In 2014, the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) working group published guidance in BMJ to evaluate the certainty of the evidence (confidence in evidence, quality of evidence) from network meta-analysis. GRADE working group suggested rating the certainty of direct evidence, indirect evidence, and network evidence, respectively. Recently, GRADE working group has published a series of papers to improve and supplement this approach. This paper introduces the frontiers and advancement of GRADE approach to rate the certainty of evidence from network meta-analysis.
The development of evidence-based clinical practice guidelines is a sophisticated and systematic process, often requiring multidisciplinary efforts. The traditional approach to developing and updating clinical practice guidelines is usually time-consuming. These limitations obstacle the effective use of guideline recommendations and efficient transformation of most recent research evidence into practice. The MAGIC system is a novel method system for rapid creation and dissemination of high-quality clinical recommendations, including rapid creation of trustworthy recommendations, thus ensuring the scientific and efficient production of clinical practice guidelines; facilitating rapid dissemination and dynamic updating of clinical practice guidelines through recommendation release system (i.e., MAGICapp); and helping promote the production of relevant high-quality original research evidence by identifying the insufficiency of evidence in the process of creation of guideline recommendations. Ultimately, a complete closed-loop digital and trustworthy evidence ecosystem is developed. In order to further promote the effective transformation of research evidence into guideline recommendations, MAGIC China Center was established. We anticipate that the Center will assist the further development and effective use of clinical practice guideline 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.
