Speaker
Description
Continuous monitoring (CM) at AstraZeneca is the systematic review and evaluation of accumulating study data to inform timely decisions. Rather than waiting for formal interims or study completion, our CM approach in early phase oncology studies, enables earlier data-driven decisions to stop for futility or safety, minimising exposure to ineffective or unsafe treatments, and to accelerate promising therapies. We present a framework that aligns statistical decision-making with operational feasibility, improving patient centricity, consistency, and auditability.
The proposed Bayesian frameworks utilise posterior or predictive probability decision rules and evaluate operating characteristics to guide when to start CM and how frequently to analyse data; balancing performance and operational feasibility. We assess probabilities of making correct decisions based on prespecified benchmarks, expected sample size, and patient allocation at suboptimal doses across clinically relevant scenarios. Given dose optimisation is often conducted whilst a compound’s full safety profile emerges, the framework monitors both binomial safety (frequency of ≥ grade 3 adverse events) and efficacy endpoints (e.g. objective response rate). Optimal statistical considerations are presented alongside potential operational constraints, including data entry lag, transfer timelines, tolerance for unclean/missing data and scope of the outputs to support decision making.
Health authority feedback has guided our recommendations for when to implement CM, ensuring alignment with regulatory expectations. The framework provides standards and templates for rigorous, reproducible CM plans for early-phase study design and conduct. By standardising CM, we deliver reliable safety and efficacy decisions, reducing time and exposure at non-optimal doses, supporting complex development plans.
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