Decision-Making Criteria and Methods for Initiating Late-Stage Clinical Trials in Drug Development From a...

The decision-making process in drug development involves "go/no-go" decisions, particularly at the transition from early to late-stage trials. While the decisions are solely made by drug developers, they must take into account the perspectives of multiple stakeholders-such as regulatory agencies, HTA bodies, payers, patients, and ethics committees-to ensure well-informed and robust decision-making. These perspectives influence key considerations, including resource allocation, risk mitigation, regulatory compliance, etc. To support this process, quantitative methodologies, including Bayesian and hybrid frequentist-Bayesian approaches, have been introduced to improve decision-making. However, these methodologies often do not fully account for the diverse priorities and needs of all stakeholders. This scoping review examines criteria and methods used in decision-making at the phase II to III transition, with a focus on broadening the probability of success (PoS) concept beyond efficacy alone. Our review explores PoS for different success definitions, such as regulatory approval, market access, financial viability, and competitive performance. Key themes include decision criteria selection, trial design optimization, utility-based approaches, financial metrics, and multi-stakeholder considerations in decision-making. Our findings highlight both the limitations of current methodologies and potential paths forward, including the integration of real-world data (RWD) and advanced analytics. This work complements a companion manuscript by Cetinyurek-Yavuz et al. (2025) providing a detailed review of PoS methodologies focused solely on efficacy, specifically PoS for achieving statistical significance in phase III studies, including definitions, terminologies, and analytical approaches. Together, these studies provide a foundation for advancing late-stage trial decisions toward a more balanced, data-driven, and stakeholder-aligned approach.