A master protocol is defined as a protocol designed with multiple sub-studies, which may have different objectives and involve coordinated efforts to evaluate one or more investigational drugs in one or more disease subtypes within the overall trial structure. Master protocol trials include three trial designs: basket trials, umbrella trials, and platform trials.

FDA guidance released in March 2022 provides recommendations for master protocol trials.

In this blog, we discuss master protocol trial designs, challenges and best practices, and the benefit of these innovative designs in oncology trials.

Types of master protocol trials

Basket trials

Basket trials are designed to test a single investigational drug or drug combination in different populations defined by different cancers, disease stages for a specific cancer, histologies, number of prior therapies, genetic or other biomarkers, or demographic characteristics.

Umbrella trials

Umbrella trials are designed to evaluate multiple investigational drugs administered as single drugs or as drug combinations in a single disease population.

Platform trials

Platform trials are master protocols in which arm(s) can be dropped or added based on knowledge gained from previously evaluated parts of the trial.

Figure 1: Basket Trials, Umbrella Trials, and Platform Trials

Image credit: Park, J. J. H., Siden, E., Zoratti, M. J., Dron, L., Harari, O., Singer, J., Lester, R. T., Thorlund, K., & Mills, E. J. (2019). Systematic review of basket trials, umbrella trials, and platform trials: A landscape analysis of master protocols. Trials, 20.

Key challenges with master protocol trials

Master protocol trials are inherently complex due to their expansive scope and varied components. Let’s refine these challenges further:

Data management and analysis

• Large amounts of data need efficient integration and processing.
• Basket trials involve multiple indications and endpoint definitions, and/or response criteria may vary across the indications.
• Umbrella trials have multiple drugs, leading to complex exposure and safety summaries.
• Platform trials continuously add new treatment arms, generating a dynamic dataset that requires real-time integration and analysis. This necessitates robust data management systems capable of handling evolving data structures and ensuring consistency across various cohorts.

Safety profile considerations

• Variability in drug effects requires tailored safety monitoring strategies.
• Adverse events of special interest might need to be defined for each drug separately.

Biomarker data complexity

• Data can be relatively large and complex.
• Having the data transfer specifications at an early stage is important to ensure that the correct data will be received and in the expected format.
• Intensive discussion might be needed with biomarker data specialists to define the rules for deriving biomarker/genomic profile of interest.
• Mapping those data from raw data to SDTM can also be challenging.

Statistical Analysis Plan (SAP) and shell development

• Potential additional complexity for statistical inference (e.g., adaptive features, multiplicity, and Bayesian methods).
• Require the team to focus on the main objectives of the study, otherwise SAP and shell can become very extensive.
• The number of tables, figures, and listings can grow significantly, making prioritization essential.
• Layout complexities arise when need to display numerous columns across multiple cohorts.

Operational and reporting challenges

• Each cohort may follow different timelines, complicating interim and final analyses.
• Frequent reportings require good planning.
• CSR(s) strategy (e.g., separate CSR for each cohort versus single CSR) should be defined sufficiently early.

Staying focused on the key study objectives is crucial to prevent data overload and inefficiencies in reporting. Exploratory analyses can be planned in a second step.

Comparative Overview: Basket vs. Umbrella vs. Platform Trials

(Click table to enlarge)

Final takeaways

Master protocol trials represent a transformative shift in clinical research — enabling the simultaneous evaluation of multiple therapies or disease subtypes under a unified framework. While designs like basket, umbrella, and platform trials offer flexibility and efficiency, they also introduce significant operational, statistical, and data management complexities.

Success is built on early planning, early discussion with safety and biomarker teams, and a focus on core study objectives to ensure meaningful insights and readiness.

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