Can you describe real-world evidence in 10 words?

Actionable insights derived from the analysis of real-world data.

 Is RWE a new era for oncology/haematology research?

Yes, if we consider the suitability of real-world evidence (RWE) in supporting regulatory applications. Although the contribution of what is now called RWE has long been recognised for safety monitoring and disease epidemiology across drugs’ lifecycles, their use to demonstrate effectiveness is more nascent and promising.

How is RWE currently perceived by the oncology/haematology community?

RWE is still perceived as poor quality, unreliable, and irrelevant for clinical decision-making. For comparative effectiveness studies or causal objectives, many biases compromise real-world data (RWD) interpretation, such as patient selection, unmeasured confounders, handling of missing data, uncertainties regarding data transformation, and lack of transparency in statistical analysis plans. However, as healthcare systems and regulators pay close attention to RWE, the community will learn how to interpret the evidence coming from the real world.

Why is having real-world data important?

There are questions that clinical trials will not be able to answer, and only RWE can give valuable insights. RWD can be used to better understand natural history of disease, current practice trends, (long-term) post-marketing safety and effectiveness of drugs, impact of different treatment patterns on patients’ outcomes, the benefit of different interventions in populations under-represented in clinical trials, unmet medical needs or gaps in cancer care.

 What are the main challenges of conducting real-world studies? [and how can they be overcome?]

We still lack a clear definition of “fit-for-purpose” RWD and how to assess data quality, relevance and reliability. To conduct real-world studies, instead of an “army of data entries filling case report forms (CRFs)”, we need a robust clinical informatics platform, systems’ interoperability for patient-level linkage to external sources, standardised data collection/extraction/processing/cleaning, formats and terminology. Many societies and regulators are publishing best practices to perform “fit-for-purpose” RWD, the first step to overcome these challenges.

Do you have a living example of how RWE has impacted patient care?

Yes, RWD has been used as external or synthetic control of non-randomised single arm trials in precision oncology, such as trastuzumab plus pertuzumab in HER2 overexpressed advanced colorectal cancer, pembrolizumab as tumor-agnostic biomarker in patients with treatment refractory MSI high malignancies. RWE had a role in identifying subpopulations outside the drug’s original label indication in whom the drug demonstrates exceptional effectiveness, leading to a label extension. For example, palbociclib plus endocrine therapy in male patients with hormone receptor positive HER2 negative metastatic breast cancer.

What aspect of RWE research excites you the most?

RWE is using imperfect data to learn from perfect patients (real patients that differ from those enrolled in clinical trials). What excites me is the possibility to make imperfect data more and more perfect using technology (such as large language models) and proper analytical methods for trial emulation. This field is emerging, and oncologists need to be educated about the research opportunities ahead.

How important is it to have a framework for collating and assessing RWE?

We critically need data standards on what constitutes “high-quality RWD” for research and regulatory applications. Another essential step involves optimising the data collection infrastructure, enabling efficient and human-resource-friendly data extraction through digital solutions. For example, CRFs incorporated in electronic medical records to streamline the collection process, and mobile applications for routine patient reported outcomes assessment. Also, we need to establish secure data linkage with external sources to enrich RWD without compromising patient privacy. Finally, we need to define best practices for study design and statistical methods. In federated learning used in multi-centre cohorts, the code is moved to a RWD repository that follows a rigorous data model, enabling the data to stay within its institutional environment instead of being transferred to another institution for analysis.

Cancer researchers tend to be busy people! Why should they attend our RESEARCH REWRITTEN conference?

To discover the utility of RWD in clinical or translational research, to familiarise with methods of mitigating biases of imperfect databases, to improve confidence and clinical acceptance of RWE, and to understand how health systems can help optimise cancer care.

 Register here to attend RESEARCH REWRITTEN [at no direct cost]. Hurry, places are limited and are filling up fast!