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Master Protocols in Oncology Trials

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.

Innovations in Clinical Trial Design for CNS Disorders

Clinical research in central nervous system (CNS) diseases has long been fraught with challenges. High failure rates, complex pathophysiology, variability in disease progression, strong placebo effects, and difficulties in recruitment and outcome measurement have made CNS disorders one of the riskiest areas for drug development. However, recent innovations in trial design — coupled with advances in digital health and statistical modelling — are transforming how we conduct clinical research in diseases like Huntington’s disease (HD), Alzheimer’s disease (AD), and multiple sclerosis (MS). This blog explores three recent trials that exemplify these innovations and proposes statistical advancements to strengthen their impact.

Adaptive designs in Huntington’s disease: The PIVOT-HD trial

Traditional fixed designs often struggle to efficiently explore dose-response relationships or adapt to emerging data. Adaptive trial designs offer a dynamic solution, particularly valuable in neurodegenerative diseases like Huntington’s disease, where treatment response and disease progression can vary widely.

Case study: PIVOT-HD trial (NCT05358717)

The PIVOT-HD trial, led by PTC Therapeutics, is a Phase II adaptive study evaluating the safety, pharmacodynamics, and early signs of efficacy of PTC518, a novel small-molecule HTT-lowering therapy. PTC518 modulates mRNA splicing to reduce levels of the mutant huntingtin protein, a key driver of HD pathology.

What sets this trial apart is its seamless adaptive design. The trial is structured to adjust dosing and the randomization ratios based on interim pharmacodynamic and safety readouts. By incorporating planned decision-making, PIVOT-HD minimizes exposure to ineffective doses and accelerates identification of promising therapeutic windows.

Digital biomarkers and remote monitoring in Alzheimer’s disease: The DETECT-AD trial

Cognitive decline in AD is insidious and can be difficult to quantify with infrequent clinic visits and subjective tests. Digital health technologies are revolutionizing outcome assessment through continuous, objective, and sensitive data collection.

Case study: DETECT-AD (Digital Evaluations and Technologies Enabling Clinical Translation in Alzheimer’s Disease)

The DETECT-AD initiative, part of a broader effort supported by the NIH and multiple research institutions, is employing wearables, mobile apps, and speech analysis to detect early signs of Alzheimer’s disease in at-risk populations.

In the DETECT-AD observational study, participants use smartphone apps and passive sensors to monitor activities like walking, typing speed, and even voice characteristics. These digital biomarkers are being correlated with traditional cognitive assessments and brain imaging data to predict cognitive decline before clinical symptoms emerge.

Platform trials in multiple sclerosis: The OCTOPUS trial

In diseases like MS, where multiple mechanisms may underlie relapses and progression, traditional “one drug, one trial” designs are increasingly inefficient. Platform trials offer a more flexible and scalable solution.

Case study: The OCTOPUS trial (UK MS Society)

The OCTOPUS (Optimal Clinical Trials Platform for Progressive MS) trial is the world’s first multi-arm, multi-stage platform trial in progressive MS. Spearheaded by the UK MS Society, this innovative study aims to test multiple repurposed therapies simultaneously, using a shared control group and adaptive design principles.

OCTOPUS promises faster answers with fewer patients and more efficient use of resources, particularly crucial in progressive MS where effective treatments are lacking.

Statistical challenges and opportunities

Despite these advances, several statistical hurdles remain. Novel designs require equally innovative statistical approaches to preserve validity and ensure robust interpretation.

Broader adoption of Bayesian statistical frameworks

Bayesian approaches allow the integration of prior knowledge (e.g., historical control data or early biomarkers) and offer probabilistic interpretations of trial results. In adaptive and platform trials, Bayesian methods facilitate:

Interim analyses with posterior probabilities guiding adaptations.
Dynamic borrowing from concurrent or historical control arms.
Greater flexibility in endpoint modelling across heterogeneous subgroups.

