Real-world Data: Overview

Real-world data and Randomized Controlled Trials

Randomized controlled trials (RCT) are the gold standard used by researchers to explore and market new drugs and interventions. Nevertheless, establishing efficacy or if the treatment works in ideal conditions is not enough. With the increasing advancements in medicine and technology, patients, providers, and sponsors opt for robust information about the effectiveness of any new treatment or if it benefits people in real-world settings.

To answer the demands for real-world evidence, more and more health care providers and regulatory bodies have started to integrate real-world data (RWD) in research and practice. Real-world data can generate medical insights from additional sources, such as electronic health records, medical surveys, and administrative claims, instead of randomized controlled trials.

Real-world data: Definition

Real-world data is defined as data obtained from a heterogeneous population in real-world settings, with sources varying from administrative claims to health surveys. Although health care providers worldwide recognize the importance of real-world data, we should note that there’s no consensus about its definition. Interestingly, in a recent study, which included 53 documents and 20 interviews, 38 definitions of real-world data were identified (Makady et al., 2017). While most of the definitions categorized real-world data as information obtained from non-randomized controlled trials, many experts were unable to provide a clear institutional definition.

Therefore, to clarify the concept of real-world data, previous definitions given by major initiatives and healthcare bodies can be utilized and modified. According to the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), real-world data is “Data used for decision-making that are not collected in conventional RCTs.” The Association of the British Pharmaceutical Industry (ABPI) states, “For the purposes of this guidance, “RWD” will refer to data obtained by any non-interventional methodology that describe what is happening in normal clinical practice.” On the other hand, the official definition given by RAND goes, “RWD” is an umbrella term for different types of health care data that are not collected in conventional RCTs. RWD in the health care sector come from various sources and include patient data, data from clinicians, hospital data, data from payers, and social data.” Last but not least, the Innovative Medicines Initiative (IMI)-GetReal defines real-world data as “An umbrella term for data regarding the effects of health interventions (e.g., benefit, risk, and resource use) that are not collected in the context of conventional RCTs. Instead, RWD is collected both prospectively and retrospectively from observations of routine clinical practice. Data collected include, but are not limited to, clinical and economic outcomes, patient-reported outcomes, and health-related quality of life. RWD can be obtained from many sources including patient registries, electronic medical records, and observational studies.” (Makady et al., 2017).

Sources of Real-world Data

Just like with the definition of real-world data, there’s a wide range of additional and non-randomized control sources that can provide real-world evidence in research and practice. Note that real-world information can be collected retrospectively and prospectively, with electronic platforms facilitating data collection. Some of the major sources of real-world data include:

