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.
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.
The QUADAS-2, QUIPS, and PROBAST tools are not specific for prognostic accuracy studies and the use of these tools to assess the risk of bias in prognostic accuracy studies is prone to bias. Therefore, QUAPAS, a risk of bias assessment tool for prognostic accuracy studies, has recently been developed. The tool combines QUADAS-2, QUIPS, and PROBAST, and consists of 5 domains, 18 signaling questions, 5 risk of bias questions, and 4 applicability questions. This paper will introduce the content and usage of QUAPAS to provide inspiration and references for domestic researchers.
Objective To systematically review the accuracy and consistency of large language models (LLM) in assessing risk of bias in analytical studies. Methods The cohort and case-control studies related to COVID-19 based on the team's published systematic review of clinical characteristics of COVID-19 were included. Two researchers independently screened the studies, extracted data, and assessed risk of bias of the included studies with the LLM-based BiasBee model (version Non-RCT) used for automated evaluation. Kappa statistics and score differences were used to analyze the agreement between LLM and human evaluations, with subgroup analysis for Chinese and English studies. Results A total of 210 studies were included. Meta-analysis showed that LLM scores were generally higher than those of human evaluators, particularly in representativeness of exposed cohorts (△=0.764) and selection of external controls (△=0.109). Kappa analysis indicated slight agreement in items such as exposure assessment (κ=0.059) and adequacy of follow-up (κ=0.093), while showing significant discrepancies in more subjective items, such as control selection (κ=−0.112) and non-response rate (κ=−0.115). Subgroup analysis revealed higher scoring consistency for LLM in English-language studies compared to that of Chinese-language studies. Conclusion LLM demonstrate potential in risk of bias assessment; however, notable differences remain in more subjective tasks. Future research should focus on optimizing prompt engineering and model fine-tuning to enhance LLM accuracy and consistency in complex tasks.
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.
The risk of bias assessment tool 2.0 (RoB 2.0) for analyzing cluster randomized trials and crossover trials (revised version 2021) has been updated. The current paper briefly delineates the history of the RoB 2.0 tool and includes an explanation and interpretation of the updated contents and software operation process for use with cluster randomized trials and crossover trials. Compared with the previous versions, the updated RoB 2.0 tool (revised version 2021) has the advantage of precise language and is easily understood. Thus, the updated RoB 2.0 tool merits popularization and further general application.
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.
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.
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.
Currently there is no tool designed specifically to assess the risk of bias in the design, conduct or analysis of systematic reviews. ROBIS (Risk Of Bias In Systematic reviews), which was developed lately, aims mainly to assess the risk of bias in the conduct and result interpretation of systematic reviews relating to interventions, etiology, diagnosis and prognosis, as well as the relevance of the systematic review questions and the practice questions that their users want to address. This paper aims to introduce the ROBIS tool to Chinese systematic review developers, guideline developers and other researchers to promote the comprehension of it and its application, so as to improve the quality of systematic reviews in China.