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.
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.
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.
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.
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.
A patient registry database is an important source of real-world data, and has been widely used in the assessment of drug and medical devices, as well as disease management. As the second part of the serial technical guidance for real-world data and studies, this paper introduces the concept and scope of potential uses of patient registry databases, proposes recommendations for planning and developing a patient registry database, and compares existing health and medical databases. This paper further develops essential quality indicators for developing a patient registry database, in expect to guide future studies.
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.
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.
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.