Research Synthesis Methods最新文献

{"title":"Data analysis and presentation methods in umbrella reviews/overviews of reviews in health care: A cross-sectional study.","authors":"Cindy Stern, Jiaoli Li, Jennifer Stone, Hanan Khalil, Kim Sears, Romy Menghao Jia, Patraporn Bhatarasakoon, Edoardo Aromataris, Ritin Fernandez","doi":"10.1017/rsm.2025.10040","DOIUrl":"10.1017/rsm.2025.10040","url":null,"abstract":"<p><p>Umbrella reviews (URs) synthesize findings from multiple systematic reviews on a specific topic. Methodological approaches for analyzing and presenting UR results vary, and reviewers often adapt methods to align with research objectives. This study examined the characteristics of analysis and presentation methods used in healthcare-related URs. A systematic PubMed search identified URs published between 2023 and 2024. Inclusion criteria focused on healthcare URs using systematic reviews as the unit of analysis. A random sample of 100 eligible URs was included. A customized, piloted data extraction form was used to collect bibliographic, conduct, and reporting data independently. Descriptive analysis and narrative synthesis summarized findings. The most common terminology for eligible studies was \"umbrella reviews\" (65%) or \"overviews\" (30%). Question frameworks included PICO (43%) and PICOS (14%), with quantitative systematic reviews included in most URs (98%), and 68% including randomized controlled trials. The most frequent methodological guidance source was Cochrane (32%). Data analysis commonly used narrative synthesis and meta-analysis, with Stata, RevMan, and GRADEPro GDT employed for presentation. Information about study overlap and certainty assessment was rarely reported.Variation exists in how data are analyzed and presented in URs, with key elements often omitted. These findings highlight the need for clearer methodological guidance to enhance consistency and reporting in future URs.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"210-224"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823197/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103288","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":"Making sense of conducting a critical interpretive synthesis: A scoping review.","authors":"Saritte Perlman, Eliana Ben-Sheleg, Moriah E Ellen","doi":"10.1017/rsm.2025.10041","DOIUrl":"10.1017/rsm.2025.10041","url":null,"abstract":"<p><p>Critical interpretive synthesis was introduced in 2006 to address various shortcomings of systematic reviews such as their limitations in synthesizing heterogeneous data, integrating diverse study types, and generating theoretical insights. This review sought to outline the methodological process of conducting critical interpretive syntheses by identifying the methods currently in use, mapping the processes that have been used to date, and highlighting directions for further research. To achieve this, a scoping review of critical interpretive syntheses published between 2006 and 2023 was conducted. Initial searches identified 1628 publications and after removal of duplicates and exclusions, 212 reviews were included in the study. Most reviews focused on health-related subjects. Authors chose to utilize the method due to its iterative, inductive, and recursive nature. Both question-based and topic-based reviews were conducted. Literature searches relied on electronic databases and reference chaining. Mapping to the original six-phase model showed most variability in use of sampling and quality assessment phases, which were each done in 50.7% of reviews. Data extraction utilized a data extraction table. Synthesis involved constant comparison, critique, and consolidation of themes into constructs, and a synthesizing argument. Refining critical interpretive synthesis methodology and its best practices are important for optimizing the utility and impact and ensuring findings are relevant and actionable for informing policy, practice, and future research.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"30-41"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823206/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103421","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":"Estimands and their implications for evidence synthesis for oncology: A simulation study of treatment switching in meta-analysis.","authors":"Rebecca Kathleen Metcalfe, Antonio Remiro-Azócar, Quang Vuong, Anders Gorst-Rasmussen, Oliver Keene, Shomoita Alam, Jay J H Park","doi":"10.1017/rsm.2025.10039","DOIUrl":"10.1017/rsm.2025.10039","url":null,"abstract":"<p><p>The ICH E9(R1) addendum provides guidelines on accounting for intercurrent events in clinical trials using the estimands framework. However, there has been limited attention to the estimands framework for meta-analysis. Using treatment switching, a well-known intercurrent event that occurs frequently in oncology, we conducted a simulation study to explore the bias introduced by pooling together estimates targeting different estimands in a meta-analysis of randomized clinical trials (RCTs) that allowed treatment switching. We simulated overall survival data of a collection of RCTs that allowed patients in the control group to switch to the intervention treatment after disease progression under fixed effects and random effects models. For each RCT, we calculated effect estimates for a treatment policy estimand that ignored treatment switching, and a hypothetical estimand that accounted for treatment switching either by fitting rank-preserving structural failure time models or by censoring switchers. Then, we performed random effects and fixed effects meta-analyses to pool together RCT effect estimates while varying the proportions of trials providing treatment policy and hypothetical effect estimates. We compared the results of meta-analyses that pooled different types of effect estimates with those that pooled only treatment policy or hypothetical estimates. We found that pooling estimates targeting different estimands results in pooled estimators that do not target any estimand of interest, and that pooling estimates of varying estimands can generate misleading results, even under a random effects model. Adopting the estimands framework for meta-analysis may improve alignment between meta-analytic results and the clinical research question of interest.