ObjectivesTo establish an appropriate data governance mode in according with the database status of clinical study.MethodsForty-six doctors of different seniority with clinical research experience from six hospitals in Beijing were selected by stratified purposeful sampling and semi-structured interview and were used to understand the status and shortcomings of data acquisition and storage in clinical research. The data resource of current clinical studies were summarized and the main target of data governance and the characteristics of clinical study data were explored to establish the domains of clinical study data governance to construct the framework of clinical research data governance.ResultsCurrently, the data sources of clinical studies were diverse, including real-world data from various medical and health records, data collected independently for clinical studies and numerous other sources. However, since collecting the data from electronic medical records was difficult for numerous reasons, a large number of researchers still collected research data by hand writing and stored it insecurely. In addition, the combination of electronic information from multiple sources was difficult. Building ALCOA+CCEA standard clinical research data management system based on clinical research data governance was urgent. Data governance includes data architecture, data model, data standards, data quality, master data, timeliness management, metadata and data security, while life cycle management and data insight were not essential parts.ConclusionsBased on the real-world data resources, domains of data governance in clinical study should include data architecture, data model, data standards, data quality, master data, timeliness management, metadata and data security.
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.
To enhance the quality and transparency of oncology real-world evidence studies, the European Society for Medical Oncology (ESMO) has developed the first specific reporting guidelines for oncology RWE studies in peer-reviewed journals "the ESMO Guidance for Reporting Oncology Real-World Evidence (GROW)". To facilitate readers understanding and application of these reporting standards, this article introduces and interprets the development process and main contents of the ESMO-GROW checklist.
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 (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.
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.
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.
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.
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.
The active comparator, new user (ACNU) design is an important design developed under the concept of the target simulation experimental framework. It aims to reduce indication confounding, immortal time bias, prevalence-incidence bias, and other unmeasured confounders by simulating head-to-head randomized controlled trials. It is widely applied in scenarios such as comparing the efficacy of newly marketed drugs with existing standard treatments, evaluating drug safety and adherence, exploring drug repurposing, and optimizing algorithms for processing medical big data. This article introduces the application and practice of the ACNU design in real-world data research from aspects such as concept, development, advantages and disadvantages, and implementation points, and also presents an outlook on its application in the field of traditional Chinese medicine. It is believed that with the progress in understanding the design of observational studies of real-world data, the ACNU design is expected to be more widely applied and provide new ideas for researchers' scientific research designs.