Real-world evidence research based on big data

Abstract

BackgroundIn recent years there has been an increasing, partially also critical interest in understanding the potential benefits of generating real-world evidence (RWE) in medicine.ObjectivesThe benefits and limitations of RWE in the context of randomized controlled trials (RCTs) are described along with a view on how they may complement each other as partners in the generation of evidence for clinical oncology. Moreover, challenges and success factors in building an effective RWE network of cooperating cancer centers are analyzed and discussed.Material and methodsThis article is based on a selective literature search (predominantly 2015–2017) combined with our practical experience to date in establishing European oncology RWE networks.ResultsRWE studies can be highly valuable and complementary to RCTs due to their high external validity. If cancer centers successfully address the various challenges in the establishment of an effective RWE study network and in the consequent execution of studies, they may efficiently generate high-quality research findings on treatment effectiveness and safety. Concerns pertaining to data privacy are of utmost importance and discussed accordingly. Securing data completeness, accuracy, and a common data structure on routinely collected disease and treatment-related data of patients with cancer is a challenging task that requires high engagement of all participants in the process.ConclusionBased on the discussed prerequisites, the analysis of comprehensive and complex real-world data in the context of a RWE study network represents an important and promising complementary partner to RCTs. This enables research into the general quality of cancer care and can permit comparative effectiveness studies across partner centers. Moreover, it will provide insights into a broader optimization of cancer care, refined therapeutic strategies for patient subgroups as well as avenues for further research in oncology.

Trial designs using real‐world data: The changing landscape of the regulatory approval process

“…55 While most traditional data processing and analysis tools applicable to RWD can be used to identify and reduce the impact of some of the limitations inherent in real-world studies, such as potential confounding factors, real-world datasets require development of new methodologies or applications to unlock their full potential. 9,11,97 The growth of machine learning and the application of natural language processing to RWD will bring additional opportunities in the future, enabling even more meaningful outcomes to be elucidated from the wealth of available RWD. 9 Machine learning techniques have already been used to enhance the prediction accuracy of algorithms to identify women with early and advanced stage breast cancer in a claims database using predictive modeling.…”

“…[6][7][8][9] The definitions of RWD and RWE are relatively consistent between key regulatory agencies (see Table 1). 7,8,10 While RWE from observational studies is well accepted for post-approval safety monitoring and to answer pharmacoeconomic questions 3,11,12 its contribution to regulatory decisions around effectiveness has been more limited.…”

Real-world patient-reported outcomes of women receiving initial endocrine-based therapy for HR+/HER2− advanced breast cancer in five European countries

“…A strength of our study was that we assessed real-world patient outcomes of patients with advanced breast cancer (i.e., women with HR+/HER2− advanced breast cancer receiving an initial ET-based regimen for advanced disease) across five European countries using a variety of well-validated standard PRO instruments, including the frequently used EORTC QLQ-C30. Real-world data complement clinical trial data to help inform patient care [ 50 ]. PRO measures are increasingly being used in clinical trials and are broadly applicable in both clinical research and daily clinical practice; they can be used to support decision making by regulators, payers, healthcare providers, and patients [ 51 ].…”

Lower-dose prescriptions in the post-marketing situation and the influencing factors thereon

“…We believe that the Real-World Data (RWD) utilized more in recent years would help resolve the deviation from the approved dose and lead to a prompt delivery of information on the optimized dose for each pharmaceutical product. Real-world evidence accumulates clinical evidence based on the actual use of drugs after marketing and will therefore, compensate for the limited information obtained before approval [18,19]. We can utilize RWD to identify patient backgrounds with lower-dose prescriptions and plan post-marketing clinical trials to clarify benefit/risk balance using lower dose for those specific populations, if necessary.…”