Research Synthesis Methods最新文献

{"title":"A comprehensive review and shiny application on the matching-adjusted indirect comparison","authors":"Ziren Jiang,&nbsp;Joseph C. Cappelleri,&nbsp;Margaret Gamalo,&nbsp;Yong Chen,&nbsp;Neal Thomas,&nbsp;Haitao Chu","doi":"10.1002/jrsm.1709","DOIUrl":"10.1002/jrsm.1709","url":null,"abstract":"<p>Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 4","pages":"671-686"},"PeriodicalIF":5.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139911664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of trial attrition rates on treatment effect estimates in chronic inflammatory diseases: A meta-epidemiological study","authors":"Silja H. Overgaard,&nbsp;Caroline M. Moos,&nbsp;John P. A. Ioannidis,&nbsp;George Luta,&nbsp;Johannes I. Berg,&nbsp;Sabrina M. Nielsen,&nbsp;Vibeke Andersen,&nbsp;Robin Christensen","doi":"10.1002/jrsm.1708","DOIUrl":"10.1002/jrsm.1708","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The objective of this meta-epidemiological study was to explore the impact of attrition rates on treatment effect estimates in randomised trials of chronic inflammatory diseases (CID) treated with biological and targeted synthetic disease-modifying drugs. We sampled trials from Cochrane reviews. Attrition rates and primary endpoint results were retrieved from trial publications; Odds ratios (ORs) were calculated from the odds of withdrawing in the experimental intervention compared to the control comparison groups (i.e., differential attrition), as well as the odds of achieving a clinical response (i.e., the trial outcome). Trials were combined using random effects restricted maximum likelihood meta-regression models and associations between estimates of treatment effects and attrition rates were analysed. From 37 meta-analyses, 179 trials were included, and 163 were analysed (301 randomised comparisons; <i>n</i> = 62,220 patients). Overall, the odds of withdrawal were lower in the experimental compared to control groups (random effects summary OR = 0.45, 95% CI, 0.41–0.50). The corresponding overall treatment effects were large (random effects summary OR = 4.43, 95% CI 3.92–4.99) with considerable heterogeneity across interventions and clinical specialties (<i>I</i>² = 85.7%). The ORs estimating treatment effect showed larger treatment benefits when the differential attrition was more prominent with more attrition in the control group (OR = 0.73, 95% CI 0.55–0.96). Higher attrition rates from the control arm are associated with larger estimated benefits of treatments with biological or targeted synthetic disease-modifying drugs in CID trials; differential attrition may affect estimates of treatment benefit in randomised trials.</p>\u0000 </section>\u0000 </div>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 4","pages":"561-575"},"PeriodicalIF":5.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139728627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-analyses of partial correlations are biased: Detection and solutions","authors":"T. D. Stanley,&nbsp;Hristos Doucouliagos,&nbsp;Tomas Havranek","doi":"10.1002/jrsm.1704","DOIUrl":"10.1002/jrsm.1704","url":null,"abstract":"<p>We demonstrate that all meta-analyses of partial correlations are biased, and yet hundreds of meta-analyses of partial correlation coefficients (PCCs) are conducted each year widely across economics, business, education, psychology, and medical research. To address these biases, we offer a new weighted average, UWLS₊₃. UWLS₊₃ is the unrestricted weighted least squares weighted average that makes an adjustment to the degrees of freedom that are used to calculate partial correlations and, by doing so, renders trivial any remaining meta-analysis bias. Our simulations also reveal that these meta-analysis biases are small-sample biases (<i>n</i> &lt; 200), and a simple correction factor of (<i>n</i> − 2)/(<i>n</i> − 1) greatly reduces these small-sample biases along with Fisher's z. In many applications where primary studies typically have hundreds or more observations, partial correlations can be meta-analyzed in standard ways with only negligible bias. However, in other fields in the social and the medical sciences that are dominated by small samples, these meta-analysis biases are easily avoidable by our proposed methods.