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"170-193"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12824772/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103238","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":"Developing an approach for assigning GRADE levels in a systematic overview of reviews of diagnostic test accuracy using general principles identified from current GRADE guidelines: A case study.","authors":"Andrew Dullea, Lydia O'Sullivan, Kirsty K O'Brien, Patricia Harrington, Marie Carrigan, Susan Ahern, Maeve McGarry, Karen Cardwell, Michelle O'Neill, Kieran A Walsh, Barbara Clyne, Susan M Smith, Mairin Ryan","doi":"10.1017/rsm.2025.10047","DOIUrl":"10.1017/rsm.2025.10047","url":null,"abstract":"<p><p>Existing guidelines on overviews of reviews and umbrella reviews recommend an assessment of the certainty of evidence, but provide limited guidance on 'how to' apply the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to such a complex evidence synthesis. We share our experience of developing a 'general principles' approach to applying GRADE to a complex overview of reviews. The approach was developed in an iterative and exploratory manner during the planning and conduct of an overview of reviews of a novel molecular imaging technique for the staging of prostate cancer, involving a formal review by a group of 11 methodologists/health services researchers. This approach was developed during the evidence synthesis process, piloted, and then applied to our ongoing overview of reviews. A 'general principles' approach of applying the domains of GRADE to an overview of reviews and arriving at an overall summary judgement for each outcome is presented. Our approach details additional factors to consider, including addressing both the primary study risk of bias as assessed by the included reviews and the risk of bias of the systematic reviews themselves, as well as the statistical heterogeneity observed in meta-analyses conducted within the included reviews. Our approach distilled key principles from the relevant GRADE guidelines and allowed us to apply GRADE to a complex body of evidence in a consistent and transparent way. The approach taken and the methods used to develop our approach may inform researchers working on overviews of reviews, umbrella reviews, or future methodological guidelines.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"225-236"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823199/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103316","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":"Exploring the methodological quality and risk of bias in 200 systematic reviews: A comparative study of ROBIS and AMSTAR-2 tools.","authors":"Carole Lunny, Nityanand Jain, Tina Nazari, Melodi Kosaner-Kließ, Lucas Santos, Ian Goodman, Alaa A M Osman, Stefano Berrone, Mohammad Najm Dadam, Connor T A Brenna, Heba Hussein, Gioia Dahdal, Diana Cespedes A, Nicola Ferri, Salmaan Kanji, Yuan Chi, Dawid Pieper, Beverly Shea, Amanda Parker, Dipika Neupane, Paul A Khan, Daniella Rangira, Kat Kolaski, Ben Ridley, Amina Berour, Kevin Sun, Radin Hamidi Rad, Zihui Ouyang, Emma K Reid, Iván Pérez-Neri, Sanabel O Barakat, Silvia Bargeri, Silvia Gianola, Greta Castellini, Sera Whitelaw, Adrienne Stevens, Shailesh B Kolekar, Kristy Wong, Paityn Major, Ebrahim Bagheri, Andrea C Tricco","doi":"10.1017/rsm.2025.10032","DOIUrl":"10.1017/rsm.2025.10032","url":null,"abstract":"<p><p>AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews, version 2) and ROBIS are tools used to assess the methodological quality and the risk of bias in a systematic review (SR). We applied AMSTAR-2 and ROBIS to a sample of 200 published SRs. We investigated the overlap in their methodological constructs, responses by item, and overall, percentage agreement, direction of effect, and timing of assessments. AMSTAR-2 contains 16 items and ROBIS 24 items. Three items in AMSTAR-2 and nine in ROBIS did not overlap in construct. Of the 200 SRs, 73% were low or critically low quality using AMSTAR-2, and 81% had a high risk of bias using ROBIS. The median time to complete AMSTAR-2 and ROBIS was 51 and 64 minutes, respectively. When assessment times were calibrated to the number of items in each tool, each item took an average of 3.2 minutes per item for AMSTAR-2 compared to 2.7 minutes for ROBIS. Nine percent of SRs had opposing ratings (i.e., AMSTAR-2 was high quality while ROBIS was high risk). In both tools, three-quarters of items showed more than 70% agreement between raters after extensive training and piloting. AMSTAR-2 and ROBIS provide complementary rather than interchangeable assessments of systematic reviews. AMSTAR-2 may be preferable when efficiency is prioritized and methodological rigour is the focus, whereas ROBIS offers a deeper examination of potential biases and external validity. Given the widespread reliance on systematic reviews for policy and practice, selecting the appropriate appraisal tool remains crucial. Future research should explore strategies to integrate the strengths of both instruments while minimizing the burden on assessors.