Research Synthesis Methods最新文献

{"title":"Facilitators and barriers to engaging patient partners in knowledge syntheses: A stage-based approach.","authors":"Anna Maria Chudyk, Sasha M Kullman, Jenna Villarba, Caroline Monnin, Janet Gunderson, Jenna Kedy, Tracy Slaney, Andrea C Tricco","doi":"10.1017/rsm.2026.10099","DOIUrl":"https://doi.org/10.1017/rsm.2026.10099","url":null,"abstract":"<p><p>Knowledge synthesis involves bringing together findings from individual research studies to answer a specific question or explore a particular topic, often at a global level. People with lived or living experience of health conditions or of accessing healthcare services (PWLE), such as patients and their families or friends, can provide valuable perspectives throughout this process. PWLE involvement can help shape the design and conduct of a knowledge synthesis to better reflect real-world concerns through a process called patient engagement. Patient engagement is the meaningful and active involvement of PWLE throughout the research process. While previous research shows that PWLE can contribute meaningfully when appropriate supports are in place, there is limited practical guidance on how to enact patient engagement in knowledge syntheses. This tutorial paper aims to guide researchers and PWLE who would like to engage in knowledge syntheses together. Building on existing research, our team (comprised of PWLE, researchers, and an academic librarian) shares lessons we have learned from working together throughout the stages of a knowledge synthesis. We present considerations for successful patient engagement when research teams are: (1) planning to engage, (2) recruiting PWLE, (3) establishing roles and rapport, (4) supporting capacity development, (5) conducting the knowledge synthesis, and (6) mobilizing findings. We also identify 12 common barriers to patient engagement and offer strategies to address them, alongside recommendations for integrating PWLE across every stage of the knowledge synthesis process.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-21"},"PeriodicalIF":6.1,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148248339","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":"Meta-research on key metrics of preregistered scoping reviews.","authors":"Kim Nordmann, Paula Kuhn, Florian Fischer","doi":"10.1017/rsm.2026.10078","DOIUrl":"https://doi.org/10.1017/rsm.2026.10078","url":null,"abstract":"<p><p>Scoping reviews are increasingly used for evidence synthesis, yet a comprehensive overview of benchmarks for their conduct is lacking. This study aimed to extract and analyze key metrics of preregistered scoping reviews to aid researchers in planning, resource allocation, methodological rigor, and innovation. We examined 2,038 scoping review protocol registrations on the Open Science Framework between April and August 2024, identifying 891 corresponding publications. Extracted variables included review type (e.g., rapid scoping review), information about the number of studies in the screening process, methodological practices, and funding. Using Stata, we calculated process metrics such as review duration, yield rate, and team composition. Qualitative analysis was applied to further metrics, including aims and eligibility criteria frameworks. Among the 891 publications, 91.47% were published as scoping reviews. On average, reviews involved 6.11 authors and took 80.41 weeks from registration to publication, with rapid scoping reviews completed in 55.63 weeks. Most publications (80.13%) originated from high-income countries, where funding was more common. An average of 5.19 databases were searched, identifying a mean of 6,920.66 potential inclusions, with 92.64 studies ultimately included (yield rate: 5.68%). Endnote was the most frequently employed screening tool. Language restrictions were reported in 37.60% of cases, and 75.42% did not conduct or report critical appraisal. This study provides valuable insights into common practices within scoping review processes, offering researchers a foundation to allocate resources more efficiently and enhance research quality through methodological benchmarks. It underscores the need for greater transparency and improved methodological rigor.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-17"},"PeriodicalIF":6.1,"publicationDate":"2026-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148248342","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":"Model-based network meta-analysis: Joint estimation of dose-response and time-course relationships.","authors":"Anders Strathe, Martin Bøg, Anders Gorst-Rasmussen","doi":"10.1017/rsm.2026.10098","DOIUrl":"https://doi.org/10.1017/rsm.2026.10098","url":null,"abstract":"<p><p>Under the network meta-analysis (NMA) framework for aggregate data, there are limited possibilities for evidence synthesis across multiple treatment doses or timepoints. Model-based network meta-analysis (MBNMA) has been recommended as a framework for either evidence synthesis across multiple dose levels or across multiple timepoints to circumvent the limitations of the NMA. A joint dose-response and time-course MBNMA (DT-MBNMA) is proposed that combines the strengths of both DT-MBNMA. This framework allows for combining data at multiple timepoints from studies in early clinical development with a broad range of doses to late-stage clinical studies with a limited range of doses. The method respects randomization and allows for assessment of consistency and hence satisfies the requirements from reimbursement agencies. The