Extended Multi-Stage Drop-the-Losers Design for Multi-Arm Clinical Trials Using Binary and Survival Endpoints.

Authors

Manuel Pfister, Pierre Colin

Published in

Biometrical journal. Biometrische Zeitschrift. Volume 68. Issue 5. Pages e70174.

Abstract

In oncology, clinical trials are a cornerstone of evaluating new treatments. However, traditional designs face significant challenges, particularly when assessing multiple treatment options. The emergence of multi-arm multi-stage (MAMS) designs, such as the drop-the-losers (DtL) approach, offers innovative solutions by combining multiple hypotheses within a single trial. This work evaluates the DtL approach. We extend the design to binary and survival endpoints. The statistical performance (Type I error control, statistical power, and biases) of the DtL design was assessed using a simulation study. Results demonstrated that the DtL design effectively balances statistical rigor and efficiency. The design strongly controls Type I error while ensuring high power in detecting drug effects. Scenarios with ineffective treatments highlighted the advantage of eliminating suboptimal options early. One limitation identified is that long-term survival endpoints may not be mature enough to support early treatment selection. Early decision-making is a key aspect of adaptive designs to support futility analysis or early efficacy analysis. It is critical to define properly the decision thresholds for such early analyses. Updating the endpoint of interest over time also aligns with clinical practices, starting with ORR, transitioning to benefit-risk scores or progression-free survival (PFS), and concluding with overall survival (OS) at the final analysis. Such an approach requires handling of correlations between drug effects across these endpoints. This work provides a comprehensive framework for implementing DtL design, demonstrating its potential to accelerate treatment evaluation in oncology.

PMID:
42798161
Bibliographic data and abstract were imported from PubMed on 26 Sep 2026.

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