For example, the GBM AGILE platform trial in glioblastoma (a CNS tumor) successfully uses Bayesian methods to adapt enrollment and determine early stopping rules. A similar framework could benefit complex CNS conditions like MS or AD, where responses are highly individualized.

Incorporating real-world evidence (RWE) in trial planning and analysis

As clinical trials increasingly occur alongside large electronic health record (EHR) systems, real-world data (RWD) can inform trial design and enhance external validity. Specifically:

RWD can help refine eligibility criteria to better represent actual patient populations.
Real-world comparators can augment underpowered control groups or offer external validation.
Longitudinal RWE provides insight into long-term treatment effects beyond trial duration.

In Alzheimer’s disease, initiatives like the AHEAD 3-45 study are already incorporating observational cohorts and RWE in trial simulation and endpoint modelling.

The next generation of neuroscience trials

The future of CNS clinical trials is increasingly adaptive, digital, and data driven. Innovative designs like PIVOT-HD, DETECT-AD, and OCTOPUS illustrate the power of new methodologies to make trials more efficient, sensitive, and patient-centric. However, to fully realize their potential, we must integrate robust statistical techniques such as Bayesian modelling and real-world data frameworks. These tools will help overcome inherent complexities in CNS research and bring transformative treatments closer to patients in need.

As we look ahead, collaboration between statisticians, clinicians, regulators, and technology developers will be essential in shaping the next generation of neuroscience trials — where precision, agility, and real-world relevance are no longer luxuries, but necessities.

Interested in learning more?

Register now to watch James Matcham’s on-demand webinar, “Clinical Trial Design Innovation in CNS Disorders.” This webinar features a review of regulatory guidelines and showcase recent successful trials in Alzheimer’s disease and other neurological disorders.

The Role of External Data in Oncology Drug Development

Randomized controlled trials (RCTs) remain the gold standard for the evaluation of the safety and effectiveness of a new treatment. However, in a number of cases alternative approaches leveraging external data (i.e., data from outside of a clinical trial) — ranging from single arm trials to augmented RCTs — can be appropriate. Here, we discuss how to leverage and incorporate external data in drug development, focusing on the use of external control arms and Bayesian borrowing.

Understanding Master Protocol Designs: Platform and Basket Trials

A clinical trial usually seeks to evaluate the effects of a candidate drug in a carefully pre-specified patient population. Every detail of the trial must be outlined in the Clinical Study Protocol (CSP), including the exact inclusion and exclusion criteria for patients, the exact variables to be measured, and the statistical hypotheses to be tested.

Platform trials and basket trials, however, are innovative study designs that allow researchers to explore multiple treatments or target multiple patient populations simultaneously under a single overarching CSP, called a master protocol. Such approaches are elegant in that sponsors may start a new study arm to investigate an additional indication, dose, or inclusion criterion in parallel with the ongoing clinical trial, without needing to write a new CSP for each new study arm (which would also need to be applied for and approved by authorities). On the other hand, the planning and writing of the CSP for platform and basket trials up front requires a lot more effort than that of a traditional study.

Here, I outline benefits and challenges of these groundbreaking methodologies.

Platform trials make it possible to add study arms

Based on a particular disease, a platform trial investigates different treatments, doses, or subgroups of patients, all in different study arms. In particular, it is possible to add study arms that were not predefined in the study protocol: It is possible to start a trial, keep it running over years, and introduce new potential treatments as they appear and after evaluation of the older treatment arms. Platform trials are adaptive, as new parts of the trial may be chosen based on knowledge gained from previously evaluated parts of the trial. Allocation rates between ongoing treatment arms may also be adapted to optimize patient recruitment.

However, platform trials may come with administrative challenges

Platform trials can be notoriously difficult to administer. The CSP (i.e., the master protocol) needs to consider precise instructions for how future decisions will be made regarding the number of interventions active at the same time, the allocation of new patients between interventions and control groups, the frequency of interim evaluations, and the rules for stopping and starting interventions at interim evaluations.

