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.
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.
Statistical graph is an indispensable part of scientific papers. It is helpful to promote the communication, dissemination, and application of academic achievements by presenting research results intuitively and accurately through standardized and beautiful visual graphs. The safety of a medical intervention is the basic premise of its clinical application, and randomized controlled trial (RCT) as an important design to determine the efficacy and safety of medical interventions, it is extremely important to accurately present the information on the safety outcomes of interventions found therein. However, the research found that the reports of RCTs didn’t adequately use visual graphs to present harms data. In order to promote clinical researchers to better use visual graphs to present harms data, international scholars recently published a consensus study in BMJ, which identified and recommended 10 statistical graphs for presenting harms data in RCTs. In order to facilitate domestic scholars to understand and apply the consensus, this article interprets the consensus and recommendations, and it is expected to provide help for improving the quality of harms visualization in domestic papers of RCTs.
Clinical prediction models typically utilize a combination of multiple variables to predict individual health outcomes. However, multiple prediction models for the same outcome often exist, making it challenging to determine the suitable model for guiding clinical practice. In recent years, an increasing number of studies have evaluated and summarized prediction models using the systematic review/meta-analysis method. However, they often report poorly on critical information. To enhance the reporting quality of systematic reviews/meta-analyses of prediction models, foreign scholars published the TRIPOD-SRMA reporting guideline in BMJ in March 2023. As the number of such systematic reviews/meta-analyses is increasing rapidly domestically, this paper interprets the reporting guideline with a published example. This study aims to assist domestic scholars in better understanding and applying this reporting guideline, ultimately improving the overall quality of relevant research.