method was validated in a simulation study, showing that the drug effect parameters and therefore indirect treatment effects could be recovered without bias, while the precision of the treatment effects was dependent on the simulated network. Compared with a standard NMA, the methodology increased the statistical efficiency of the indirect treatment comparison (ITC). The use of the method was further illustrated on a dataset consisting of seven randomized clinical trials (RCTs) (26 treatment arms) in the treatment of obesity with Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), with a broad range of doses and follow-up times. The method integrated phase 2 and 3 data seamlessly into the meta-analysis and provided greater precision on the treatment effects compared to NMA. Finally, the statistical framework may be used to support clinical decision-making, providing a framework for ITC during drug development.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-20"},"PeriodicalIF":6.1,"publicationDate":"2026-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148147742","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":"Using <i>Elicit</i> AI research assistant for data extraction in systematic reviews: A feasibility study across environmental and life sciences.","authors":"Malgorzata Lagisz, Ayumi Mizuno, Kyle Morrison, Pietro Pollo, Lorenzo Ricolfi, Yefeng Yang, Shinichi Nakagawa","doi":"10.1017/rsm.2026.10080","DOIUrl":"https://doi.org/10.1017/rsm.2026.10080","url":null,"abstract":"<p><p>Data extraction in systematic reviews, maps, and meta-analyses is time-consuming and prone to human error or subjective judgment. Large Language Models offer the potential for saving time, yet their performance has been evaluated in a limited range of platforms, disciplines, and review types. We assessed the performance of the Elicit platform across diverse data extraction tasks using journal articles from seven systematic reviews in life and environmental sciences. Human-extracted data served as the gold standard. For each review, we used eight articles for prompt development and another eight for testing. Initial prompts were iteratively refined to exceed 87% accuracy or up to five rounds. We then tested extraction accuracy, reproducibility across user accounts, and the effect of Elicit's high-accuracy mode. Of 90 considered prompts, 70 exceeded the 87% accuracy when compared to gold standard, but tended to be lower when tested on a new set of articles. Repeating data extractions with different Elicit user accounts resulted in 90% agreement on extracted values, though supporting quotes and reasoning matched in only 46% and 30% of cases, respectively. In high-accuracy mode, value matches dropped to 77%, with just 10% quote matches and 0% reasoning matches. Extraction accuracy did not differ by data types. Elicit also helped identify eight (<1%) errors in the gold standard data. Our results show that Elicit can complement, but not replace, human data extractors. Elicit may be best used for sanity checks and to evaluate the clarity of data extraction protocols. Prompts must be fine-tuned and independently validated.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-18"},"PeriodicalIF":6.1,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148051590","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":"Back-transformations in random-effects meta-analysis-Impact and interpretation.","authors":"Jan-Bernd Igelmann, Markus Pauly, Wolfgang Viechtbauer","doi":"10.1017/rsm.2026.10096","DOIUrl":"https://doi.org/10.1017/rsm.2026.10096","url":null,"abstract":"<p><p>In meta-analyses, effect size measures with bounded or non-normal sampling distributions are commonly analyzed on a transformed scale to justify normality assumptions. While point estimates and confidence intervals (CIs) are routinely back-transformed to the original scale for interpretation, this practice is nontrivial in random-effects models. In particular, standard inverse back-transformations yield estimates of the median rather than the mean effect size due to Jensen's inequality. Integral back-transformations provide a principled solution for recovering the mean on the original scale, but their use entails practical issues. We study integral back-transformations for several effect size measures, including correlation coefficients, proportions, odds and risk ratios, and Cronbach's alpha. We derive general formulations for integral back-transformations and corresponding CIs that are applicable across different transformation functions and provide a software implementation. Although required to obtain correct mean estimates, these approaches must be used with caution, as they are sensitive to heterogeneity estimation and can be unstable for unbounded transformations. Certain asymmetric transformations can also lead to inconsistent inference results. We illustrate these issues using analytical considerations and re-analyses of several meta-analyses. Importantly, we stress that the choice of back-transformation depends on the analyst's goals. The standard inverse back-transformation remains well suited for descriptive purposes and is often preferable in practice, provided that it is correctly interpreted as a back-transformed median effect size. We recommend using the integral back-transformation only when a mean estimate is explicitly required, and restricting its use for CIs to cases where they are demanded, but not for hypothesis testing.