Real-world data is data routinely collected in clinical practice about a patient's health, coming from varied sources of information. This real-world data comes from electronic health records, information about products, diseases, digital health technologies, health plan providers, devices and social media, observational studies, as well as laboratory or imaging tests, prescription and sales of medicines, among other sources. Traditionally, and until recently, such data were applied only in post-marketing surveillance and studies of natural history, prevalence, incidence and progression of diseases. However, in recent years, with the increased availability of data generated with the help of technology, real-world data has begun to be seen as a viable alternative to filling gaps in knowledge or the impossibility of conducting randomized clinical trials (1– 3).
Randomized clinical trials remain the gold standard in evaluating the safety and effectiveness of medicines and medical devices, however, real-world data can facilitate the design and complementation of the results of these studies. In this context, the analysis of real-world data can contribute to reducing costs in the clinical phase, being easier to access and filling gaps in the care and treatment of diseases. Furthermore, it can make it easier to obtain information that would otherwise be more difficult to obtain or would be restricted to specific groups of patients. Important data such as rare adverse events, for example, can be evaluated in studies with real-world data. But, without a doubt, new methodologies need to be developed to guarantee confidence in the evidence generated through these data (1,2,4).
It is important to recognize that there are still a number of challenges in using real-world evidence to determine the potential benefits or risks of a medicine or medical device, including missing data, lack of randomization and lack of standardization in data collection. Such factors can lead to biases in the analysis, compromising the reliability and, consequently, the suitability of using these data in determining the efficacy and safety of a product or in making medical, regulatory and administrative decisions related to patients. Given these limitations, it is necessary to seek the standardization of processes and the establishment of criteria that guarantee the quality and reliability of the results produced by studies that use real-world data (1–3,5,6).
In recent years, international regulatory agencies have published guidelines for using real-world data for drug registration. In partnership with international agencies, ANVISA strived to standardize terminologies and guidelines regarding the design and conduct of studies with real-world data, which resulted in the publication of version 1 of the Guide to good practices for real-world data studies . The guide is a document that guides the design of protocols to demonstrate the safety and effectiveness of a medicine through studies with real-world data. This document aims to guide the construction of protocols, but it is up to the sponsor to adapt their studies to the scientific literature in order to meet the requirements for evaluating safety and efficacy based on evidence and current regulations (7).
In this new guide, ANVISA presents the current legislation, listing all resolutions and guides for the registration of medicines that must be observed and followed by sponsors and researchers. In addition, it provides data definitions and real-world evidence, and classifies data into the following types: qualitative and/or quantitative; whether structured, semi-structured or unstructured.
This classification is important in evaluating the feasibility of studies, execution and analysis of results.
Real-world studies should be used to evaluate the inclusion or change of dose or indication of a medication, as well as to evaluate the expansion of its use for underserved populations or for populations in which carrying out classic studies is difficult. Furthermore, real-world studies can also be carried out in the context of rare diseases, in situations where it is not possible to carry out traditional clinical trials for ethical reasons and to support the safety and efficacy results of randomized clinical trials.
There are characteristics that can compromise the collection, organization and analysis of real-world data, such as heterogeneity, lack and destructuring of data as well as the diversity of its sources. Thus, the challenges encountered in the development of real-world studies can impact the credibility of their results. To generate reliable evidence, observational studies need to be carefully designed and controlled, with their limitations clearly described in the protocol.
The choice of real-world study design is crucial to eliminating or minimizing biases arising from data collection and analysis. ANVISA points out, as important steps, the clarity of the research question and the choice of variables, followed by the reliability of the database, planning of collection and analysis techniques, calculation of the effect of sample size, assessment of the quality of sources of data, description of the sample number, clarity of selection criteria and data traceability. The types of study designs presented in the guide for generating real-world evidence are observational studies (prospective and retrospective), single-arm externally controlled trials, pragmatic studies, and sequential clinical trials. Despite emphasizing the prioritization of prospective studies, the guide offers the possibility of using retrospective studies, as long as they are capable of generating quality evidence.
Given the unfeasibility of carrying out a randomized clinical trial, real-world studies can be a feasible and interesting alternative. It is worth checking in the new guide the factors that need to be considered in the design and execution of these studies, aiming to ensure a high standard of scientific quality, always in accordance with ethical principles and current legislation.
References:
1. Liu F, Demosthenes P. Real-world data: a brief review of the methods, applications, challenges and opportunities. Vol. 22, BMC Medical Research Methodology. BioMed Central Ltd; 2022.
2. Dang A. Real-World Evidence: A Primer. Vol. 37, Pharmaceutical Medicine. Addis; 2023. p. 25–36.
3. Chodankar D. Introduction to real-world evidence studies. Vol. 12, Perspectives in Clinical Research. Wolters Kluwer Medknow Publications; 2021. p. 171–4.
4. Monti S, Grosso V, Todoerti M, Caporali R. Randomized controlled trials and real-world data: differences and similarities to untangled literature data. Vol. 57, Rheumatology (Oxford, England). NLM (Medline); 2018. p. vii54–8.
5. Bakker E, Plueschke K, Jonker CJ, Kurz X, Starokozhko V, Mol PGM. Contribution of Real-World Evidence in European Medicines Agency’s Regulatory Decision Making. Clin Pharmacol Ther. 2023 Jan 1;113(1):135–51.
6. Blacketer C, Defalco FJ, Ryan PB, Rijnbeek PR. Increasing trust in real-world evidence through evaluation of observational data quality. Journal of the American Medical Informatics Association. 2021 Oct 1;28(10):2251–7.
7. ANVISA. Best practice guide for real-world data studies. Guide no 64/2023. 2023. Guian64_2023_versao1.pdf (www.gov.br)
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