This paper introduces the main contents of ROB-ME (Risk Of Bias due to Missing Evidence), including backgrounds, scope of the tool, signal questions and the operation process. The ROB-ME tool has the advantages of clear logic, complete details, simple operation and good applicability. The ROB-ME tool offers considerable advantages for assessing the risk of non-reporting biases and will be useful to researchers, thus being worth popularizing and applying.
Measurement properties studies of patient-reported outcome measures (PROMs) aims to validate the measurement properties of PROMs. In the process of designing and statistical analysis of these measurement properties studies, bias will occur if there are any defects, which will affect the quality of PROMs. Therefore, the COSMIN (consensus-based standards for the selection of health measurement instruments) team has developed the COSMIN risk of bias (COSMIN-RoB) checklist to evaluate risk of bias of studies on measurement properties of PROMs. The checklist can be used to develop systematic reviews of PROMs measurement properties, and for PROMs developers, it can also be used to guide the research design in the measurement tool development process for reducing bias. At present, similar assessment tools are lacking in China. Therefore, this article aims to introduce the primary contents of COSMIN-RoB checklist and to interpret how to evaluate risk of bias of the internal structure studies of PROMs with examples.
The current issue of air pollution has pushed the development of the corresponding observational air pollution studies. The World Health Organization has developed a new risk of bias (RoB) assessment instrument and a related guideline for assessing the risk of potential bias in observational air pollution studies. This study introduced the background, methods, uses, advantages and disadvantages, precautions, and usage scenarios of the RoB instrument. It is expected to provide researchers with corresponding quality evaluation tools when writing related systematic review and meta-analysis, which will also help provide reporting standards for observational air pollution studies, thereby improving the quality of studies.
This study aims to introduce how to use the PROBAST (prediction model risk of bias assessment tool) to evaluate risk of bias and applicability of the study of diagnostic or prognostic predictive models, including the introduction of the background, the scope of application and use of the tool. This tool mainly involves the four areas of participants, predictors, outcomes and analyses. The risk of bias in the research is evaluated through the four areas, while the applicability is evaluated in the first three. PROBAST provides a standardized approach to evaluate the critical appraisal of the study of diagnostic or prognostic predictive models, which screens qualified literature for data analysis and helps to establish a scientific basis for clinical decision-making.
The COSMIN-RoB checklist includes three sections with a total of 10 boxes, which is used to evaluate risk of bias of studies on content validity, internal structure, and other measurement properties. COSMIN classifies reliability, measurement error, criteria validity, hypothesis testing for construct validity, and responsiveness as other measurement properties, which primarily focus on the quality of the (sub)scale as a whole, rather than on the item level. Among the five measurement properties, reliability, measurement error and criteria validity are the most widely used in the studies. Therefore, this paper aims to interpret COSMIN-RoB checklist with examples to guide researchers to evaluate the risk of bias of the studies on reliability, measurement error and criteria validity of PROMs.
At present, there are many items/checklists used to assess the methodological quality of animal studies. Yet, no tool has been specifically designed for assessing internal validity of animal studies. This articles introduce and interprets SYRCLE's risk of bias tool for animal studies in detail for Chinese scholars to accurately assess the methodological quality of animal studies when they develop systematic reviews on animal studies, so as to provide references for scientific design and implementation of animal studies.
The COSMIN community updated the COSMIN-RoB checklist on reliability and measurement error in 2021. The updated checklist can be applied to the assessment of all types of outcome measurement studies, including clinician-reported outcome measures (ClinPOMs), performance-basd outcome measurement instruments (PerFOMs), and laboratory values. In order to help readers better understand and apply the updated COSMIN-RoB checklist and provide methodological references for conducting systematic reviews of ClinPOMs, PerFOMs and laboratory values, this paper aimed to interpret the updated COSMIN-RoB checklist on reliability and measurement error studies.
High-quality randomized controlled trials are the best source of evidence to explain the relationship between health interventions and outcomes. However, in cases where they are insufficient, indirect, or inappropriate, researchers may need to include non-randomized studies of interventions to strengthen the evidence body and improve the certainty (quality) of evidence. The latest research from the GRADE working group provides a way for researchers to integrate randomized and non-randomized evidence. The present paper introduced the relevant methods to provide guidance for systematic reviewers, health technology assessors, and guideline developers.
RoB2 (revised version 2019), an authoritative tool for assessing the risk of bias in randomized controlled trials, has been updated and improved based on the original version. This article elaborated and interpreted the background and main content of RoB2 (revised version 2019), as well as the operation process of the new software. Compared with the previous version of RoB2 (revised version 2018), RoB2 (revised version 2019) has the advantages of rich content, complete details, accurate questions, and simple operation, etc. Additionally, it is more user-friendly for researchers and beginners. The risk bias assessment of randomized controlled trials is more comprehensive and accurate, and it is an authoritative, trustworthy, and popular tool for evaluating the risk of bias in randomized controlled studies in medical practice.
ObjectiveTo systematically interpret the updated risk of bias in non-randomized studies of interventions version 2 (ROBINS-I V2) in 2024, summarizing its key improvements, operational procedures, and clinical application value. MethodsThrough literature review and case studies, the improvements of ROBINS-I V2 were compared with the 2016 version, including the expansion of bias domains, refinement of signaling questions, and optimization of decision flowcharts. A retrospective study in stomatology was used to demonstrate the practical application of the tool. ResultsThe ROBINS-I V2 tool has restructured the hierarchy and refined the definitions of bias domains, optimized the evaluation processes across seven risk-of-bias dimensions, and minimized subjective judgment errors through standardized decision flowcharts. ConclusionROBINS-I V2 significantly improves the rigor of bias assessment in non-randomized intervention studies through its scientific design and standardized workflow. It is recommended for evidence quality grading and decision-making support in clinical research.