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 2","pages":"313-325"},"PeriodicalIF":9.8,"publicationDate":"2024-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1704","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139717116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Footprint of publication selection bias on meta-analyses in medicine, environmental sciences, psychology, and economics","authors":"František Bartoš,&nbsp;Maximilian Maier,&nbsp;Eric-Jan Wagenmakers,&nbsp;Franziska Nippold,&nbsp;Hristos Doucouliagos,&nbsp;John P. A. Ioannidis,&nbsp;Willem M. Otte,&nbsp;Martina Sladekova,&nbsp;Teshome K. Deresssa,&nbsp;Stephan B. Bruns,&nbsp;Daniele Fanelli,&nbsp;T. D. Stanley","doi":"10.1002/jrsm.1703","DOIUrl":"10.1002/jrsm.1703","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Publication selection bias undermines the systematic accumulation of evidence. To assess the extent of this problem, we survey over 68,000 meta-analyses containing over 700,000 effect size estimates from medicine (67,386/597,699), environmental sciences (199/12,707), psychology (605/23,563), and economics (327/91,421). Our results indicate that meta-analyses in economics are the most severely contaminated by publication selection bias, closely followed by meta-analyses in environmental sciences and psychology, whereas meta-analyses in medicine are contaminated the least. After adjusting for publication selection bias, the median probability of the presence of an effect decreased from 99.9% to 29.7% in economics, from 98.9% to 55.7% in psychology, from 99.8% to 70.7% in environmental sciences, and from 38.0% to 29.7% in medicine. The median absolute effect sizes (in terms of standardized mean differences) decreased from <i>d</i> = 0.20 to <i>d</i> = 0.07 in economics, from <i>d</i> = 0.37 to <i>d</i> = 0.26 in psychology, from <i>d</i> = 0.62 to <i>d</i> = 0.43 in environmental sciences, and from <i>d</i> = 0.24 to <i>d</i> = 0.13 in medicine.</p>\u0000 </section>\u0000 </div>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 3","pages":"500-511"},"PeriodicalIF":9.8,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139701391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"amstar2Vis: An R package for presenting the critical appraisal of systematic reviews based on the items of AMSTAR 2","authors":"Konstantinos I. Bougioukas,&nbsp;Paschalis Karakasis,&nbsp;Konstantinos Pamporis,&nbsp;Emmanouil Bouras,&nbsp;Anna-Bettina Haidich","doi":"10.1002/jrsm.1705","DOIUrl":"10.1002/jrsm.1705","url":null,"abstract":"<p>Systematic reviews (SRs) have an important role in the healthcare decision-making practice. Assessing the overall confidence in the results of SRs using quality assessment tools, such as “A MeaSurement Tool to Assess Systematic Reviews 2” (AMSTAR 2), is crucial since not all SRs are conducted using the most rigorous methods. In this article, we introduce a free, open-source R package called “amstar2Vis” (https://github.com/bougioukas/amstar2Vis) that provides easy-to-use functions for presenting the critical appraisal of SRs, based on the items of AMSTAR 2 checklist. An illustrative example is outlined, describing the steps involved in creating a detailed table with the item ratings and the overall confidence ratings, generating a stacked bar plot that shows the distribution of ratings as percentages of SRs for each AMSTAR 2 item, and creating a “ggplot2” graph that shows the distribution of overall confidence ratings (“Critically Low,” “Low,” “Moderate,” or “High”). We expect “amstar2Vis” to be useful for overview authors and methodologists who assess the quality of SRs with AMSTAR 2 checklist and facilitate the production of pertinent publication-ready tables and figures. Future research and applications could further investigate the functionality or potential improvements of our package.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 3","pages":"512-522"},"PeriodicalIF":9.8,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139690787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations","authors":"Lorna Wheaton,&nbsp;Dan Jackson,&nbsp;Sylwia Bujkiewicz","doi":"10.1002/jrsm.1707","DOIUrl":"10.1002/jrsm.1707","url":null,"abstract":"<p>During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker-mixed populations, later trials are more likely to enroll biomarker-positive patients alone, thus leading to trials of the same treatment investigated in different populations. When conducting a meta-analysis, a conservative approach would be to combine only trials conducted in the biomarker-positive subgroup. However, this discards potentially useful information on treatment effects in the biomarker-positive subgroup concealed within observed treatment effects in biomarker-mixed populations. We extend standard random-effects meta-analysis to combine treatment effects obtained from trials with different populations to estimate pooled treatment effects in a biomarker subgroup of interest. The model assumes a systematic difference in treatment effects between biomarker-positive and biomarker-negative subgroups, which is estimated from trials which report either or both treatment effects. The systematic difference and proportion of biomarker-negative patients in biomarker-mixed studies are used to interpolate treatment effects in the biomarker-positive subgroup from observed treatment effects in the biomarker-mixed population. The developed methods are applied to an illustrative example in metastatic colorectal cancer and evaluated in a simulation study. In the example, the developed method improved precision of the pooled treatment effect estimate compared with standard random-effects meta-analysis of trials investigating only biomarker-positive patients. The simulation study confirmed that when the systematic difference in treatment effects between biomarker subgroups is not very large, the developed method can improve precision of estimation of pooled treatment effects while maintaining low bias.