Real-World Evidence

Von Dr. Korbinian Weigl

Real-World Evidence (RWE) is clinical evidence regarding the use, potential benefits, and/or risks of a medical therapy. It is derived from the analysis of real-world data (RWD).

RWD are routinely collected data relating to patient health status or the delivery of health care from a variety of sources other than traditional clinical trials (e.g. claims databases, hospital data, electronic health records, product and disease registries, health data, data gathered from other sources such as mobile devices and wearables, etc.).

Another related term, real-world insights (RWI), refers to the insights generated by leveraging RWE, which is used by different stakeholders from the healthcare industry to inform internal research and business-related decisions.

As mentioned above, sources of RWD comprise data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources, such as electronic health records (EHRs), claims and billing activities, product and disease registries, patient-generated data from inhome settings, and data from other sources, such as mobile devices.

Healthcare databases are systems used by healthcare practitioners to record routine clinical and laboratory data during their day-to-day practice. They are probably the most significant sources of RWD. Healthcare databases broadly represent the actual clinical practice, and their analysis can enable quick and systematic evidence synthesis about efficacy and safety of drugs, quality of life and other patient-reported outcomes, and the natural history of disease.

Claims databases include billing and other healthcare administrative data entered by pharmacies, or health insurers. Various stakeholders like health researchers, insurance companies, and health authorities use information from these databases to assess the long-term impact and effectiveness of health interventions in the ‘real world’. Generally, claims databases consist of data on inpatient, outpatient, emergency room, pharmacy services, and include data on the services received by the patient during clinical visits, surgeries, diagnostics, laboratory tests, hospitalisation and length of stay, and pharmacy filing.

Registries are organised systems that collect, analyse, and publish observational data on a patient population with specific characteristics in a prospective manner. Registry data are usually collected in the form of cohort studies with a predetermined clinical or public health-related purpose. Registries have evolved from paper-based patient records to electronic databases and often contain large amounts of data, encompassing a variety of information, such as clinical information or biological samples stored in bio-banks. Registries enrol a much larger and more diverse patient sample than an RCT, and can also be a source of recruitment of patients for RCTs.

Registries can be either disease (or condition) registries, focusing on populations with a particular disease or diseases, or product registries, focusing on populations using specific products, i.e., treatments or devices. Registries can be hospital based (that collect information from patients with a specific disease diagnosed and treated at a single hospital or a group of hospitals), or population-based (that collect information from all people living within a specific geographic region).

It should be noted that the rapid increase in the use of technology, such as electronic systems, biosensors, mobile and wearable devices in healthcare, has led to the accumulation of large amounts of RWD.

Important to remember when dealing with RWD sources: they all come with various strengths and weaknesses. In order to fully understand the data and to generate insightful RWE, the underlying biases of each RWD source needs to be ascertained and addressed. If RWD is focused on one purpose (e.g. claims data), their selection and the associated processes are aligned exactly for that goal, which might result in an inherent selection bias that needs to be addressed when analysing the data.

In order to generate RWE out of RWD, different types of experimental and observational study designs can be carried out. The different types of RWE studies are non-interventional (i.e., observational) studies, registry analysis, claims database analysis, patient surveys, and abstraction and analysis. Observational studies can be conducted in the form of cohort studies, cross-sectional studies, or case–control studies. Data collection for RWE studies can be done prospectively or retrospectively.

One common factor in all RWE studies is that the treatment is prescribed as per marketing authorisation, physician discretion, and national or regional treatment guidelines, and not as per a pre-specified protocol as in the case of RCTs. Often, some prospective, multicentre, observational studies are conducted as part of routine clinical practice, and such trials are sometimes called pragmatic clinical trials.

There are currently three different pathways through which European Medicines Agency (EMA) can generate RWE: The Data Analysis and Real World Interrogation Network (DARWIN), studies using in-house electronic health databases, and studies procured through the EMA framework contracts. DARWIN was established in 2022 as a pan-European federated network to deliver RWE from across Europe. Sources include hospitals, primary care, health insurance, registries and biobanks. Studies using in-house electronic health databases are based on the various databases that are established within EMA and which consist of primary care health records from different countries in Europe. Those databases are planned to be extended to specialised settings and are thought to provide quick turnaround answers to simple questions or support more complex studies. Studies procured through the EMA frameworks contracts are based on up to eight research organisations and academic institutions than can be asked to perform studies on behalf of EMA. As of February 2024, DARWIN EU has entered full operation mode and has become EMA’s primary RWE generation pathway.

Overall, an EMA review states that the current EMA RWE framework with its three evidence generation pathways is able to address a broad range of research questions and help support decision-making in a variety of regulatory contexts and procedures. In 2022, this was particularly true for research topics concerning conditions and medicines used in the primary care setting, which constituted half of all queries received. In order to be able to also cover other settings (secondary and tertiary care, rare diseases, etc), wider access to additional, more diverse and complementary data sources, including hospital, claims and registry data, is needed. Similarly, data sources from additional European countries would be desirable for broader geographical representativeness.

