Predictive analytics apply statistical and modeling techniques to current and historical data, making predictions about future events based on information about past and current ones.
Predictive analytics can be used to assess risks and predict trends generally. In clinical trials, predictive analytics can be particularly useful in helping clinical trial teams identify potential participants, choose a trial site and dates, set schedules for enrollment periods, and more. Anju’s TA Scan uses predictive analytics to improve clinical trial planning and execution.
The Rise of Predictive Analytics
Predictive analytics uses regression analysis, machine learning, and other forms of analysis to glean patterns from past data. These patterns are used to make predictions about what may happen in the future, on the assumption that what will happen next is more or less likely to follow the same route as what happened before.
Current uses for predictive analytics in the life sciences include:
- Identifying patterns and anomalies in transactions or clinical data to spot errors or prevent data tampering.
- Predict which patients are likely to miss appointments, have difficulty adhering to treatment regimens, or drop out of a clinical trial or other course of treatment.
- Analyzing use patterns at clinical trial sites and other healthcare facilities to determine where potential study participants might be found — or where they may already be participating in a competing study or treatment.
Clinical trials remain the gold standard for demonstrating the efficacy and safety of new treatments and therapies, write Bin Zhang and fellow researchers in a 2023 article in Communications Medicine. Yet high failure rates persist in the clinical trial sphere. Zhang et al. identify two main reasons for these failure rates:
- Poor patient cohort selection and recruiting methods.
- Failures to monitor patients effectively during the trial itself.
As artificial intelligence, machine learning, and related analytics tools become more commonplace, these technologies can be leveraged to address the main reasons clinical trials fail. They can also be used to help clinical trial planning and development teams make decisions about site selection, enrollment periods, and other essential elements of clinical trial design and setup.
Benefits and Challenges of Data-Driven Trial Design
Using predictive analytics in clinical trial design can help address one of the biggest challenges design teams face: recruiting a complete, high-quality participant cohort.
“The most time-consuming part of a clinical trial is recruiting patients, taking up to one-third of the study length. One in five trials don’t even recruit the required number of people, and nearly all trials exceed the expected recruitment timelines,” explains science writer Matthew Hutson in an article in Nature.
Predictive analytics can make recommendations for building participant cohorts based on past information about clinical trial participants. An analysis of current and historical clinical trial data can help trial teams choose sites closer to available participants and avoid competing with other trials for the same patient cohort.
“Clinical development is also facing increasing demands to generate targeted and meaningful data,” write Chris Anagnostopoulos and fellow authors at McKinsey. By providing insights into how clinical trials have proceeded in the past, predictive analytics can allow clinical trial design and planning teams to make more informed decisions about how to proceed — including how to gather, maintain, and analyze data.
Gathering data and using predictive analytics can be challenging. Even models that demonstrate strong predictive abilities cannot help clinical trial teams produce better outcomes in trial design or execution on their own, according to Nehal Hassan and fellow researchers. Using predictive analytics is a valuable first step only if the insights gleaned are readily available to clinicians — at the right time and in formats that are easy to search, visualize, and share.
Optimizing Predictive Analytics Use During the Design Phase
Predictive analytics uses historical data to identify and report patterns. These patterns can provide valuable insights into what has worked in past clinical trials, as well as what is gaining or losing traction and what ought to be avoided.
Anju’s TA Scan enables highly precise predictive analytics of clinical trial outcome scenarios:
- The simulation tool uses aggregated public clinical trial data to analyze and optimize assumptions for target patient profile enrollment. The simulation capabilities receive regular updates, ensuring they’re always working from the most recent available clinical trial data.
- TA Scan’s feasibility algorithm allows planning teams to make strategic decisions on country-level trial initiation, timelines, and target enrollment using the predictive enrollment scenarios based on patient population and the occurrence and location of competing clinical studies during enrollment periods. The algorithm also provides recommendations for site availability and scheduling dates.
- Adaptive, interactive reporting spans all disease indications on local, national, or global levels, providing deeper insights into trial, investigator, and study site data. Search results can be organized into exportable files or maintained within TA Scan to take advantage of automatic analysis updates based on new information.
Predictive analytics do not replace the work of clinical trial planning teams. Instead, TA Scan uses predictive analytics to provide data-driven insights based on current information regarding more than 400,000 clinical trials occurring in hundreds of countries worldwide. With these insights, clinical trial teams can lower costs, boost success rates, and speed time to market for new therapies and treatments.
Images used under license by Shutterstock.com.
Authored by Elke Ydens, Associate Director of Business Solutions, Data Division
Elke Ydens, Associate Director of Business Solutions at Anju’s Data Division, brings over a decade of life sciences experience and a PhD in Biochemistry and Biotechnology from the University of Antwerp. As a Subject Matter Expert in Data Science, she adeptly addresses customer needs, leveraging her background in neuro-immunology and biochemistry. Elke remains dedicated to professional growth, contributing to industry publications, and staying updated on industry trends, while also finding success in extracurricular pursuits, formerly competing in world and European bridge championships, and more recently active in beekeeping and coaching. Connect with Elke on LinkedIn to explore her achievements further.