Estimation of treatment effects in early-phase randomized clinical trials involving external control data

• Published in: Journal of biopharmaceutical statistics Vol. 34; no. 5; pp. 680 - 699
• Main Authors: Götte, Heiko, Kirchner, Marietta, Krisam, Johannes, Allignol, Arthur, Schüler, Armin, Kieser, Meinhard
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 02.09.2024; Taylor & Francis Ltd
• Subjects: Clinical trials; Computer Simulation; Data Interpretation, Statistical; Early drug development; estimation; external control data; Humans; Models, Statistical; Proportional Hazards Models; Randomized Controlled Trials as Topic - methods; Randomized Controlled Trials as Topic - statistics & numerical data; Research Design - statistics & numerical data; RMSE; Sample Size; Simulation; time-to-event data; Treatment Outcome; estimation; external control data; RMSE; Early drug development; time-to-event data
• ISSN: 1054-3406; 1520-5711; 1520-5711
• DOI: 10.1080/10543406.2023.2256835

There are good reasons to perform a randomized controlled trial (RCT) even in early phases of clinical development. However, the low sample sizes in those settings lead to high variability of the treatment effect estimate. The variability could be reduced by adding external control data if available. For the common setting of suitable subject-level control group data only available from one external (clinical trial or real-world) data source, we evaluate different analysis options for estimating the treatment effect via hazard ratios. The impact of the external control data is usually guided by the level of similarity with the current RCT data. Such level of similarity can be determined via outcome and/or baseline covariate data comparisons. We provide an overview over existing methods, propose a novel option for a combined assessment of outcome and baseline data, and compare a selected set of approaches in a simulation study under varying assumptions regarding observable and unobservable confounder distributions using a time-to-event model. Our various simulation scenarios also reflect the differences between external clinical trial and real-world data. Data combinations via simple outcome-based borrowing or simple propensity score weighting with baseline covariate data are not recommended. Analysis options which conflate outcome and baseline covariate data perform best in our simulation study.