In a reflection paper by EMA several examples where RWD has supported regulatory assessment are given: characterization of disease epidemiology; understand clinical context and unmed medical need; patterns of drug utilization; support the feasibility assessment and the planning of non-interventional post-authorisation safety, effectiveness and drug utilisation studies by measuring outcome incidence, treatment exposure, the duration of available follow-up and the impact of applying different eligibility criteria on sample size; compare patient characteristics of a study population to those of the clinical practice population in a real-world setting; and perform post-marketing monitoring, investigate safety concerns and effectiveness, and evaluate the effectiveness of risk minimisation measures.

Now we know what RWE is, where it comes from. The question remains: why do we need it? Market authorization of medicinal products still heavily rely on evidence from RCTs, so why even bother with RWE?

Researchers are increasingly realising the importance of RWE to generate valuable insights into the efficacy, safety, and the pattern of usage of drugs and medical products. RWE studies have been used to explore different aspects in health and disease, such as epidemiology, disease burden, treatment patterns, safety, treatment outcomes, long-term outcomes, and patient-reported outcomes such as satisfaction, quality of life, medication adherence, and patient experience. RWD have been in use in research for post-marketing surveillance, as well as to monitor disease progression through natural disease history studies. RWD can be used to achieve better informed and more efficient regulatory decision-making as a complement to existing evidence.

RWE is currently used throughout the healthcare system. Life science researchers long for additional insights into the broader impact of a medication’s use in routine clinical care, including the safety and efficacy of novel therapies. The use of RWE can help regulators monitor post-market safety and adverse events, and even help authorities in decision-making. Health plans and payers benefit from RWE as it offers – among others – possibilities to tie payments for treatment to both short and long-term effectiveness of pharmaceuticals and other therapies. Healthcare providers can make use of clinical guidelines derived from RWE, and physicians are given another tool to complement their understanding of a certain disease, as RWE adds evidence to existing RCTs.

A standard RCT enrolls a small segment of the disease population and tests therapies in a controlled environment. RCTs are typically costly and take a considerable amount of time to complete. It has been well known that RCTs by themselves cannot give a complete picture of safety of any medical product, and adverse effects that are not reported in RCTs are often encountered in routine clinical practice. In contrast, RWE generation is more cost effective and can happen more quickly than standard RCTs. However, RWD is often vast and unstructured compared to data collected during RCTs.

While RCTs still remain the gold standard for assessing safety and efficacy of drugs and medical products and the evidence from RCT represents the outcome of a ‘standardised’ intervention used in an ‘idealised’ setting, RWE represents the outcome of ‘variable’ treatment patterns in the ‘real world’. Thus, RWE contrasts strongly with evidence generated from RCTs while at the same time complements evidence from RCTs to get the ‘full picture’.

There are approaches to emulate so-called target trials with RWD. They intend to tackle the problem of missing randomisation, while at the same time deal with biases which are inherent to observational data. However, these target trial emulation come with caveats and methodological challenges, and several conditions must be met in order for RWD to yield valid causal inferences.

Advantages of RWE include: no strict eligibility criteria and thus fewer chances of exclusions based on concomitant medications and comborbidities, quick and cost-effective as less time is required for patient recruitment/enrolment, possibility to undertake research that cannot be done with RCT (high-risk groups, i.e. pregnant women and children), ability to track real-world patient behavior, large sample size facilitates sub-population analyses, less common effects, generalisability and modelling.

Disadvantags of RWE can comprise its mostly unstructured nature. Futhermore, applying the correct methods to harness valid and reliable RWE out of RWD remains a challenge which requires knowledge about RWD sources, epidemiological and statistical methods and in-depth information about the underlying health systems.

(European Medicines Agency (EMA) 2024)

(Veradigm 2025)

(Dang 2023, S. 26)

(European Medicines Agency (EMA) 2025)

(Braitmaier und Didelez 2022)

Literaturverzeichnis

Braitmaier, Malte; Didelez, Vanessa (2022): Emulierung von „target trials“ mit Real-world-Daten. In: Prävention und Gesundheitsförderung.
DOI: 10.1007/s11553-022-00967-9.

Dang, Amit (2023): Real-World Evidence: A Primer. In: Pharmaceutical medicine 37 (1), S. 25–36.
DOI: 10.1007/s40290-022-00456-6.

European Medicines Agency (EMA) (2024): Real-world evidence provided by EMA. Online verfügbar unter https://www.ema.europa.eu/en/documents/other/guide-real-world-evidence-provided-ema-support-regulatory-decision-making_en.pdf, zuletzt geprüft am 06.02.2025.

European Medicines Agency (EMA) (2025): Reflection paper on use of real-world data in non-interventional studies to generate real-world evidence for regulatory purposes. Online verfügbar unter https://www.ema.europa.eu/en/documents/other/reflection-paper-use-real-world-data-non-interventional-studies-generate-real-world-evidence-regulatory-purposes_en.pdf, zuletzt geprüft am 07.04.2025.

Veradigm (2025): Real-World Evidence in Healthcare: A Complete Guide. Online verfügbar unter https://veradigm.com/real-world-evidence/, zuletzt aktualisiert am 05.02.2025, zuletzt geprüft am 06.02.2025.

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