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"63-92"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103330","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":"What can we learn from 1,000 meta-analyses across 10 different disciplines?","authors":"Weilun Wu, Jianhua Duan, W Robert Reed, Elizabeth Tipton","doi":"10.1017/rsm.2025.10035","DOIUrl":"10.1017/rsm.2025.10035","url":null,"abstract":"<p><p>This study analyzes 1,000 meta-analyses drawn from 10 disciplines-including medicine, psychology, education, biology, and economics-to document and compare methodological practices across fields. We find large differences in the size of meta-analyses, the number of effect sizes per study, and the types of effect sizes used. Disciplines also vary in their use of unpublished studies, the frequency and type of tests for publication bias, and whether they attempt to correct for it. Notably, many meta-analyses include multiple effect sizes from the same study, yet fail to account for statistical dependence in their analyses. We document the limited use of advanced methods-such as multilevel models and cluster-adjusted standard errors-that can accommodate dependent data structures. Correlations are frequently used as effect sizes in some disciplines, yet researchers often fail to address the methodological issues this introduces, including biased weighting and misleading tests for publication bias. We also find that meta-regression is underutilized, even when sample sizes are large enough to support it. This work serves as a resource for researchers conducting their first meta-analyses, as a benchmark for researchers designing simulation experiments, and as a reference for applied meta-analysts aiming to improve their methodological practices.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"123-156"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823205/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103368","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":"Automating the data extraction process for systematic reviews using GPT-4o and o3.","authors":"Yuki Kataoka, Tomohiro Takayama, Keisuke Yoshimura, Ryuhei So, Yasushi Tsujimoto, Yosuke Yamagishi, Shiro Takagi, Yuki Furukawa, Masatsugu Sakata, Đorđe Bašić, Andrea Cipriani, Pim Cuijpers, Eirini Karyotaki, Mathias Harrer, Stefan Leucht, Ava Homiar, Edoardo G Ostinelli, Clara Miguel, Alessandro Rodolico, Toshi A Furukawa","doi":"10.1017/rsm.2025.10030","DOIUrl":"10.1017/rsm.2025.10030","url":null,"abstract":"<p><p>Large language models have shown promise for automating data extraction (DE) in systematic reviews (SRs), but most existing approaches require manual interaction. We developed an open-source system using GPT-4o to automatically extract data with no human intervention during the extraction process. We developed the system on a dataset of 290 randomized controlled trials (RCTs) from a published SR about cognitive behavioral therapy for insomnia. We evaluated the system on two other datasets: 5 RCTs from an updated search for the same review and 10 RCTs used in a separate published study that had also evaluated automated DE. We developed the best approach across all variables in the development dataset using GPT-4o. The performance in the updated-search dataset using o3 was 74.9% sensitivity, 76.7% specificity, 75.7 precision, 93.5% variable detection comprehensiveness, and 75.3% accuracy. In both datasets, accuracy was higher for string variables (e.g., country, study design, drug names, and outcome definitions) compared with numeric variables. In the third external validation dataset, GPT-4o showed a lower performance with a mean accuracy of 84.4% compared with the previous study. However, by adjusting our DE method, while maintaining the same prompting technique, we achieved a mean accuracy of 96.3%, which was comparable to the previous manual extraction study. Our system shows potential for assisting the DE of string variables alongside a human reviewer. However, it cannot yet replace humans for numeric DE. Further evaluation across diverse review contexts is needed to establish broader applicability.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"42-62"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103253","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":"Incorporating the possibility of cure into network meta-analyses: A case study from resected Stage III/IV melanoma.","authors":"Keith Chan, Sarah Goring, Kabirraaj Toor, Murat Kurt, Andriy Moshyk, Jeroen Jansen","doi":"10.1017/rsm.2025.10038","DOIUrl":"10.1017/rsm.2025.10038","url":null,"abstract":"<p><p>In many areas of oncology, cancer drugs are now associated with long-term survivorship and mixture cure models (MCM) are increasingly being used for survival analysis. The objective of this article was to propose a methodology for conducting network meta-analysis (NMA) of MCM. This method was illustrated through a case study evaluating recurrence-free survival (RFS) with adjuvant therapy for stage III/IV resected melanoma. For the case study, the MCM NMA was conducted by: (1) fitting MCMs to each trial included within the network of evidence; and (2) incorporating the parameters of the MCMs into a multivariate NMA. Outputs included relative effect estimates for