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.
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 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.
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.
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.
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.
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 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.
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.
ObjectivesTo comprehensively evaluate the methodological quality and applicability of the results of systematic reviews on acupuncture treatment for primary depression.MethodsWeb of Science, EMbase, PubMed, The Cochrane Library, CNKI, CBM, WanFang Data and VIP databases were electronically searched to collect systematic reviews/meta-analyses on acupuncture treatment for primary depression from inception to December 5th, 2018. Two researchers independently screened and extracted data by using tools of AMSTAR 2 to evaluate the methodological quality, using ROBIS to assess risk of bias, and using CASP-S.R to evaluate the applicability of the results.ResultsA total of 18 systematic reviews/meta-analyses were included, and all focused on acupuncture intervention, including 2 primary outcome indicators. According to AMSTAR 2 evaluation results, there were 4 high quality studies, 12 medium quality studies and 2 low quality studies; ROBIS results found 10 high bias risk studies, 7 low bias risk studies and 1 unclear; CASP-S.R showed only 4 design studies applicable to local individuals, and there were no studies on the relationship between design benefits, hazards and costs.ConclusionsThe quality of systematic reviews/meta-analyses for acupuncture treatment of primary depression is moderate, however with a certain bias. Most studies may not directly benefit local individuals. All studies have no relationship with cost hazards. It is expected for further reviewers to strictly follow systematic evaluation method to improve research quality and reduce bias, while the applicability of the systematic review to individuals from different regions should be considered as well as the relationship between the benefit and cost hazard. In addition, more valid RCTs are required to provide higher quality evidence and explore correlated and comprehensive mechanism.