Yet platform trials are helpful in collaborative projects

Despite the administrative challenges, a platform trial may be very beneficial in, for example, collaborative projects between multiple clinics or academic groups worldwide. Multiple groups of researchers may contribute to the larger project, enabling the comparison of different treatment strategies through the streamlined study arms detailed by the master protocol. Research groups may be able to share control groups and quickly adapt to new or evolving therapeutic landscapes. The STAMPEDE prostate cancer study is an example in which 12,000 patients were enrolled between 2005 and 2023.¹ Another example is the I-SPY platform trial, in which 28 active interventions against breast cancer have been tested so far since the start of recruitment in 2010.²

Basket trials allow for multiple indications

Unlike platform designs, basket designs do not permit adding new treatments during the trial. Instead, while the trial targets a specific therapy, it allows sponsors to test multiple indications. Think of each basket coming with a new set of patients, with their own inclusion and exclusion criteria, to a trial. Each basket will be randomized to its own study arms (usually active and control treatment arms), but the outcome of the study may be a combination of the results from all the different study arms. This way, a proof of concept may be approached early and jointly between, say, different cancer indications that may be candidates for the same drug. The assessment of each indication may be derived given the results of the other indications, for example, using a Bayesian method.

Common criticism of basket trial designs

Basket designs do get criticized for enabling a positive study outcome even in situations where no indication shows sufficient efficacy on its own. This is a justified comment. As you go into a follow-up study to recruit a larger number of patients with a single indication, your amount of evidence from a positive basket trial may be very light for the specific indication. This means the follow-up study has a larger element of gambling than it would have had were the first efficacy study based on that same single indication. We cannot be sure that there really is a treatment effect in one particular indication.

When are basket designs useful?

For the reason mentioned, a basket design makes the most sense when there is clinical reason that all the indications can be improved by the same molecular drug mechanism. Perhaps because the indications were all caused by the same mechanism. In such cases, coherent results in different patient populations do strengthen each other. The BRAF V600 Vemurafenib is an example basket trial in which patients had the same mutation (BRAF V600) but different diagnoses.³ It included 122 patients from 5 indications (NSCLC, Colorectal, Cholangiocarcinoma, ECD or LCH, and Thyroid) plus one “other” basket.

Interested in learning more? Download our complimentary ebook, Adaptive Trial Design, which outlines common adaptive trial designs, benefits of adaptive trials, how to optimize your adaptive trial, and a ten-point framework to determine if your trial should be adaptive.

Understanding the Economic Benefits of Platform Trials

Many thanks to Kyle Wathen and Behnam Sharif for their input on this post.

As clinical trials become more complex and innovative, trial sponsors are becoming more interested in the strategic benefits offered by master protocols and, specifically, platform trials. A new paper in JAMA, co-authored by Cytel statisticians and colleagues at Harvard University, McMaster University, the University of British Columbia, and a number of other distinguished research institutions, compares the economic costs of platform trials versus trials with two treatments wherein a single intervention is compared to a control. The results offer a glimpse into the financial benefits sponsors can receive from platform trials.

What it means to be a lead analyst on a Global COVID-19 Trial

The TOGETHER Trial for COVID-19 therapies, designed by clinical trial specialists at Cytel won the Society for Clinical Trials David Sackett Trial of the Year Award for 2021. I interviewed Hinda Ruton, Research Associate at Cytel, who made significant contributions as Lead Analyst to several studies in TOGETHER Trial.

Hinda has been working as a lead statistician and statistical programmer for clinical trials, and statistician for real world data (RWD) analysis. Prior to joining Cytel, he was the program coordinator of the Rwanda Human Resources for Health program in the Ministry of Health. He coordinated a program of $150 million USD, aiming to build the capacity of health professionals. This was done in collaboration with 25 of the best teaching institutions in the US. Hinda has an academic appointment at the University of Rwanda where he teaches biostatistics and information management systems.