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 4","pages":"543-560"},"PeriodicalIF":5.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1707","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139690788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of bias due to selective inclusion of study effect estimates in meta-analyses of nutrition research","authors":"Raju Kanukula,&nbsp;Joanne E. McKenzie,&nbsp;Lisa Bero,&nbsp;Zhaoli Dai,&nbsp;Sally McDonald,&nbsp;Cynthia M. Kroeger,&nbsp;Elizabeth Korevaar,&nbsp;Andrew Forbes,&nbsp;Matthew J. Page","doi":"10.1002/jrsm.1706","DOIUrl":"10.1002/jrsm.1706","url":null,"abstract":"<p>We aimed to explore, in a sample of systematic reviews (SRs) with meta-analyses of the association between food/diet and health-related outcomes, whether systematic reviewers selectively included study effect estimates in meta-analyses when multiple effect estimates were available. We randomly selected SRs of food/diet and health-related outcomes published between January 2018 and June 2019. We selected the first presented meta-analysis in each review (index meta-analysis), and extracted from study reports all study effect estimates that were eligible for inclusion in the meta-analysis. We calculated the Potential Bias Index (PBI) to quantify and test for evidence of selective inclusion. The PBI ranges from 0 to 1; values above or below 0.5 suggest selective inclusion of effect estimates more or less favourable to the intervention, respectively. We also compared the index meta-analytic estimate to the median of a randomly constructed distribution of meta-analytic estimates (i.e., the estimate expected when there is no selective inclusion). Thirty-nine SRs with 312 studies were included. The estimated PBI was 0.49 (95% CI 0.42–0.55), suggesting that the selection of study effect estimates from those reported was consistent with a process of random selection. In addition, the index meta-analytic effect estimates were similar, on average, to what we would expect to see in meta-analyses generated when there was no selective inclusion. Despite this, we recommend that systematic reviewers report the methods used to select effect estimates to include in meta-analyses, which can help readers understand the risk of selective inclusion bias in the SRs.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 4","pages":"524-542"},"PeriodicalIF":5.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139690844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Language inclusion in ecological systematic reviews and maps: Barriers and perspectives","authors":"Kelsey Hannah,&nbsp;Neal R. Haddaway,&nbsp;Richard A. Fuller,&nbsp;Tatsuya Amano","doi":"10.1002/jrsm.1699","DOIUrl":"10.1002/jrsm.1699","url":null,"abstract":"<p>Systematic reviews and maps are considered a reliable form of research evidence, but often neglect non-English-language literature, which can be a source of important evidence. To understand the barriers that might limit authors' ability or intent to find and include non-English-language literature, we assessed factors that may predict the inclusion of non-English-language literature in ecological systematic reviews and maps, as well as the review authors' perspectives. We assessed systematic reviews and maps published in <i>Environmental Evidence</i> (<i>n</i> = 72). We also surveyed authors from each paper (<i>n</i> = 32 responses), gathering information on the barriers to the inclusion of non-English language literature. 44% of the reviewed papers (32/72) excluded non-English literature from their searches and inclusions. Commonly cited reasons included constraints related to resources and time. Regression analysis revealed that reviews with larger author teams, authors from diverse countries, especially those with non-English primary languages, and teams with multilingual capabilities searched in a significantly greater number of non-English languages. Our survey exposed limited language diversity within the review teams and inadequate funding as the principal barriers to incorporating non-English language literature. To improve language inclusion and reduce bias in systematic reviews and maps, our study suggests increasing language diversity within review teams. Combining machine translation with language skills can alleviate the financial and resource burdens of translation. Funding applications could also include translation costs. Additionally, establishing language exchange systems would enable access to information in more languages. Further studies investigating language inclusion in other journals would strengthen these conclusions.