Real-world data (RWD) and real-world evidence (RWE) are transforming the clinical research landscape. During our recent webinar, Phastar’s experts explored the growing impact of RWD and RWE, highlighting their use in clinical trials, regulatory decision-making, and real-world applications. The session included presentations from Phastar’s expert statisticians and data scientists, offering practical insights into how these data sources drive innovation in clinical research as well as a recent case study from our work with a Top 10 global pharma company.
Below, Matthew Thompson, Associate Director, Programming at Phastar describes the growing number of RWE studies registered on ClinicalTrials.gov from 2005 to 2022, underscoring the increasing demand for RWE. Over the past six to seven years, this trend has gained momentum as healthcare stakeholders recognize the value of RWE in understanding drug efficacy and safety beyond traditional randomized controlled trials (RCTs).
Defining RWD and RWE
Phastar Principal Biostatistician, Li Huang, introduced key definitions of RWD and RWE, as outlined by regulatory bodies like the FDA and EMA:
- Real-World Data (RWD): Data relating to patient health status and healthcare delivery, collected from sources like electronic health records (EHRs), claims datasets, and patient-generated health apps.
- Real-World Evidence (RWE): Clinical evidence derived from the analysis of RWD to inform healthcare decisions, including safety, effectiveness, and patient outcomes.
Li highlighted how RWE can complement RCTs, particularly when RCTs are not feasible. Observational studies leveraging RWD can provide valuable insights into real-world treatment outcomes and support hypothesis generation, trial feasibility assessments, and regulatory decision-making.
The FDA and EMA have established frameworks to integrate RWE into regulatory decision-making. The FDA’s 2023 guidance emphasizes the use of RWD in clinical studies to support safety and efficacy evaluations, particularly for non-interventional designs. [1]
From Data to Evidence: Practical Considerations
Senior Statistician at Phastar, Andrew Elders, then provided a practical perspective on converting RWD into actionable RWE. He emphasized that research questions should address evidence gaps rather than be driven by available data. Common objectives include:
- Population Characteristics: Enhancing knowledge about specific patient groups or subgroups.
- Treatment Effectiveness: Evaluating outcomes for different therapeutic approaches based on real-world use of medications and treatment patterns.
- Safety Profiles: Identifying risks or long-term complications associated with treatments.
- Disease Characteristics: Describing the natural history of diseases and developing understanding of biomarkers in relation to real-world treatments.
Data Quality and Transformation
Ensuring data quality is paramount in real-world evidence studies. Andrew highlighted the key characteristics that the data must have:
- Reliable and credible: Accurately representing intended medical concepts while being complete and trustworthy.
- Relevant: Addressing a specific research question or evidence gap.
- Representative: Reflecting the real-world population or practice settings.
- Well-managed: Proper aggregation and transformation of data, especially when integrating multiple sources, is crucial to maintain consistency and avoid errors during analysis.
Andrew highlighted the complexities of working with real-world data (RWD), emphasizing that while it is inherently messy, Phastar has extensive cross-functional expertise to successfully manage and utilize it.
Effective handling of RWD often begins with robust data management and cleaning. Andrew also discussed the importance of thoughtful study design for primary data collection in real-world settings, including the development of case report forms (CRFs) tailored to answer specific research questions. Andrew elaborated on Phastar’s programming capabilities, including data engineering, algorithm development, and deriving unavailable parameters.
He outlined common challenges in working with RWD, such as unstructured formats, illogical dates, and treatment gaps. Missing data and various biases—such as selection bias or immortal time bias—were also highlighted as critical issues. Andrew explained that these challenges can often be mitigated through careful study design, alongside the application of statistical methods to address missing data and biases.
Alice Wang, Associate Director of Data Science at Phastar, discussed our ongoing partnership with a Top 10 Global pharma company, presented at the RSS 2024 conference. The study aimed to identify factors influencing treatment decisions in patients with resectable non-small cell lung cancer and assess the alignment of automated machine learning models with traditional logistic regression outcomes. Below Alice discusses the main objectives of the study, watch the full webinar for a detailed explanation.
Conclusion
RWD provides invaluable insights into treatment patterns, patient outcomes, and prescribing behaviors in everyday healthcare settings, offering a deeper understanding that extends beyond the scope of traditional clinical trials. While working with RWD can be complex, addressing challenges such as data quality and bias ensures that its full potential can be realized.
Phastar’s cross-functional expertise and robust methodologies can help you effectively harness RWD and RWE to support evidence-based decision-making, enhance trial design, and generate impactful insights. Let us help you navigate the complexities and unlock the possibilities that RWD offers for clinical research.
References
[ 1 ] U.S. Food and Drug Administration. Considerations for the Use of Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products. Published August 2023. Available at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/considerations-use-real-world-data-and-real-world-evidence-support-regulatory-decision-making-drug.
