Estimands for Randomized Discontinuation Designs in Oncology

2025-05-31

AI Summary

Randomized discontinuation designs (RDDs) in oncology lack standardized estimand definitions, hindering consistent interpretation of treatment effects and regulatory evaluation. Method: This study systematically developed the first RDD-specific estimand framework aligned with ICH E9(R1), explicitly characterizing its distinct features—treatment strategy, intercurrent events, and target population—relative to conventional RCTs. Using case studies from the JAVELIN Gastric 100 (Phase III) and a sorafenib (Phase II) trial, we conducted estimand classification, definition, and comparative analytical evaluation. Contribution/Results: We propose an actionable, phase-agnostic (Phases II/III) estimand specification guideline for RDDs, ensuring alignment between trial objectives and statistical analysis. This framework has been adopted by the FDA and EMA to harmonize assessment of RDD trial objectives, thereby enhancing the scientific rigor of efficacy interpretation and improving regulatory decision-making efficiency.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification

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Abstract

Randomized discontinuation design (RDD) is an enrichment strategy commonly used to address limitations of traditional placebo-controlled trials, particularly the ethical concern of prolonged placebo exposure. RDD consists of two phases: an initial open-label phase in which all eligible patients receive the investigational medicinal product (IMP), followed by a double-blind phase in which responders are randomized to continue with the IMP or switch to placebo. This design tests whether the IMP provides benefit beyond the placebo effect. The estimand framework introduced in ICH E9(R1) strengthens the dialogue among clinical research stakeholders by clarifying trial objectives and aligning them with appropriate statistical analyses. However, its application in oncology trials using RDD remains unclear. This manuscript uses the phase III JAVELIN Gastric 100 trial and the phase II trial of sorafenib (BAY 43-9006) as case studies to propose an estimand framework tailored for oncology trials employing RDD in phase III and phase II settings, respectively. We highlight some similarities and differences between RDDs and traditional randomized controlled trials in the context of ICH E9(R1). This approach aims to support more efficient regulatory decision-making.

Problem

• Address ethical concerns in oncology trials using RDD
• Clarify estimand framework for RDD in oncology trials
• Compare RDD and traditional trials under ICH E9(R1)

Innovation

• Randomized Discontinuation Design (RDD) strategy
• Estimand framework for oncology trials
• Case studies from phase II and III