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"Designing Simpler Oncology Trials to Improve Feasibility"

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Oncology clinical trials are among the most complex in drug development, often characterized by demanding schedules of assessments, frequent amendments, intricate protocol requirements, and challenges with patient recruitment. This complexity places heavy burdens on sites and participants while creating downstream operational challenges such as delayed study startup, increased workload, protocol deviations, and participant attrition. By benchmarking oncology startup and operational data against general clinical trial performance, sponsors can better pinpoint the unique drivers of delay and apply design and technology strategies to address them.

This session will highlight practical approaches to streamlining oncology trial design, beginning with stronger alignment between clinical development and operations stakeholders. Panelists will explore common pitfalls that hinder execution, strategies for setting clear goals and KPIs to reduce unnecessary complexity, and best practices for creating participant and site-friendly protocols. Discussion will also examine the role of data and AI in informing smarter design decisions. Attendees will gain actionable insights into how oncology trials can balance scientific rigor with operational feasibility—ultimately leading to improved outcomes and a more sustainable research environment.

Attendees will walk away with a clearer understanding of:

• How the level of complexity of oncology study designs compares to other therapeutic areas
• Ways to streamline the study design process across clinical development and clinical operations stakeholders
• Identifying challenges in study designs that impact downstream operations
• Approaches to optimize trial design to reduce complexity for participants and research sites
• The role of data and AI in study design

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