eal world data (RWD) reflect the experience of individuals through their routine interactions with the healthcare system (claims data, electronic health records), or through data collected in surveys, registries, and more recently from sensors (“wearables”). RWD contain information on diagnoses, procedures, inpatient and outpatient episodes, laboratory and imaging examinations, prescriptions, costs, physical activity level (using sensors), or quality of life (using registries or surveys).

RWD have been utilized in clinical and epidemiologic research in the US and Europe for several decades, particularly to assess product safety. However, new information technologies and exponential growth in the volume of medical information collected in digital format have enabled rapid expansion of RWD use worldwide. In Asia, universal health insurance databases with complete capture of healthcare events for almost the entire country or region have become available for research in Japan, South Korea, and Taiwan during the past 10 years. The Pharmaceuticals and Medical Devices Agency (PMDA) of Japan has developed the Medical Information Net-Work (MID-NET) database of 23 hospitals’ electronic medical records for a real-time assessment of drug safety since 2011. While RWD can provide information to answer a broad range of questions that characterize product performance in large populations and healthcare systems, the credibility of the research depends on using the right study designs and analytical tools to analyze these large and complex datasets efficiently.

Janssen and other pharmaceutical companies have established strong internal capabilities as well as collaborations with physicians, academia, and data owners to utilize RWD in order to provide population-based evidence about disease epidemiology and the association of treatments with outcomes. This information is used to guide operations and decision-making from drug discovery to drug development and post-authorization medical practice (Figure 1).

Figure 1. Practical uses of RWD during drug development and post-marketing.

Pre- and Post-Authorization Phase

In the pre-authorization phase, RWD are used to identify the patient population whose needs are not being met by current therapies, and to assess risk factors for disease progression or for poorer outcomes. This information can be used to identify targets for intervention during early phases of candidate drug selection. Drug utilization and physician prescribing studies can provide a comprehensive picture of treatment strategies that are being used in real world settings. These data allow evaluation of the appropriateness of drug therapy and can guide clinical trial design.

RWD can also be used to assess feasibility and to establish patient eligibility criteria in clinical trial design to define the target study population. It can show the impact of different inclusion and exclusion criteria on the potential pool of patients. Using information about incidence, prevalence, comorbidities, mortality patterns, outcomes, and prognostic risk factors generated from RWD, patient selection can be optimized to ensure rapid recruitment and the relevance of study results to routine practice. By understanding demographic characteristics, treatment pathways, and the natural history of disease in targeted patients in specific settings, RWD are used to help determine the study size, location, and length of follow-up needed to demonstrate an impact on disease outcomes, ensuring that clinical trials are efficient and have increased likelihood of being successful.

In the post-authorization phase, RWD are widely used by industry to detect or validate safety signals of medicines after they have been licensed. With RWD that cover very large populations over longer periods of time it is possible to measure risks of rare but potentially serious side effects that might not be identified in clinical trials, particularly if the trials focus on shorter-term outcomes or enroll a limited number of subjects. Using RWD in active safety surveillance (with statistical analyses that are appropriate for multiple comparisons) provides complementary evidence to that obtained through more costly, but less efficient, pharmacovigilance programs.

Broader Use of RWD

RWD play an important role in evaluating the effectiveness of a drug or vaccine in large and heterogeneous populations in ways that are not possible in randomized control trials. Studies of effectiveness can confirm clinical trial findings in real world settings. The evidence can be used to guide further investment in clinical studies to support approval for new indications. Population-based effectiveness data are also used by recommending agencies (e.g., healthcare technology assessment groups) to guide treatment and policy decisions. These data can also provide inputs for cost-effectiveness models.

This broader use of RWD outside of traditional post-marketing safety studies has become an important topic of discussion in the scientific and regulatory communities. While much research has been undertaken to clarify the strengths and limitations of observational research and various analytic designs, researchers continue to explore and assess a variety of analytic approaches, governance models, transparency initiatives, as well as study replications and distributed data network analyses in hopes of gaining agreement for a broader role for RWD in the scientific, clinical, and regulatory decision-making processes.

Conclusion

RWD are rich resources for answering complex medical questions, including epidemiology of diseases, treatment patterns, effectiveness and safety of specific treatments, and more. Access to large, highly granular datasets held within electronic healthcare databases can be used to improve the efficiency of drug development at each stage.