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.
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.
In 2019, the national government issued the document "Implementation Plan for Supporting the Construction of the Boao Lecheng International Medical Tourism Pilot Area", which allowed the use of innovative drugs and medical devices in medical institution of Boao Lecheng. These medical products had been designed to meet urgent clinical requirements and had been approved by regulatory authorities overseas. Through the use of these medical products, real-world data were generated in the routine clinical practice, based on which real-world evidence might be produced for regulatory decision-making by using scientific and rigorous methods. In March 2020, the first medical device product using domestic real-world data was approved, suggesting that the real-world data initiative in Boao Lecheng achieved initial success. This work also provided important experience for promoting the practice of medical device regulatory decision-making based on real-world evidence in China. Here, we shared the preliminary experiences from the study on the first approved medical device product and discussed the issues on developing a real-world data research framework in Boao Lecheng in attempt to offer insights for future studies.
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.
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.
Real-world data is been increasingly valued nowadays. This paper combined with related requirements of clinical evaluation of medical devices in China, studied the role of real-world evidence in pre-marketing clinical evaluation of medical devices in terms of technical evaluation, in aim of providing reference for the future application of China's real-world evidence in pre-marketing clinical evaluation.
Retrospective chart review (RCR) is a type of research that answers specific research questions based on the existing patient medical records or related databases through a series of research processes including data extraction, data collation, statistical analysis, etc. Relying on the development of medical big data, as well as the relatively simple implementation process and low cost of information acquisition, RCR is increasingly used in the medical research field. In this paper, we conducted the visual analysis of high-quality RCR published in the past five years, and explored and summarized the current research status and hotspots by analyzing the characteristics of the number of publications, national/regional and institutional cooperation networks, author cooperation networks, keyword co-occurrence and clustering networks. We further systematically combed the methodological core of this kind of research from eight aspects: research question and hypothesis, applicability of chart, study design, data collecting, statistical analysis, interpretation of results, and reporting specification. By summarizing the shortcomings, unique advantages and application prospects of RCR, providing guidance and suggestions for the standardized application of RCR in the medical research field in the future.
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.
Research of generating real-world evidence using real world data has attracted considerable attention globally. Outcome research of treatment based on existing health and medical data or registries has become one of the most important topics. However, there exists certain confusions in this line of research on how to design and implement appropriate statistical analysis. Therefore, in the fourth chapter of the series technical guidance to develop real world evidence by China REal world data and studies Alliance (ChinaREAL), we aim to provide an guidance on statistical analysis in the study to assess therapeutic outcomes based on existing health and medical data or registries.In this chapter, we first emphasize the significance of pre-specified statistical analysis plan, recommending key components of the statistical analysis plan. We then summarize the issue of sample size calculation in this content and clarify the interpretation of statistical p-value. Secondly, we recommend procedures to be considered to tackle the issue related to the selection bias, information bias and most importantly, confounding bias. We discuss the multivariable regression analysis as well as the popular causal inference models. We also suggest that careful consideration should be made to deal with missing data in real-world databases. Finally, we list core content of the statistical report.
Evidence-based medicine is the methodology of modern clinical research and plays an important role in guiding clinical practice. It has become an integral part of medical education. In the digital age, evidence-based medicine has evolved to incorporate innovative research models that utilize multimodal clinical big data and artificial intelligence methods. These advancements aim to address the challenges posed by diverse research questions, data methods, and evidence sources. However, the current teaching content in medical schools often fails to keep pace with the rapidly evolving disciplines, impeding students' comprehensive understanding of the discipline's knowledge system, cutting-edge theories, and development directions. In this regard, this article takes the opportunity of graduate curriculum reform to incorporate real-world data research, artificial intelligence, and bioinformatics into the existing evidence-based medicine curriculum, and explores the reform of evidence-based medicine teaching in the information age. The aim is to enable students to truly understand the role and value of evidence-based medicine in the development of medicine, while possessing a solid theoretical foundation, a broad international perspective, and a keen research sense, in order to cultivate talents for the development of the evidence-based medicine discipline.