Read more »

The TOGETHER Trial Journey: Interview with Ofir Harari

The award-winning TOGETHER Trial was designed with the vision of ensuring that COVID-19 therapies are both effective and accessible to the majority of people, especially in the low- and middle-income countries. Members of the TOGETHER Trial, led by Principal Researcher Dr. Edward Mills (Cytel & McMaster), studied existing interventions as possible treatments for COVID-19. The TOGETHER Trial recently won the Society of Clinical Trials David Sackett Trial of the Year Award for 2021.

I interviewed Ofir Harari, Senior Research Principal (Statistics) at Cytel, who passionately worked on the TOGETHER Trial from its inception. Ofir has been working in the field of statistics and data analysis since 2007. His experience includes design and analysis of randomized and cluster-randomized clinical trials, Bayesian adaptive designs, statistical emulation, geospatial analysis, and network meta-analysis. At Cytel, Ofir leads projects in the area of real-world analytics. Prior to joining Cytel, Ofir was a postdoctoral fellow at the University of Toronto and Simon Fraser University. Ofir’s interest and expertise lie in the intersection of statistical methodology and software development.

Read more »

Society of Clinical Trials names TOGETHER “Trial of the Year”

Early in the pandemic, it became clear that many of the COVID-19 therapies being tested in wealthier nations, were not taking into consideration the accessibility of these medicines in low- and middle-income countries (LMICs). An adaptive platform trial was quickly developed to test repurposed medicines in LMICs to ensure affordable and equitable access. TOGETHER has now launched in Brazil, the Democratic Republic of Congo, Pakistan, South Africa and Vietnam. It has enrolled over 6000 patients and just received the Society of Clinical Trial’s David Sackett Trial of the Year Award for 2021.

Strategies for Selecting New Indications for a Platform Trial

Thanks to Dr. Kyle Wathen for comments on this blog.

The increasing use of platform trials for the testing of a wide range of therapies raises new questions for trial design optimization and simulation. A challenge, however, is ensuring that strategies for selecting new indications for a platform are built into the risk-mitigation strategies that often go into optimizing trial design. In other words, a part of de-risking a platform trial requires a design that is robust and flexible for unknown indications that could be added in the future. In a recent Cytel webinar, Dr. Kyle Wathen, VP of Scientific Strategy and Innovation, examines this and related issues.

Platform Trials, Can they Benefit Animal Studies?

Master protocols and platform clinical trials have become an innovative and efficient approach to testing multiple compounds in a single and consistent framework. But how can they be applied to animal studies? At the BAYES2022: Bayesian Biostatistics conference (October 12–14, 2022, in Bethesda, Maryland), Cytel’s VP of Scientific Strategy and Innovation Kyle Wathen and Senior Research Principal Krishna Padmanabhan will be presenting. Dr. Wathen’s abstract on such platform trials is below along with his commentary on this important and timely upcoming talk (See our previous post for more information on Dr. Padmanabhan’s talk.)

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Types of master protocol trials

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

Key challenges with master protocol trials

Comparative Overview: Basket vs. Umbrella vs. Platform Trials

Final takeaways

Adaptive designs in Huntington’s disease: The PIVOT-HD trial

Case study: PIVOT-HD trial (NCT05358717)

Digital biomarkers and remote monitoring in Alzheimer’s disease: The DETECT-AD trial

Case study: DETECT-AD (Digital Evaluations and Technologies Enabling Clinical Translation in Alzheimer’s Disease)

Platform trials in multiple sclerosis: The OCTOPUS trial

Case study: The OCTOPUS trial (UK MS Society)

Statistical challenges and opportunities

Broader adoption of Bayesian statistical frameworks

Incorporating real-world evidence (RWE) in trial planning and analysis

The next generation of neuroscience trials

Interested in learning more?

Platform trials make it possible to add study arms

However, platform trials may come with administrative challenges

Yet platform trials are helpful in collaborative projects

Basket trials allow for multiple indications

Common criticism of basket trial designs

When are basket designs useful?