Evidence synthesis is the process of systematically gathering, analyzing, and integrating available research evidence. The quality of evidence synthesis depends on the quality of the original studies included. Validity assessment, also known as risk of bias assessment, is an essential method for assessing the quality of these original studies. Currently, there are numerous validity assessment tools available, but some of them lack a rigorous development process and evaluation. The application of inappropriate validity assessment tools to assessing the quality of the original studies during the evidence synthesis process may compromise the accuracy of study conclusions and mislead the clinical practice. To address this dilemma, the LATITUDES Network, a one-stop resource website for validity assessment tools, was established in September 2023, led by academics at the University of Bristol, U.K. This Network is dedicated to collecting, sorting and promoting validity assessment tools to improve the accuracy of original study validity assessments and increase the robustness and reliability of the results of evidence synthesis. This study introduces the background of the establishment of the LATITUDES Network, the included validity assessment tools, and the training resources for the use of validity assessment tools, in order to provide a reference for domestic scholars to learn more about the LATITUDES Network, to better use the appropriate validity assessment tools to conduct study quality assessments, and to provide references for the development of validity assessment tools.
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.
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.
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.
GRADE（Grades of Recommendation, Assessment, Development，and Evaluation）方法为卫生保健中的证据质量评价与推荐强度评级提供指导。对那些为系统评价、卫生技术评估及临床实践指南总结证据的人而言，GRADE具有重要意义。GRADE提供了一个系统而透明的框架用以明确问题，确定所关注的结局，总结针对某问题的证据，以及从证据到形成推荐或作出决策。GRADE方法的广泛传播与应用，获全球50余个组织认可，这些组织大多有很强的影响力（http://www.gradeworkinggroup.org/），足以证明该工作的重要性。本文介绍临床流行病学杂志将刊出的20篇系列文章，为如何使用GRADE方法提供指导。
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.
ObjectiveTo evaluate whether and to what extent the new risk of bias (ROB) tool has been used in Cochrane systematic reviews (CSRs) on acupuncture. MethodsWe searched the Cochrane Database of Systematic Review (CDSR) in issue 12, 2011. Two reviewers independently selected CSRs which primarily focused on acupuncture and moxibustion. Then the data involving in essential information, the information about ROB (sequence generation, allocation concealment, blindness, incomplete outcome data, selective reporting and other potential sources of bias) and GRADE were extracted and statistically analyzed. ResultsIn total, 41CSRs were identified, of which 19 CSRs were updated reviews. Thirty-three were published between 2009 and 2011. 60.98% reviews used the Cochrane Handbook as their ROB assessment tool. Most CSRs gave information about sequence generation, allocation concealment, blindness, and incomplete outcome data, however, half of them (54.55%, 8/69) showed selective reporting or other potential sources of bias. Conclusion"Risk of bias" tools have been used in most CSRs on acupuncture since 2009. However, the lack of evaluation items still remains.
Selective non-reporting and publication bias of study results threaten the validity of systematic reviews and meta-analyses, thus affect clinical decision making. There are no rigorous methods to evaluate the risk of bias in network meta-analyses currently. This paper introduces the main contents of ROB-MEN (risk of bias due to missing evidence in network meta-analysis), including tables of the tool, operation process and signal questions. The pairwise comparisons table and the ROB-MEN table are the tool’s core. The ROB-MEN tool can be applied to very large and complex networks including lots of interventions to avoid time-consuming and labor-intensive process, and it has the advantages of clear logic, complete details and good applicability. It is the first tool used to evaluate the risk of bias due to missing evidence in network meta-analysis and is useful to researchers, thus being worth popularizing and applying.
证据质量升级的最常见原因是效应量大。当方法学严谨的观察性研究表明风险至少降低或增加2倍时，GRADE建议考虑将证据质量升高1级；当风险至少降低或增加5倍时，考虑将证据质量升高2级。当存在剂量-反应关系，或所有合理的混杂、偏倚会降低明显的治疗效应，或混杂、偏倚使得结果无效为假效应时，系统评价作者和指南制定者也可考虑升高证据质量。其他考虑因素包括起效迅速、潜在的疾病（状态）趋势以及间接证据。
With the rapid development of artificial intelligence (AI) and machine learning technologies, the development of AI-based prediction models has become increasingly prevalent in the medical field. However, the PROBAST tool, which is used to evaluate prediction models, has shown growing limitations when assessing models built on AI technologies. Therefore, Moons and colleagues updated and expanded PROBAST to develop the PROBAST+AI tool. This tool is suitable for evaluating prediction model studies based on both artificial intelligence methods and regression methods. It covers four domains: participants and data sources, predictors, outcomes, and analysis, allowing for systematic assessment of quality in model development, risk of bias in model evaluation, and applicability. This article interprets the content and evaluation process of the PROBAST+AI tool, aiming to provide references and guidance for domestic researchers using this tool.