The Friends of Cancer Research Real‐World Data Collaboration Pilot 2.0: Methodological Recommendations from Oncology Case Studies

Abstract

The purpose of this study was to evaluate the potential collective opportunities and challenges of transforming real-world data (RWD) to real-world evidence for clinical effectiveness by focusing on aligning analytic definitions of oncology end points. Patients treated with a qualifying therapy for advanced non-small cell lung cancer in the frontline setting meeting broad eligibility criteria were included to reflect the real-world population. Although a trend toward improved outcomes in patients receiving PD-(L)1 therapy over standard chemotherapy was observed in RWD analyses, the magnitude and consistency of treatment effect was more heterogeneous than previously observed in controlled clinical trials. The study design and analysis process highlighted the identification of pertinent methodological issues and potential innovative approaches that could inform the development of high-quality RWD studies.The 21st Century Cures Act was enacted in 2016, to evaluate the health policy goals of trial design modernization and use of realworld evidence (RWE) creating numerous synergistic efforts. 1 These efforts aim to further understand ways in which RWE may complement or supplement randomized controlled trial data for regulatory purposes. [2][3][4][5][6] Evaluating applied examples of real-world data (RWD) and the generation of RWE are central to this effort. We build on the foundational work from the Duke-Margolis Center for Health Policy 2,3,6 and the National Academies of Science, Engineering, and Medicine (NASEM) 5 by highlighting

Real-world study of overall survival with palbociclib plus aromatase inhibitor in HR+/HER2− metastatic breast cancer

“…This approach to identify mortality and OS as an endpoint was validated within the Flatiron Database and is important in MBC research because OS estimates can be biased by low sensitivity in mortality surveillance 31 . If patients did not die, they were censored at the study cutoff date (September 30, 2020) 39 .…”

A novel analytical framework for risk stratification of real‐world data using machine learning: A small cell lung cancer study

“…The devised model development pipeline provided methodological solutions to address some of the highlighted challenges of using RWD. 2 , 12 , 13 , 14 The solution proposed to address RWD challenges and aspects that required further investigations are summarized in Table 3 .…”

“…Proper choice of methods can be the key for advancing methodologies aimed to extract relevant clinical information from RWD for risk stratification. 1 , 2 , 12 , 14 This will provide an opportunity to improve our understanding of disease, inform clinical study design, and develop individualized therapies 2 , 3 , 13 …”

“…Figure 1 highlights the importance of contextualizing the clinical processes behind the data 14 . This allowed to avoid a strategy to reduce the risk stratification that was biased by treatment decisions (such as including PCI as an input covariate).…”

“…10,11 However, RWD poses a series of practical challenges, including data quality, sample size, defining role of variables in clinical processes, addressing missing values and potential biases, and interpretation of results. 2,[12][13][14] Furthermore, censoring of data should be handled in risk stratification tasks. 15 Cox hazard ratios are widely used in oncology for the comparison of survival outcomes.…”