Navigating Complexity: Challenges and Opportunities in Clinical Trial Data Management for Oncology Research

Key learnings from a roundtable discussion with industry experts, May 2025

Introduction

As cancer cases grow globally, oncology research funding continues to increase and innovation is needed more than ever. Increasingly, oncology researchers are also exploring novel modalities such as cell and gene therapy, antibody-drug conjugates (ADCs), epigenetic biomarkers, and more to develop new targeted methods of detection and treatment aligned with a given patient’s profile. To bring forth the next generation of breakthrough treatments, research teams must rely on clinical technology to manage study data, ensure accurate and complete trial records, and more quickly translate data into insights.

As study complexity increases, technology must keep pace – yet many solutions on the market today were designed for a simpler research paradigm defined by static protocols, fixed designs, and single-intervention studies. When traditional systems are stretched to address complex research needs, timelines extend and costs increase, creating barriers to study agility and potentially delaying the market availability of life-saving treatments.

A group of experts in oncology trial data management recently convened in Raleigh, North Carolina to discuss the role of eClinical technology in powering the future of oncology research and innovation. This industry insights report explores key themes from that discussion, including common challenges research teams face when working with traditional electronic data capture (EDC) systems and opportunities for technology specifically designed for complex research to better accommodate the unique demands of modern oncology trials.  

The Promise and Challenges of Complex Trial Design

Adaptive Trial Design

Traditionally, clinical trials rely on a study protocol that clearly defines inclusion and exclusion criteria, study visit schedule, randomization and study arm design, and treatment plans. In contrast, many oncology studies typically require greater sophistication, with teams increasingly employing adaptive study designs to be able to act more quickly on accumulating safety and efficacy data. These studies rely on the ability to dynamically adjust dosing regimens, inclusion/exclusion criteria, sample size, and endpoints in response to initial insights – all of which require the EDC to be able to recognize and respond to complex decision-making logic while maintaining data integrity and compliant audit trials. In the words of one data management leader:

“As oncology protocols grow increasingly complex—incorporating specialty labs, imaging, extended durations, blinding, and other intricate components—sponsors must rely on EDC systems that can support frequent amendments. Sponsors have to partner with CROs that have specialized therapeutic expertise or hire in-house personnel capable of designing and implementing these protocols, both of which can be costly.”

This level of protocol complexity is challenging to build out in traditional EDCs; many systems were built for basic study workflows and simply cannot accommodate the flexibility demands of these more complex trials. For those systems that do offer greater flexibility, complex builds typically require in-depth product expertise, advanced programming, and outsourced support and training. This all contributes to extended build times and costly resourcing, slowing innovation and placing heavy financial burden in particular on small and midsize research organizations.

Patient Registration & Cohort Tracking

In line with the above, oncology studies often include sophisticated study features such as basket and umbrella trials with multiple study sub-arms, cycle-based data collection, and rolling patient enrollment. These complex designs make participant enrollment and cohort tracking particularly critical and challenging. Trials may be structured to test a single treatment across patients with common tumor mutations but different diagnoses, a range of treatment options across a given diagnosis, or a single treatment for a specific diagnosis across a range of biomarkers. Early-phase studies may seek to dynamically expand cohorts to accelerate more personalized insights by sub-arm after dose escalation evaluation is complete. Oncology studies also tend to face higher dropout rates due to disease progression and adverse events.

Across these complexities, many EDCs are not natively built to facilitate ongoing eligibility reviews, rolling patient registrations, and visibility into cohorts over time. In particular, traditional EDC systems do not make it easy to look across participants’ data to identify trends and facilitate essential cohort-level decision-making at cohort review meetings, such as expansion or closure based on frequency of DLTs (dose-limiting toxicities), optimal clinical dose and interim patient response to treatment.

