This blog is part one in a series exploring the use of real-world evidence to produce meaningful insights in clinical pharma.
Clinical trials are crucial for developing new medications and treatments, but they require meticulous pre-planning and feasibility assessments, which can be extremely time-consuming. Real-world evidence (RWE) offers a promising solution to expedite this process. Derived from sources such as electronic health records (EHRs) and claims data, RWE provides invaluable insights into real-world patient populations, aiding in various aspects of clinical trial feasibility studies.
This blog will explore the myriad applications of RWE to facilitate clinical trial feasibility studies. It’s part one of a blog series deep diving into real world evidence applications into clinical pharma.
What is a clinical trial feasibility study?
A clinical trial feasibility study is a vital preliminary step in the clinical trial process. It involves carrying out a comprehensive assessment prior to initiating a clinical trial or a new pharmaceutical, treatment or technology, to evaluate the practicality and viability of the proposed study. Feasibility studies are conducted to ensure that the trial can be carried out effectively, ethically, and with efficient use of resources.
Some of the most crucial aspects of feasibility studies include ensuring that the overall study design best aligns with the research objectives, evaluating the suitability of potential trial sites, such as hospitals and clinics, and analyzing the patient population, to determine the feasible sample size for clinical trials.
How can RWE assist in clinical trial feasibility studies?
RWE can aid this process in a number of ways. Firstly, by leveraging RWE, researchers can estimate the size of the target patient population for a trial. This helps determine if enough patients can be recruited within a reasonable timeframe and at the planned sites. Furthermore, RWE enables researchers to evaluate how realistic the study’s proposed inclusion and exclusion criteria are. Analyzing existing data can help determine if criteria are too strict or potentially excluding large proportions of the patient population.
Finally, RWE can inform study design, for example, by helping researchers determine a feasible number of clinic visits and follow-up periods to minimize patient burden. Leveraging RWE, researchers can also test different trial designs, like varying eligibility criteria, and estimate potential outcomes before the actual trial begins.
However, it’s important to note that RWE has limitations. The data may not perfectly capture all the details needed for a clinical trial, and there can be biases in how data is collected. RWE should be viewed as complementary to traditional feasibility studies, not a replacement.
How to conduct a feasibility study for a clinical trial using RWD:
RWD can enhance feasibility studies in a number of ways. Here’s out to get the most out of this data:
- Define your research question and intervention: Clearly outline what you’re trying to learn in the clinical trial and the intervention being evaluated (drug, therapy, etc.). This will guide your search for relevant RWD sources.
- Identify potential RWD sources: Explore databases containing electronic health records (EHRs), insurance claims data, or other sources that might hold information on your target patient population and the intervention of interest.
- Assess data completeness and quality: Evaluate the RWD source to ensure it captures the data elements needed for your study design. Look for completeness (are there missing values?), accuracy, and consistency in data collection.
- Simulate the trial using RWD: This is where it gets interesting. Utilize statistical methods to create a virtual cohort within the RWD that meets your planned trial’s eligibility criteria. Analyze this virtual cohort to estimate:
- Recruitment feasibility: Can you realistically recruit enough patients with the planned criteria and timeframe?
- Data collection feasibility: Is the necessary data readily available in the RWD source with minimal missing information?
- Sample size estimation: Based on the virtual cohort, can you achieve statistically significant results with a realistic sample size?
- Trial design optimization: Simulate different trial designs (e.g., varying clinic visit frequency) to identify the most efficient approach.
- Analyze potential biases: RWD can have inherent biases due to how data is collected and documented. Be aware of these limitations and consider how they might impact your feasibility assessment.
- Refine your clinical trial protocol: Based on the insights from the RWD analysis, refine your clinical trial protocol. You might need to adjust eligibility criteria, recruitment strategies, or data collection methods to improve feasibility.
In addition, researchers should collaborate with experts in RWD analysis and clinical trial design for a comprehensive feasibility assessment. Consider using existing tools and frameworks specifically designed for trial emulation with RWD. Remember, RWD is a valuable tool, but it shouldn’t replace traditional feasibility studies entirely. Combine RWD analysis with investigator site visits and discussions to get a well-rounded picture.
Leveraging real-world evidence (RWE) can unlock immense potential for enhancing clinical trial feasibility studies. By tapping into data sources like electronic health records and claims data, researchers can gain invaluable insights into patient populations and optimize study designs.