• Supplementary data: Although randomized controlled trials provide valuable health-related information, real-world data from patient-reported outcomes and claims can complement findings and provide insights into the long-term effectiveness of the new treatment. Real-world data can also help experts generate hypotheses, design studies, test research questions, and recruit subjects.
• Pragmatic trials: While randomized controlled trials limit their findings to controlled environments and populations, pragmatic trials or large simple trials aim to test the effectiveness of any novel treatment in real settings. Popular study designs include population enrichment randomized controlled trials, comprehensive cohort studies, non-randomized controlled trials, and cluster studies (“Sources of Real-world Data”). Note that these studies can provide valuable perspective information.
• Observational studies: Observational studies are also a powerful source of real-world evidence, including cohort studies, case reports, and case-control studies. Health-related observations may include various aspects, such as health outcomes, social factors, patient well-being, and quality of life.
• Health surveys: Health surveys are a powerful source of health care information which can provide valuable insights into a patient’s subjective experiences and well-being. Note that health surveys provide medical information about the population of interest (e.g., a wide range of patients), not just the participants in the clinical trial (Dang & Vallish, 2016). In addition, patient-powered networks, which are online platforms run by patients to collect data on a disease or medications, can also be used as a rich source of real-world evidence.
• Administrative data: Administrative data provided by pharmacies and health insurance companies are another legitimate source of real-world data, as well as medical research. A recent review proved that claims-based non-randomized and randomized controlled trials could complement each other by creating an overlap of information (Najafzadeh et al., 2017). To be more precise, randomized controlled trials may focus only on a particular variable and miss long-term outcomes (e.g., effects on mortality). Insurance claims data and billing information, on the other hand, can provide long-term data and follow-ups at a lower cost. Note that claims provide retrospective or real-time data, particularly for reimbursement and economic outcomes. In addition, databases that provide health information outside of the trials (e.g., Medicare) can be used to deal with missing data and drop-outs.
• Electronic health records and patient registries: Electronic health records and other electronic databases can be used to provide information about laboratory tests and standard medical services related to health care treatments and interventions. Note that electronic records reduce costs and improve interoperability. Patient registries that contain information on a group of patients can also provide valuable data and benefit observations. Note that registries are prospective, with disease-based registries being the most powerful source of high-quality data (e.g., the Global Registry of Acute  Coronary  Events) (Dang & Vallish, 2016).
• Social media: With the increasing role of technology in health care settings, social media data is also beneficial. It can provide valuable information on additional factors that may affect a patient’s treatment, non-compliance, and emotional well-being (“Sources of Real-world Data”). Note that social media channels can improve doctor-patient communication, recruitment, and drug marketing.

Real-World Data: Application and Benefits

Real-world data has numerous applications in research and practice. Real-life data can give valuable information about the effectiveness of treatment in real-world settings, and across diverse populations. Note that real-world data can be used as primary data from interventions or as secondary data from patient-reported outcomes and administrative sources. Interestingly, a recent review conducted by the IMI-GetReal revealed that usually, real-world data is utilized to provide insights before and after the market authorization of a new drug; assess the pharmacoeconomic properties of treatment; and explore the effectiveness in conditional reimbursement schemes (Makady et al., 2017). Note that Makady and colleagues evaluated the existing policies of  six European agencies, as follows: “the Dental and Pharmaceutical Benefits Agency (Sweden), the National Institute for Health and Care Excellence (United Kingdom), the Institute for Quality and Efficiency in Health-care (Germany), the High Authority for Health (France), the Italian Medicines Agency (Italy), and the National Healthcare Institute (The Netherlands)” (Makady et al., 2017). Some of the major applications of real-world data include:

• Drug development: Drug development is a complex process, in which safety and efficacy come first. To support research and routine clinical practices, real-world data can be employed in drug development. Real-world data can be used to examine factors, such as the nature of a disease, existing clinical practices, and medical costs.
• Pre- and after-market authorization and decision-making: Randomized controlled trials, especially Phase III trials, are essential to provide information about the safety and efficacy of a new intervention. Nevertheless, real-world data has become a powerful source of information in research. Real-world data can be used to assess the generalizability of scientific results and real-world safety. As a result, more and more regulatory bodies, health technology assessment organizations, and patients are opting for robust real-world data to complement clinical findings.
• Pharmacoeconomic analyses: Since clinical trials can be lengthy and costly, real-world data can be used to provide robust information on the efficacy of a new drug and its pharmacoeconomic properties. Data can also be used to assess effectiveness across heterogeneous populations and between different products on the market.
• Reimbursement schemes: Since medical costs are increasing, patients, payers, and insurance companies are looking for reliable real-world data about the benefits of any novel treatment, including long-term effects and real-world safety. Real-world information can also provide valuable insights into the financial side of new interventions and healthcare services, which often obstructs health care practice.

