Given the growing importance of real-world data (RWD) in drug development, efficacy evaluation, and regulatory decision-making, establishing a scientific and systematic data quality regulatory framework has become a strategic priority for global pharmaceutical regulatory authorities. This paper analyzed the EU's advanced practices in RWD quality regulation, compared the RWD quality regulatory systems of China and the EU, and aimed to derive implications for enhancing China's own framework. The EU has made significant progress by promoting the interconnection, intercommunication, and efficient utilization of data resources, implementing a collaborative responsibility mechanism spanning the entire data lifecycle, developing a standardized, tool-based quality assessment system, and facilitating international cooperation and alignment of rules. While China has established an initial regulatory system for RWD quality, it still confronts challenges such as unclear mechanisms for data acquisition and utilization, underdeveloped operational standards, and unclear responsibility delineation. In contrast, by adapting relevant EU experience, China can refine its regulatory framework, establish mechanisms for the interconnection, intercommunication, and efficient utilization of RWD, develop more practical quality assessment toolkits, improve the lifecycle responsibility-sharing mechanism, and promote the alignment of RWD quality regulation with international standards. These enhancements will advance the standardization and refinement of RWD quality regulation in China, ultimately strengthening the scientific rigor and reliability of regulatory decisions.
With the boom of information technology and data science, real-world evidence (RWE) which is produced using diverse real-world data (RWD) has become an important source for healthcare practice and policy decisions, such as regulatory and coverage decisions, guideline development, and disease management. The production of high-quality RWE requires not only complete, accurate and usable data, but also scientific and sound study designs and data analyses to enable the questions of interest to be reliably answered. In order to improve the quality of production and use of RWE, China REal world data and studies ALliance (ChinaREAL) has developed the first series of technical guidance for developing real-world data and subsequent studies. The efforts are ongoing which would ultimately inform better healthcare practice and policy decisions.
Real-world data (RWD) in clinical research on specific categories of medical devices can generate sufficient quality evidence which will be used in decision making. This paper discusses the limitations of traditional randomized controlled trials in clinical research of medical devices, summarizes and analyses the applicable conditions of real-world evidence (RWE) for medical devices, interprets the new FDA guidance document on the characteristics of RWD for medical devices, in order to provide evidence for the use of RWE in medical devices in our country.
As an important source for real-world data, existing health and medical data have gained wide attentions recently. As the first part of the serial technical guidance for real-world data and studies, this report introduced the concepts, features and potential applications of existing medical and health data, proposed recommendations for planning and developing a research database using existing health and medical data, and developed essential indicators for assessing the quality of such research databases. The technical guidance may standardize and improve the development of research database using existing health and medical data in China.
Assessing the clinical value of pharmaceuticals is crucial for comprehensive evaluation in clinical practice and plays a vital role in supporting decision-making for drug supply assurance. Real-world data (RWD) offers valuable insights into the actual diagnosis and treatment processes, serving as a significant data source for evaluating the clinical demand, effectiveness, and safety of drugs. This technical guidance aims to elucidate the scope of application of RWD for the clinical value assessment of pharmaceuticals, as well as the key considerations for conducting value assessment research. These considerations include identifying the dimensions of clinical value that necessitate RWD and effectively utilizing RWD for evaluation purposes. Additionally, this guidance provides essential points for implementing pharmaceutical clinical value assessment based on real-world data, with a specific focus on study design and statistical analysis. By doing so, this guidance assists researchers in accurately comprehending and standardizing the utilization of real-world research in conducting pharmaceutical clinical research.
The rapid advancement of causal inference is driving a paradigm shift across various disciplines. "Target trial emulation" has emerged as an exceptionally promising framework for observational real-world studies, attracting substantial attention from medical scholars and regulatory agencies worldwide. This article aims to provide an introduction to CERBOT, an online tool that assists in implementing target trial emulation studies, while highlighting the advancements in this domain. Additionally, the article provides an illustrative example to elucidate the operational process of CERBOT. The objectives are to support domestic researchers in conducting target trial emulation studies and enhance the quality of real-world studies in the domestic medical field, as well as improve the medical service level in clinical practice.
Randomized controlled trials are considered as the gold standard for determining the causality, and are usually used to evaluate the efficacy and safety of medical interventions. However, in some cases it is not feasible to conduct a randomized controlled trial. In recent years, a framework called “target trial emulation study” has been formally established to guide the design and analysis of observational studies based on real-world data. This framework provides an effective method for causal inference based on observational studies. In order to facilitate domestic scholars to understand and apply the framework to solve related clinical problems, this article introduces it from the basic concept, framework structure and implementation steps, development status, and prospects.
Observational studies based on real-world data are providing increasing amount of evidence for evaluating therapeutic outcomes, which is important for timely decision-making. Although time and costs for data collection could be saved using real-world data, it is significantly more complex to design real world researches with lower risk of bias. In order to enhance the validity of causal inference and to reduce potential risk of bias in real world studies, the Working Group of China Real world data and studies Alliance (China REAL) has formulated recommendations for designing observational studies to evaluate therapeutic outcomes based on real-world data. This guidance introduces design types commonly used in real world research; recommends key elements to consider in observational studies, including sample selection, specifying and allocating exposures, defining study entry and endpoints, and pre-designing statistical analysis protocols; and summarizes potential biases and corresponding control measures in real-world studies. These recommendations introduces key elements in designing observational studies using real-world data, for the purpose of improving the validity of causal inference. However, the application scope of these recommendations may be limited and warrant constant improvement.
The application of economic tools to evaluate the cost and health benefits and screen out more cost-effective drugs and technologies is an important measure to improve efficiency of medical resource allocation in China. Given the inherent differences between strict clinical trials and clinical routine practice, using trial-based economic evaluations to guide relevant medical decisions may lead to a certain risk of value deviation. Recent development of real-world data provides opportunities to assess the cost-effectiveness of drugs under the practical utilization, and has gradually become a new research hotspot. However, the complexity of the actual clinical environment also puts higher demands on researchers and decision makers to construct, understand and apply real-world evidence. In order to further prompt the normalization of economic evaluation based on real-world data and promote the scientific application of real-world evidence in medical and health decision-making, this project aims at the crucial issues including scope, research design and quality evaluation, to clarify the key considerations on the using of real-world evidence in medical decision-making. Combined with the international guidelines, the latest advancement of relevant research areas and the advice and opinions from multidisciplinary experts, we aim to provide technical references and guidance for researchers and decision makers, and to strengthen the evidence base of management policies.
Real-world data studies have experienced rapid development in recent years, however, misunderstandings persist. This paper aims to improve practice and promote standardization by updating the categorization of real-world data, proposing two conceptual frameworks for conducting real-world data studies, developing the concepts of research data infrastructure and clarifying the misconceptions on registry database, and discussing future development.