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 3","pages":"466-482"},"PeriodicalIF":9.8,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139574323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"P-hacking in meta-analyses: A formalization and new meta-analytic methods","authors":"Maya B. Mathur","doi":"10.1002/jrsm.1701","DOIUrl":"10.1002/jrsm.1701","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with nonsignificant or negative estimates). However, meta-analyses can also be compromised by selection within studies (SWS), in which investigators “<i>p</i>-hack” results <i>within</i> their study to obtain an affirmative estimate. Published estimates can then be biased even conditional on affirmative status, which comprises the performance of existing methods that only consider SAS. We propose two new analysis methods that accommodate joint SAS and SWS; both analyze only the published nonaffirmative estimates. First, we propose estimating the underlying meta-analytic mean by fitting “right-truncated meta-analysis” (RTMA) to the published nonaffirmative estimates. This method essentially imputes the entire underlying distribution of population effects. Second, we propose conducting a standard meta-analysis of only the nonaffirmative studies (MAN); this estimate is conservative (negatively biased) under weakened assumptions. We provide an R package (phacking) and website (metabias.io). Our proposed methods supplement existing methods by assessing the robustness of meta-analyses to joint SAS and SWS.</p>\u0000 </section>\u0000 </div>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 3","pages":"483-499"},"PeriodicalIF":9.8,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jrsm.1701","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139562599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency of use and adequacy of Cochrane risk of bias tool 2 in non-Cochrane systematic reviews published in 2020: Meta-research study","authors":"Andrija Babić,&nbsp;Ognjen Barcot,&nbsp;Tomislav Visković,&nbsp;Frano Šarić,&nbsp;Aleksandar Kirkovski,&nbsp;Ivana Barun,&nbsp;Zvonimir Križanac,&nbsp;Roshan Arjun Ananda,&nbsp;Yuli Viviana Fuentes Barreiro,&nbsp;Narges Malih,&nbsp;Daiana Anne-Marie Dimcea,&nbsp;Josipa Ordulj,&nbsp;Ishanka Weerasekara,&nbsp;Matteo Spezia,&nbsp;Marija Franka Žuljević,&nbsp;Jelena Šuto,&nbsp;Luca Tancredi,&nbsp;Anđela Pijuk,&nbsp;Susanna Sammali,&nbsp;Veronica Iascone,&nbsp;Thilo von Groote,&nbsp;Tina Poklepović Peričić,&nbsp;Livia Puljak","doi":"10.1002/jrsm.1695","DOIUrl":"10.1002/jrsm.1695","url":null,"abstract":"<p>Risk of bias (RoB) assessment is essential to the systematic review methodology. The new version of the Cochrane RoB tool for randomized trials (RoB 2) was published in 2019 to address limitations identified since the first version of the tool was published in 2008 and to increase the reliability of assessments. This study analyzed the frequency of usage of the RoB 2 and the adequacy of reporting the RoB 2 assessments in non-Cochrane reviews published in 2020. This meta-research study included non-Cochrane systematic reviews of interventions published in 2020. For the reviews that used the RoB 2 tool, we analyzed the reporting of the RoB 2 assessment. Among 3880 included reviews, the Cochrane RoB 1 tool was the most frequently used (<i>N</i> = 2228; 57.4%), followed by the Cochrane RoB 2 tool (<i>N</i> = 267; 6.9%). From 267 reviews that reported using the RoB 2 tool, 213 (79.8%) actually used it. In 26 (12.2%) reviews, erroneous statements were used to indicate the RoB 2 assessment. Only 20 (9.4%) reviews presented a complete RoB 2 assessment with a detailed table of answers to all signaling questions. The judgment of risk of bias by the RoB 2 tool was not justified by a comment in 158 (74.2%) reviews. Only in 33 (14.5%) of reviews the judgment in all domains was justified in the accompanying comment. In most reviews (81.7%), the RoB was inadequately assessed at the study level. In conclusion, the majority of non-Cochrane reviews published in 2020 still used the Cochrane RoB 1 tool. Many reviews used the RoB 2 tool inadequately. Further studies about the uptake and the use of the RoB 2 tool are needed.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"15 3","pages":"430-440"},"PeriodicalIF":9.8,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139540815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}