Real-World Data: Application and Benefits

Real-world data has numerous applications in research and practice. Real-life data can give valuable information about the effectiveness of treatment in real-world settings, and across diverse populations. Note that real-world data can be used as primary data from interventions or as secondary data from patient-reported outcomes and administrative sources. Interestingly, a recent review conducted by the IMI-GetReal revealed that usually, real-world data is utilized to provide insights before and after the market authorization of a new drug; assess the pharmacoeconomic properties of treatment; and explore the effectiveness in conditional reimbursement schemes (Makady et al., 2017). Note that Makady and colleagues evaluated the existing policies of  six European agencies, as follows: “the Dental and Pharmaceutical Benefits Agency (Sweden), the National Institute for Health and Care Excellence (United Kingdom), the Institute for Quality and Efficiency in Health-care (Germany), the High Authority for Health (France), the Italian Medicines Agency (Italy), and the National Healthcare Institute (The Netherlands)” (Makady et al., 2017). Some of the major applications of real-world data include:

• Drug development: Drug development is a complex process, in which safety and efficacy come first. To support research and routine clinical practices, real-world data can be employed in drug development. Real-world data can be used to examine factors, such as the nature of a disease, existing clinical practices, and medical costs.
• Pre- and after-market authorization and decision-making: Randomized controlled trials, especially Phase III trials, are essential to provide information about the safety and efficacy of a new intervention. Nevertheless, real-world data has become a powerful source of information in research. Real-world data can be used to assess the generalizability of scientific results and real-world safety. As a result, more and more regulatory bodies, health technology assessment organizations, and patients are opting for robust real-world data to complement clinical findings.
• Pharmacoeconomic analyses: Since clinical trials can be lengthy and costly, real-world data can be used to provide robust information on the efficacy of a new drug and its pharmacoeconomic properties. Data can also be used to assess effectiveness across heterogeneous populations and between different products on the market.
• Reimbursement schemes: Since medical costs are increasing, patients, payers, and insurance companies are looking for reliable real-world data about the benefits of any novel treatment, including long-term effects and real-world safety. Real-world information can also provide valuable insights into the financial side of new interventions and healthcare services, which often obstructs healthcare practice.

Real-world Data: Conclusion

Medical research and drug development can challenge standard healthcare practices. Although randomized controlled trials are the gold standard in research, real-world evidence becomes crucial to establish the effectiveness of a new intervention. To meet the need for real-world evidence, experts, payers, and patients must integrate real-world data (RWD) in practice.

To sum up, real-world data is vital in health care. As explained above, real-world data can be obtained from various sources, such as patient registries, administrative claims, and social media channels, in order to assess heterogeneous groups and settings. Findings can be applied to different real environments in order to provide insights into drug safety, in health and financial terms, and its long-term effects. Real-world data can improve decision-making, pre-authorization, and reimbursement of new drugs and treatments and benefit medical research and patient outcomes. Therefore, regulatory agencies and experts must reach a consensus and embrace real-world data in practice. In the end, patient well-being is the main focus of digital health research.

References

Dang, A., & Vallish, B. (2016). Real-world evidence: An Indian perspective. Perspectives in Clinical Research.

Makady, A., Ham, R., de Boer, A., Hillege, H., Klungel, O., Goettsch, W., on behalf of GetReal Workpackage (2017). Policies for Use of Real-World Data in Health Technology Assessment (HTA): A Comparative Study of Six HTA Agencies. Value in Health: The journal of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), 20 (4).

Makady, A., de Boer, A., Hillege, H., Klungel, O., Goettsch, W., on behalf of GetReal Work Package (2017). What Is Real-World Data? A Review of Definitions Based on Literature and Stakeholder Interviews. Value in Health: The journal of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), 20 (7).

Najafzadeh, M., Gagne, J., & Schneeweiss, S. (2017). Synergies from Integrating Randomized Controlled Trials and Real-World Data Analyses. Clinical Pharmacology and Therapeutics.

Singal, A., Higgins, P., & Waljee, A. (2014). A Primer on Effectiveness and Efficacy Trials. Clinical and Translational Gastroenterology, 5. Retrieved from https://www.nature.com/articles/ctg201313

Sources of Real-World Data. Retrieved from https://rwe-navigator.eu/use-real-world-evidence/sources-of-real-world-data/