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-14"},"PeriodicalIF":6.1,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147939549","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":"Meta-regression with categorical moderators and dependent effect sizes: A simulation study.","authors":"Belén Fernández-Castilla, José Antonio López-López, María Rubio-Aparicio","doi":"10.1017/rsm.2026.10092","DOIUrl":"https://doi.org/10.1017/rsm.2026.10092","url":null,"abstract":"<p><p>Categorical moderators are often found in meta-analysis and examined using meta-regression models. When multiple effect sizes are present within studies, several methods can be used for meta-regression: multivariate models, three-level models, correlated-effects models with robust variance estimation (RVE), three-level models with RVE, and correlated-effects models with RVE and cluster wild bootstrapping (CWB). This study aimed to compare the performance of these methods through a simulation study. Cohen's <i>d</i> values were generated under a multivariate model, incorporating a binary variable that could represent either study-level or effect size-level characteristics. When the moderator referred to an effect size-level characteristic, its effect was allowed to vary across studies. Factors manipulated in the simulation included number of studies, number of outcomes per study, and the distribution of effect sizes across the categories of the moderator variable, ranging from balanced to highly unbalanced. The methods were applied and compared in terms of bias, Type I error, and power. The results showed that all methods exhibited lower power to detect effects when the moderator variable referred to study-level characteristics and the effect size distribution was very unbalanced. Methods based on RVE (correlated-effects with RVE or with RVE and CWB, and three-level models with RVE) effectively controlled Type I error rates but tended to be overconservative. In contrast, three-level models achieved higher power but at the cost of inflated Type I error. The best balance between Type I error control and power was observed when using a combination of three-level models and RVE.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-27"},"PeriodicalIF":6.1,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147925027","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":"Barriers to searching for grey literature in systematic reviews: A survey of authors and editors.","authors":"Jane O'Sullivan, Sarah Dawson, Julian Piers Thomas Higgins","doi":"10.1017/rsm.2026.10087","DOIUrl":"https://doi.org/10.1017/rsm.2026.10087","url":null,"abstract":"<p><p>To explore the perceptions of, and barriers to, grey literature searching among medical researchers and journal editors. A cross-sectional survey of authors of systematic reviews and the editors of the journals in which the reviews were published. Systematic reviews indexed in MEDLINE, spanning a 4-week period in 2019. We excluded protocols. We asked whether the reviewers were performing a grey literature search. If they were, we asked about their approach to the grey literature search and relevant guidance. If they were not, we asked about their rationale for this. We elucidated understandings of grey literature from all reviewers. The survey to journal editors asked about their perceptions towards grey literature. A consecutive sample of 1,229 systematic reviews was included. A total of 155 authors responded, and a total of 46 journal editors responded. The majority (57%) of reviewers reported performing a grey literature search. However, there was no consensus on types of grey literature items or sources among the reviewers. The most frequent barrier to grey literature searches was concern about the quality/detail of the grey literature items. Editors expressed negative perceptions towards grey literature, rooted in suspicion of its quality. While many reviewers reported performing a grey literature search, there was a diverse understanding of the term grey literature. Concerns about the quality of grey literature exist among reviewers and editors alike. There is a marked discrepancy between the best-practice guidelines and the gatekeepers of journals.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-13"},"PeriodicalIF":6.1,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147872773","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":"How comprehensive is comprehensive? Prevalence of searches beyond peer-reviewed journal articles and bibliographic sources: a cross-sectional study of systematic reviews.","authors":"Jane O'Sullivan, Sarah Dawson, Chris Cooper, Julian Piers Thomas Higgins","doi":"10.1017/rsm.2026.10086","DOIUrl":"https://doi.org/10.1017/rsm.2026.10086","url":null,"abstract":"<p><p>To examine the extent to which information sources other than journal articles are sought for systematic reviews. Cross-sectional study of published systematic reviews. We examined all published systematic reviews included in MEDLINE in a 4-week period in 2019. Both systematic reviews and protocols of reviews were eligible for inclusion. (1) Number and types of information sources sought in systematic reviews; (2) proportion of reviews that explicitly searched for study reports other than journal articles; (3) proportion of reviews that searched resources containing study reports other than journal articles. A total of 1,262 systematic reviews fulfilled the eligibility criteria. The median number of information resources searched for all systematic reviews was 4. Of the 1,262 reviews, study reports other than journal articles were sought in 40% (<i>n</i> = 502) of systematic reviews (97% (<i>n</i> = 64) of Cochrane reviews and 37% (<i>n</i> = 438) of non-Cochrane reviews). Trial registers were searched in 88% of Cochrane reviews and 21% of non-Cochrane reviews. In 99.3% (<i>n</i> = 1,253) of all the systematic reviews, the searches performed had the potential to identify study reports other than journal articles. Between a third and a half of systematic reviews search for study reports other than journal articles. Systematic review searches often search resources that include study reports other than journal articles, whether or not the reviewers explicitly sought them.