Nonrandomized studies are an important method for evaluating the effects of exposures (including environmental, occupational, and behavioral exposures) on human health. Risk of bias in nonrandomized studies of exposures (ROBINS-E) is used to evaluate the risk of bias in natural or occupational exposure observational studies. This paper introduces the main contents of ROBINS-E 2022, including backgrounds, seven domains, signal questions and the operation process.
The planning and reporting of synthesis questions in systematic review of intervention have a direct and important impact on the validity of the evaluation and the credibility of the results. Planning helps to reduce bias in the evaluation process and ensure the reproducibility of data synthesis. However, the field of systematic review currently lacks specific checklists and tools to guide how to plan and report these issues. The InSynQ (Intervention Synthesis Questions) checklist is a tool designed for planning and reporting data synthesis issues in systematic reviews of interventions. Its goal is to promote the standardization of systematic review methods, support systematic review participants in planning and comprehensively reporting data synthesis issues and structures, and provide a more accurate evidence base for clinical decision-making.
With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.