Protocol Amendments and Mid-Study Updates

Oncology studies also face a higher incidence of protocol amendments as researchers seek to act quickly on available data. While many systems can accommodate small changes without EDC down time, larger amendments such as modifying eligibility criteria or adding or removing a study arm can take significant time to implement in traditional systems. These changes can lead to months-long delays and costly database migrations, risking data integrity and slowing time-to-market for innovation and access to new treatments for patients.

Opportunities for the Future

To accommodate the complex requirements of study design in oncology without sacrificing speed and accuracy, EDC technology must incorporate sophisticated protocol version controls, code-free complex calculations and edit checks (including across forms), and options to set triggers at the aggregate level to better track adverse events (AEs), serious adverse events (SAEs), and interim patient responses over time.

Most critically, systems must be able to respond dynamically to both internal and external decision-making logic in order to track a unique treatment path for a given study participant. They must also facilitate seamless mid-study updates and protocol amendments without risk to the live database or the ongoing audit trail.

The Cost of Disconnect Between Protocol and Study Builder

Often exacerbating current data management challenges is a disconnect between the team that scoped and validated the study protocol and the team that builds out the study database to facilitate data collection and analysis. This introduces outsized risk for complex study designs, including common oncology research features like cycle-based treatments, complex dosing algorithms, and biomarker-driven decision trees. Without deep clinical context, database programmers can misinterpret or oversimplify requirements, resulting in unintuitive study design and operational friction for clinical teams.

A Dedicated Service Model

When working with a platform with more sophisticated build capabilities, onboarding to a new EDC is daunting. Study builders find themselves faced with high stakes and time pressure to learn new functionality while simultaneously seeking to align the database build with the precise requirements of the study protocol. Third-party and outsourced models of training and support beyond the technology vendor introduce added risk, extending timelines and layering on incremental cost while complicating the team structure between vendor, consultants, trainers, and internal builders.

EDC vendors must play an active role in surmounting this learning gap by simplifying their service models and prioritizing personalized service and hands-on, in-house support in addition to more seamless technology. Options to co-build with EDC product experts dedicated to reading and understanding the full study protocol can reduce build risk and facilitate more seamless knowledge transfer on both protocol and technical build between clinical operators, data managers, and builders. As one experienced data management leader shared:

“It is critical for the programmers who are working on the initial protocol as well as protocol amendments to understand the visit structure, the forms contained within each visit as well as understanding which forms may need to be triggered dynamically based on prior responses. If this does not occur, it can lead to errors in programming which are then discovered during UAT by data management and which must then be corrected with additional rounds of testing. The consequence is an extended timeline, a frustrated data management team and unhappy sponsor and clinical team.”

Risks to Site Compliance and Data Integrity

The implications of limited protocol expertise in the study build extend beyond inconvenience for clinical operators. When systems do not align with the natural clinical workflow, issues flow downstream as sites develop workarounds that can compromise data quality and introduce compliance risk. Most acutely, poorly designed systems can delay patient enrollment and extend study timelines if site users are not comfortable with EDC workflows and data entry requirements.

Many EDC systems exacerbate this risk with poor end user experience. Historically, EDC providers have paid little attention to site satisfaction, designing interfaces that feel confusing and unintuitive to clinical teams more familiar with paper forms.

For clinical operators, recruiting and retaining sites is crucial to study success – any barrier to site adoption and retention introduces risk of lower enrollment, reduced compliance, and potential for study delays. To mitigate these challenges, teams must spend more time training and monitoring sites, increasing burden on the clinical teams and leading to frustration for on-the-ground investigators.

Better Solutions for Sites

To support site compliance, EDC providers must prioritize ease of use for investigators and site personnel in addition to designing for project leads, builders, and monitors. This means providing electronic case report forms (eCRFs) that more closely mirror familiar paper forms, organizing the interface to flow logically within a patient visit, and reducing the number of clicks necessary to navigate between relevant pages in the EDC.

