Understand how this type of data can be usefu

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What is Real-World Data?

Real-World Data (RWD) refers to information about patient health and behavior that is collected outside of controlled research environments, such as randomized clinical trials (RCTs). Unlike data generated in experimental settings, where researchers control variables to assess a specific treatment or condition, real-world data reflects the experience of patients in everyday scenarios, where many variable factors can influence outcomes. Evidence obtained through this data is referred to as Real-World Evidence (RWE).

Real-world data can be qualitative or quantitative and can come from various sources, including: electronic health records, clinical laboratories and lab tests, health insurance utilization, pharmacy purchases, complaint records, health audit data, data from sensors and wearable devices, patient-reported quality of life data, caregiver reports, images, text, social media posts, among others.

Real-World Data and Clinical Research

Real-world studies (such as case-control, prospective cohort, and retrospective cohort) are increasingly seen as a tool to complement information from clinical trials in contexts where these are limited, such as generalizing to broader and more heterogeneous populations, long-term evaluations, and rare diseases or adverse events.

The use of RWD and RWE is also becoming more common in drug approval processes or in expanding indications for new patient groups. This is especially relevant in situations where conducting a traditional clinical trial is not feasible due to ethical, logistical, or specific health condition concerns. These data help fill gaps when conventional research is not viable, providing a valuable way to obtain evidence to support decisions about treatments and therapies.

Real-world studies can provide important insights, such as:

• Evaluation of treatment efficacy and safety in everyday settings, where conditions can vary significantly compared to traditional clinical trials.

• Identification of treatment patterns and evaluation of how drugs or therapies are used in clinical practice.

• Long-term outcome studies, as RWD can provide continuous data on patient progress.

• Impact analysis of treatments on broader populations, such as patients with comorbidities or other characteristics that may be excluded from conventional clinical trials.

• Assistance in the development of new therapies, improving understanding of conditions and the most effective treatments for specific patient groups.

Advantages and Challenges

Real-world studies offer several advantages, including:

• Patient data is often readily available, making it easier to obtain information that reflects the characteristics of the studied population.

• Time and resource savings compared to randomized clinical trials, which are more complex in terms of methodology, structure, and logistics.

• Facilitation of analysis for rare or late-onset diseases and events, which would not be possible to study in trials with limited duration.

• Useful for post-marketing surveillance to provide ongoing monitoring of the safety and long-term efficacy of drugs or devices.

However, there are challenges, especially when using these studies for regulatory purposes, such as:

• Quality and consistency: Since the data comes from diverse sources, there may be inconsistencies and a lack of standardization in data collection.

• Privacy and security: The collection of sensitive patient data requires strict management to ensure information is protected.

• Interpretation and bias: Unlike randomized studies, real-world data can be influenced by a variety of uncontrolled factors, making precise analysis challenging.

When working with real-world studies and data, it is essential to maintain realistic expectations about the advantages and disadvantages of this approach. While these data are often expected to provide a clear and conclusive view on complex issues, it is important to remember that they have limitations. The use of real-world data should be done cautiously, carefully, and in a standardized manner. It is crucial to consider the uncertainties and limitations arising from biases and methodological flaws, which are common in any study aiming to reflect reality. These flaws can impact the reliability of the data, requiring a detailed analysis and a deep understanding of the context in which the data was collected.

However, when the conditions are appropriate, real-world data can be a powerful tool to support regulatory decisions and provide relevant scientific evidence. The combination of real-world data and its analysis can indeed generate valid scientific evidence, provided the characteristics of the data are well understood and properly evaluated.

Contact us today by email: contact@grinn.co and find out how we can boost the success of your project.

References

FDA. Food and Drug Administration (2017). Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. Guidance for Industry and Food and Drug Administration Staff. Available from: https://www.fda.gov/media/99447/download?attachment.

ANVISA. Agência Nacional de Vigilância Sanitária. (2023). Guia de boas práticas para estudos de dados do mundo real. Available from:: https://www.gov.br/anvisa/pt-br/assuntos/noticias-anvisa/2023/anvisa-publica-guia-de-evidencias-de-mundo-real-e-anuncia-grupo-de-trabalho-para-outubro/Guian64_2023_versao1.pdf