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-11"},"PeriodicalIF":6.1,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147831576","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":"Evaluation of the replicability of systematic reviews with meta-analyses of the effects of health interventions.","authors":"Daniel G Hamilton, Joanne E McKenzie, Phi-Yen Nguyen, Melissa L Rethlefsen, Steve McDonald, Sue E Brennan, Fiona M Fidler, Julian P T Higgins, Raju Kanukula, Sathya Karunananthan, Lara J Maxwell, David Moher, Shinichi Nakagawa, David Nunan, Peter Tugwell, Vivian A Welch, Matthew J Page","doi":"10.1017/rsm.2025.10064","DOIUrl":"10.1017/rsm.2025.10064","url":null,"abstract":"<p><p>Systematic reviews are often characterized as being inherently replicable, but several studies have challenged this claim. The objective of the study was to investigate the variation in results following independent replication of literature searches and meta-analyses of systematic reviews. We included 10 systematic reviews of the effects of health interventions published in November 2020. Two information specialists repeated the original database search strategies. Two experienced review authors screened full-text articles, extracted data, and calculated the results for the first reported meta-analysis. All replicators were initially blinded to the results of the original review. A meta-analysis was considered not 'fully replicable' if the original and replicated summary estimate or confidence interval width differed by more than 10%, and meaningfully different if there was a difference in the direction or statistical significance. The difference between the number of records retrieved by the original reviewers and the information specialists exceeded 10% in 25/43 (58%) searches for the first replicator and 21/43 (49%) searches for the second. Eight meta-analyses (80%, 95% CI: 49-96) were initially classified as not fully replicable. After screening and data discrepancies were addressed, the number of meta-analyses classified as not fully replicable decreased to five (50%, 95% CI: 24-76). Differences were classified as meaningful in one blinded replication (10%, 95% CI: 1-40) and none of the unblinded replications (0%, 95% CI: 0-28). The results of systematic review processes were not always consistent when their reported methods were repeated. However, these inconsistencies seldom affected summary estimates from meta-analyses in a meaningful way.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 3","pages":"538-556"},"PeriodicalIF":8.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13126212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697140","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":"Reporting and evaluation of assumptions and certainty of evidence in network meta-analyses.","authors":"Kansak Boonpattharatthiti, Kanyaphak Chueadi, Phiyanuch Thimkorn, Deborah M Caldwell, Nathorn Chaiyakunapruk, Teerapon Dhippayom","doi":"10.1017/rsm.2025.10045","DOIUrl":"10.1017/rsm.2025.10045","url":null,"abstract":"<p><p>Network meta-analysis (NMA) facilitates the comparison of multiple treatments by integrating both direct and indirect evidence. Applications of NMA in medical decision making have grown exponentially. However, the validity of NMA findings depends on key assumptions: homogeneity, transitivity, and consistency. A lack of consistent assessment of these assumptions potentially compromises the reliability of NMA outcomes. The objective of this study is to evaluate the extent to which researchers address NMA assumptions and report the assessment of evidence certainty in NMA publications. A total of 22,079 studies were identified from PubMed, Embase, and Cochrane CENTRAL (January 2010-August 2024). A sample of 393 NMAs was calculated to represent this population and randomly selected. Data on study characteristics, NMA assumptions, and the certainty of evidence were extracted and analyzed descriptively. Of the 393 NMAs, 71.8% were published between 2020 and 2024. Homogeneity was assessed in 300 (76.3%) NMAs, transitivity in 45 (11.5%) NMAs, and consistency in 265 (67.4%) NMAs. The certainty of evidence was assessed in 110 (28.0%) studies, predominantly using GRADE (71 NMAs; 18.1%) or CINeMA (29 NMAs; 7.4%). NMAs published in journals with high-impact factors more frequently evaluate these aspects than those published in low-impact journals. The assessment of NMA assumptions is inconsistently reported across studies, particularly for transitivity and consistency assumptions. Our findings highlight the need for standardized protocols or reporting guidelines to ensure these assessments are conducted and transparently reported.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 3","pages":"557-566"},"PeriodicalIF":8.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13126206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696871","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}