Real-world evidence (RWE) research is gaining significant importance in biopharmaceutical product development as well as its commercialization. The increasing need of the industry to seek broader information about the safety and effectiveness in the real-world setting, which typically impacts the ensuing reimbursement and utilization of new products, is determined by regulators, public and private payers, and prescribers – in order to understand the impact of a new product in a such a setting. As a result, RWE is now included earlier in the research and development phase. (1)

Real-world evidence or real world data (RWD) is nothing but the information collected under normal day-to-day circumstances that is found outside of a randomized clinical trial. This data is considered to be RWE when it is looked at from and analysed within the context of what is being measured. Ultimately, RWE can be used to evaluate treatment effectiveness in daily settings to guide clinical decision-making and answer scientific questions. (2)

Today, healthcare industry is rapidly expanding with varied data sources, such as electronic health records (EHRs), insurance claims data, patient registries, surveys, medical devices, imaging, genomics, etc. that capture enormous amounts of patient health and medical information. These data reflect patient’s routine valuable health information in the context of real clinical practice. This evidence from real-world setting can be used to study different aspects, such as epidemiology and burden of a disease, co-morbidities, treatment patterns, adherence, and outcomes of different treatments. Therefore, RWE can be used to design clinical studies, inform hypotheses, thereby improving the probability of approval and successful treatment take-off. These applications not only facilitate compressed clinical trial timelines and consequent cost-savings, but they can also serve as a powerful complement to evidence gathered from randomized control trials (RCTs), which is a gold standard for assessing biopharmaceutical drug safety and efficacy among researchers. (3)

Real-world evidence can significantly impact clinical study design. Here’s how: An effective trial design normally begins with creating a hypothesis and defining the patient cohort. This process requires the most extensive research and analysis, which is why it is a lengthy and iterative one, requiring many sequences of refinement. Real-world evidence can be optimized to test the hypotheses across diverse datasets quickly. With RWD, which consists of multiple and expansive datasets, specific insights to indication and severity of interest (e.g. rheumatoid arthritis, stage 4 chronic kidney disease, lymphoma, etc.) can be achieved in order to recognize clinical phenotypes, outcomes, unmet needs, and much more. Real-world evidence can provide answers pertaining to clinical gaps and the consequent findings can then be used to strengthen a hypothesis and further to design and tune the RCTs to all possible unmet needs. (3)

This is because, conventional primary tools used to generate a hypothesis are limited to 1) existing published literature from previous studies conducted in the patient population of interest or 2) expensive and lengthy primary chart studies. These methods fail to provide extensive information, particularly regarding rare diseases and less common disease subsets. Advances in RWE technologies, therefore, in these instances, can provide researchers the ability to quickly test hypotheses to assess clinical relevance along with care and treatment pathways of the desired cohort. (3)

In case of regulatory applications of RWE, various large data initiatives are developing standardized and consistent concepts across multiple data types and sources, in addition to bringing forward the importance of RWE in addressing concerns, such as medical product safety, patient-centred outcomes (PROs), and the value of new technologies and care delivery programs. There are many ways in which RWE can be harnessed to improve regulatory decision-making, such as to support the change of label change for new dosing administration or facilitate post-marketing safety surveillance. Furthermore, some applications can also apply RWE in historical controls or the progress and regulatory appraisal of products intended to treat rare disease populations. These examples have been fairly well characterised and followed by US FDA and industry stakeholders in recent years. (4)

In our view, RWE studies are useful complements to RCTs, since they reproduce the routine utility of drugs, devices and other products, providing a more wide-ranging view of patient response to medications, improved assessment of disease patterns, additional information on safety as well as economic analyses. Moreover, since RWE provides the actual care that patients receive in clinics, which is not limited by a strict inclusion and exclusion criteria; it generates long term efficacy and safety data as well as economic assessment – all in a real-world setting. Additionally, it allows for a comparison between multiple interventions. (5) It can also facilitate well-informed healthcare and policy decisions. Healthcare industry, therefore, needs to keep up with new developments in RWE, data sources, analytical techniques, and study methodologies to ensure competitiveness as well as maximum ROI on new products. (2)

Become an Certified HEOR Professional – Enrol yourself here!


  1. Cziraky M, Pollock M. Real world evidence studies. October, 2015. 
  2. Jadhav S. Using real world data to enhance clinical trials. January, 2017.
  3. Ahmed R, Rusli E. Using Real-World Evidence to Optimize Clinical Trials- Improving Trial Design, Patient Recruitment, and Data Analysis. Shyft Analytics. 
  4. Incorporating Real-World Evidence in Regulatory Decision-Making: A Pragmatic Approach to Randomization in the Clinical Setting. Margolis Center for Health Policy- Duke University. 
  5. Mahajan R. Real world data: Additional source for making clinical decisions. International Journal of Applied and Basic Medical Research 2015; 5(2):82.

Related Posts