The Importance of Real-Time Monitoring and Data Availability

Throughout oncology studies, data transparency and real-time monitoring of AEs, SAEs, and interim efficacy indicators is paramount to ensuring patient safety and effective resource allocation. Real-time availability of aggregate study results is crucial to inform next steps in adaptive trials, both within the system and through seamless exports to enable external analysis by biostatisticians and third-party data monitoring committees (DMCs).

This requirement is particularly significant in early phase trials where safety decisions must be made rapidly based on accumulating data.  Key metrics like dose-limiting toxicities (DLTs) and biomarker correlations help researchers determine how to adapt the trial over time, while data like progression-free survival curves provide greater insights into treatment efficacy.

Centralized Monitoring and Easy Exports

To facilitate timely review of metrics across cohorts, sites, and study milestones, EDC platforms must support centralized monitoring, streamlined query management, seamless data exports, and data triggers to proactively alert researchers when key metrics such as DLTs hit critical thresholds.  

Bloated Costs and Extended Project Timelines

From complex designs requiring outsourced training and long study builds to delays for mid-study updates, the current state of data management for oncology research requires exorbitant investment of both time and money. Research teams are often faced with complicated billing, multiple six-figure software contracts, and complicated networks of in-house workers and third-party vendors and consultants in order to complete a single study.

The level of coordination and investment necessary to execute complex research with existing EDC options places particular strain on small and midsize research organizations, delaying the timeline to recoup investments and to get effective treatments to market. Because the work is so important and an EDC system is required to complete it, sponsors and CROs find themselves at the will of software providers, facing limited alternatives in response to rising prices and extended timelines. One round table participant explained that:

“The more advanced and adaptable the system, the higher the cost—expenses that can quickly erode study budgets and place additional strain on the sponsors. This underscores the importance of selecting the right EDC partner—whether outsourced or managed in-house—to ensure both efficiency and long-term value.”

A Shift Towards Transparency

To better support the next wave of medical innovation in oncology, EDC providers must shift towards less complicated pricing models and service and technology offerings that simplify the procurement and study build process. When study designs require multiple technologies – whether it be for complex randomization, decentralized data collection, or advanced data analysis – vendors can reduce the burden on clinical teams by providing easy-to-use APIs and consultative service and support to facilitate more seamless collaboration across platforms.

Looking to the Future: Opportunities in Oncology Trial Data Management

In summary, the increasing complexity and urgency of oncology clinical research necessitates technology solutions and service offerings natively designed to adapt to more sophisticated study requirements. Decision makers at oncology sponsors and CROs can support research teams – including clinical operators, data managers, monitors, site investigators, and internal and third-party statisticians – by evaluating data management systems against a core set of criteria:

• Select a system purpose-built for adaptive trials and complex study logic, including the ability to incorporate dynamic recruitment and cohort management, complex calculations, branching logic, and cross-form edit checks without heavy coding as well as support for mid-study updates without data migration or downtime
• Look for vendors who offer hands-on training and support in-house, including dedicated resources who can partner with internal teams to bridge the gap between clinical depth and technical build expertise
• Prioritize site user experience as a key contributor to data integrity, on-time study completion, and research success
• Evaluate both in-product data monitoring and review features, including the ability to set up triggers and alerts across aggregate study data, and ease of exporting data in appropriate formats for external/third-party analysis
• Consider total cost of ownership – including software license price, services, training, internal resourcing, and timelines – when evaluating systems, and push for less complicated pricing and billing to aid in comparisons

By prioritizing these criteria in their technology selection process, organizations can better position themselves to deliver high-quality oncology research that ultimately accelerates life-saving treatments to patients.

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About the sponsor

This expert roundtable and resulting white paper are sponsored by Prelude, an eClinical technology platform focused on providing solutions that keep pace with the evolving needs of modern complex clinical research.

Prelude empowers our partners to accelerate the development of new medicines and devices with highly configurable eClinical data management solutions and unmatched in-house customer support. Prelude simplifies the research process from study build to closeout with a system designed to support complex studies while delivering a superior user experience to sites and investigators.