In the world of clinical trials, timelines are everything, with speed to market, regulatory approval, and ultimately patient access to new therapies, all dependent on how efficiently trials can be conducted.
One of the most critical factors in clinical trial success is patient recruitment, but with the complex nature of clinical trials and the sheer number of variables present, we still see around 80% of trials failing to meet initial enrollment targets and timelines. With these delays in recruitment often snowballing into missed deadlines, escalating costs, and regulatory setbacks, it’s no surprise we continue to see more advanced approaches to establish realistic forecasting.
Below, we examine the importance of data in supporting predictable clinical trial timelines, and how data solutions, such as those offered by Anju’s TA Scan, can help address these issues through high-quality benchmark data and enrollment simulations.
Leveraging Data for Precision
Enrollment benchmarking has become an invaluable tool for driving more predictability in trial timelines, ensuring sponsors can establish more realistic expectations, effectively allocate operational resources, and mitigate risks associated with specific patient populations and trial designs.
At the study level, enrollment benchmarking plays a key role in trial budgeting and financial planning. Clinical trials are costly, and with a large portion of that cost directly tied to patient recruitment, through site fees, investigator payments and study monitoring, even a slight deviation from a projected timeline can mean significant extra costs. Knowing how many patients are likely to enroll, and how many sites you might need to achieve a given recruitment target can make or break a study.
Country-level benchmarking is also key where data is available, as recruitment rates can vary dramatically between countries due to differences in healthcare infrastructure, regulatory environments, disease prevalence, existing standard of care and awareness and attitudes to clinical trials.
How Data Supports Clinical Trial Planning and Execution
Historical Data: Leveraging enrollment data from historical studies in the same patient population, trial designs, and regions helps sponsors forecast recruitment rates, identify potential challenges, and adjust strategies as needed. This minimizes guesswork and improves the accuracy of planning.
Predictive Analytics: Advanced data analytics allow sponsors to go beyond historical trends using mathematical modelling. By predicting potential enrollment challenges or identifying site capacity issues at trial planning or early operational stages, data can empower sponsors to make proactive decisions to support timelines.
Real-Time Monitoring: Through monitoring real-time data from ongoing trials, sponsors can track recruitment progress and course correct if enrollment is slower than anticipated. As an example, sponsors could allocate additional funds for outreach, recruitment campaigns, or patient support initiatives, or they could initiate new sites within existing countries. This dynamic approach to data keeps trials agile and responsive, preventing costly delays.
Balancing Data with Real-World Experience
There is no doubt that benchmark data is key to estimating recruitment rates, but the knowledge of clinical teams and their real-world experience in disease areas is critical to ensuring accurate country and study-level projections.
TA Scan’s Trial Feasibility Flex is a new analytics module within TA Scan, which combines the power of highly curated historical clinical data from TA Scan, with our users’ own intelligence, to enable precise enrollment projections tailored to a specific trial design. Through its simulation module, users can assess the impact of a wide range of variables – such as country selection, patient distribution at the country level, regulatory delay, anticipated recruitment rate, and other trial parameters – on clinical outcomes.
Trial Feasibility Flex allows users to:
- Build enrollment scenarios with high-quality data, enhanced by user insights, for accurate and tailored predictive enrollment projections.
- Visualize projected country-level timelines, with options to customize regulatory delay.
- Prioritize sites based on experience and capacity to take on additional trials, and fine-tune projections and site recommendations by adjusting trial parameters.
TA Scan Trial Feasibility Flex
TA Scan Trial Feasibility Flex allows the incorporation of internal insights on top of high-quality external data, to generate precise predictive enrollment scenarios for streamlined and impactful trial planning.
Enrollment benchmarking is a powerful tool that supports clinical trial success at every stage—from planning and budgeting to execution.
By analyzing data at the study level, sponsors can make more accurate financial projections, allocate resources efficiently, and minimize unexpected costs. At the country level, tailored benchmarks ensure recruitment strategies are optimized for each region’s unique challenges.
However, clinical teams still face considerable challenges. Sites often overcommit, and conventional benchmark data may overlook the complex variables involved in a given planned trial. As trials become more globalized and target increasingly niche patient populations, the need for granular data to drive modelling is essential, as is the ability to customize input parameters based on experience and operational knowledge to drive precision. The more specific the data, the better the benchmark.
This synergy of robust data and expert input provides sponsors with the most accurate outputs in forecasting and scenario planning, with tools such as TA Scan available to support. With this approach, sponsors are able to optimize enrollment projections, identifying risks on time, and driving more successful trial outcomes.
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Authored by Jenna Morris, Executive Director of Data Solutions, Data Division
Jenna Morris, Executive Director of Data Solutions at Anju Software, brings over 18 years of expertise in data, clinical technology, and clinical research. Jenna supports Anju’s global data strategy, leveraging her extensive experience in optimizing clinical trial efficiency. Passionate about data-driven insights and advanced technologies, she focuses on building strategic operational frameworks that drive successful clinical programs. Jenna is committed to enhancing precision in clinical trial processes and maximizing outcomes for impactful research. Connect with Jenna on LinkedIn to learn more about her work and insights.