the MCM NMA as well as absolute estimates of survival (RFS), modeled within the Bayesian multivariate NMA, by incorporating absolute baseline effects of the reference treatment. The case study was intended for illustrative purposes of the MCM NMA methodology and is not meant for clinical interpretation. The case study demonstrated the feasibility of conducting an MCM NMA and highlighted key issues and considerations when conducting such analyses, including plausibility of cure, maturity of data, process for model selection, and the presentation and interpretation of results. MCM NMA provides a method of comparative survival that acknowledges the benefit newer treatments may confer on a subset of patients, resulting in long-term survival and reflection of this survival in extrapolation. In the future, this method may provide an additional metric to compare treatments that is of value to patients.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"157-169"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103392","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":"StudyTypeTeller-Large language models to automatically classify research study types for systematic reviews.","authors":"Simona Emilova Doneva, Shirin de Viragh, Hanna Hubarava, Stefan Schandelmaier, Matthias Briel, Benjamin Victor Ineichen","doi":"10.1017/rsm.2025.10031","DOIUrl":"10.1017/rsm.2025.10031","url":null,"abstract":"<p><p>screening, a labor-intensive aspect of systematic review, is increasingly challenging due to the rising volume of scientific publications. Recent advances suggest that generative large language models like generative pre-trained transformer (GPT) could aid this process by classifying references into study types such as randomized-controlled trials (RCTs) or animal studies prior to abstract screening. However, it is unknown how these GPT models perform in classifying such scientific study types in the biomedical field. Additionally, their performance has not been directly compared with earlier transformer-based models like bidirectional encoder representations from transformers (BERT). To address this, we developed a human-annotated corpus of 2,645 PubMed titles and abstracts, annotated for 14 study types, including different types of RCTs and animal studies, systematic reviews, study protocols, case reports, as well as in vitro studies. Using this corpus, we compared the performance of GPT-3.5 and GPT-4 in automatically classifying these study types against established BERT models. Our results show that fine-tuned pretrained BERT models consistently outperformed GPT models, achieving F1-scores above 0.8, compared to approximately 0.6 for GPT models. Advanced prompting strategies did not substantially boost GPT performance. In conclusion, these findings highlight that, even though GPT models benefit from advanced capabilities and extensive training data, their performance in niche tasks like scientific multi-class study classification is inferior to smaller fine-tuned models. Nevertheless, the use of automated methods remains promising for reducing the volume of records, making the screening of large reference libraries more feasible. Our corpus is openly available and can be used to harness other natural language processing (NLP) approaches.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"16 6","pages":"1005-1024"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12657658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103290","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":"NMAsurv: An R Shiny application for network meta-analysis based on survival data.","authors":"Taihang Shao, Mingye Zhao, Fenghao Shi, Mingjun Rui, Wenxi Tang","doi":"10.1017/rsm.2025.10020","DOIUrl":"10.1017/rsm.2025.10020","url":null,"abstract":"<p><p>Network meta-analysis (NMA) is becoming increasingly important, especially in the field of medicine, as it allows for comparisons across multiple trials with different interventions. For time-to-event data, that is, survival data, traditional NMA based on the proportional hazards (PH) assumption simply synthesizes reported hazard ratios (HRs). Novel methods for NMA based on the non-PH assumption have been proposed and implemented using R software. However, these methods often involve complex methodologies and require advanced programming skills, creating a barrier for many researchers. Therefore, we developed an R Shiny tool, NMAsurv (https://psurvivala.shinyapps.io/NMAsurv/). NMAsurv allows users with little or zero background in R to conduct survival-data-based NMA effortlessly. The tool supports various functions such as drawing network plots, testing the PH assumption, and building NMA models. Users can input either reconstructed pseudo-individual participant data or aggregated data. NMAsurv offers a user-friendly interface for extracting parameter estimations from various NMA models, including fractional polynomial, piecewise exponential models, parametric survival models, Cox PH model, and generalized gamma model. Additionally, it enables users to effortlessly create survival and HR plots. All operations can be performed by an intuitive \"point-and-click\" interface. In this study, we introduce all the functionalities and features of NMAsurv and demonstrate its application using a real-world NMA example.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"16 6","pages":"1042-1056"},"PeriodicalIF":8.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12657653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103224","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}