, the overall survival estimates from the ECA were very similar to those of the target randomized control, based on Kaplan–Meier curves and hazard ratio and confidence interval estimates. This suggests that in the future, a randomized control may be able to be augmented by an ECA without compromising the understanding of the treatment effect, assuming sufficient knowledge, measurement, and availability of all or most of the important prognostic variables. [ABSTRACT FROM AUTHOR]