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.
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.
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.
ObjectiveTo interpret ROBIS, a new tool to evaluate the risk of bias in systematic reviews, to promote the comprehension of it and its proper application. MethodsWe explained each item of ROBIS tool, used it to evaluate the risk of bias of a selected intervention review whose title was Cyclophosphamide for Primary Nephrotic Syndrome of Children: A Systematic Review, and judged the risk of bias in the review. ResultsThe selected systematic review as a whole was rated as “high risk of bias”, because there existed high risk of bias in domain 2 to 4, namely identification and selection of studies, data collection and study appraisal, synthesis and findings. The risk of bias in domain 1 (study eligibility criteria) was low. The relevance of identified studies and the review’s research question was appropriately considered and the reviewers avoided emphasizing results on the basis of their statistical significance. ConclusionROBIS is a new tool worthy of being recommended to evaluate risk of bias in systematic reviews. Reviewers should use ROBIS items as standards to conduct and produce high quality systematic reviews.
ObjectiveTo evaluate the risk of bias and reliability of conclusions of systematic reviews (SRs) of lung cancer screening. MethodsWe searched PubMed, EMbase, The Cochrane Library (Issue 2, 2016), Web of Knowledge, CBM, WanFang Data and CNKI to collect SRs of lung cancer screening from inception to February 29th, 2016. The ROBIS tool was applied to assess the risk of bias of included SRs, and then GRADE system was used for evidence quality assessment of outcomes of SRs. ResultsA total of 11 SRs involving 5 outcomes (mortality, detection rate, survival rate, over-diagnosis and potential benefits and harms) were included. The results of risk of bias assessment by ROBIS tool showed:Two studies completely matched the 4 questions of phase 1. In the phase 2, 6 studies were low risk of bias in the including criteria field; 8 studies were low risk of bias in the literature search and screening field; 3 studies were low risk of bias in the data abstraction and quality assessment field; and 5 studies were low risk of bias in the data synthesis field. In the phase 3 of comprehensive risk of bias results, 5 studies were low risk. The results of evidence quality assessment by GRADE system showed:three studies had A level evidence on the outcome of mortality; 1 study had A level evidence on detection; 1 study had A level evidence on survival rate; 3 studies on over-diagnosis had C level evidence; and 2 studies on potential benefits and harms had B level evidence. ConclusionThe risk of bias of SRs of lung cancer screening is totally modest; however, the evidence quality of outcomes of these SRs is totally low. Clinicians should cautiously use these evidence to make decision based on local situation.
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.
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.
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.
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.
Accurately assessing the risk of bias is a critical challenge in network meta-analysis (NMA). By integrating direct and indirect evidence, NMA enables the comparison of multiple interventions, but its outcomes are often influenced by bias risks, particularly the propagation of bias within complex evidence networks. This paper systematically reviews commonly used bias risk assessment tools in NMA, highlighting their applications, limitations, and challenges across interventional trials, observational studies, diagnostic tests, and animal experiments. Addressing the issues of tool misapplication, mixed usage, and the lack of comprehensive tools for overall bias assessment in NMA, we propose strategies such as simplifying tool operation, enhancing usability, and standardizing evaluation processes. Furthermore, advancements in artificial intelligence (AI) and large language models (LLMs) offer promising opportunities to streamline bias risk assessments and reduce human interference. The development of specialized tools and the integration of intelligent technologies will enhance the rigor and reliability of NMA studies, providing robust evidence to support medical research and clinical decision-making.