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.
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.
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.
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.
With the acceleration of global innovative drug development, selecting safe, effective, and cost-effective products from numerous drugs has posed new challenges for the decision-making process of medical insurance drug access and dynamic updating of insurance directory. Real-world data (RWD) provides a new perspective for evaluation of clinical and economic value of drugs, but there are still uncertainties regarding the scope, quality standards, and evidence categories of RWD that can be used. Based on the current status of domestic and international RWD supporting the assessment of the clinical and economic value of drugs, this paper, in collaboration with national RWD and healthcare experts, has developed the key considerations for using real-world data to evaluate the clinical and economic value of drugs. This paper first clarifies the scope of RWD that can be used to evaluate the clinical and economic value of drugs evaluate; secondly, provides specific requirements and guidance on data attribution, data governance, and quality standards for RWD; finally, summarizes the evidence categories of RWD supporting evaluate the clinical and economic value of drugs evaluate.
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.
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.
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 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.