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.
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.
Traditional Chinese medicine (TCM) has a long history. In the process of fighting against diseases, TCM has formed a unique theoretical system and the way to think and diagnose. The holistic thinking, and the treatment according to syndrome differentiation are the most prominent characteristics of TCM, which matches with advanced medical concept and direction. The clinical efficacy has always been the basis for the advancement of TCM. However, issues such as the lagging behind of modern research on the evaluation of TCM curative effect, as well as lacking high-quality scientific research evidence, impede the development and promotion of the TCM toward the world. To address the above problems, recent progress in real-word study (RWS) has provided the opportunity for TCM researches, especially for the post-marketing evaluation of Chinese patent medicine (CPM). The formulation of this technical guidance for RWS of CPM is helpful to researchers in carrying out standardized, reasonable and scientific researches, to improve the quality of production and use of real-word evidence, and to promote the advancement of the TCM industry.
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.
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.
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.