Research Synthesis Methods最新文献

{"title":"Conducting evidence synthesis and developing evidence-based advice in public health and beyond: A scoping review and map of methods guidance.","authors":"Ani Movsisyan, Kolahta Asres Ioab, Jan William Himmels, Gina Loretta Bantle, Andreea Dobrescu, Signe Flottorp, Frode Forland, Arianna Gadinger, Christina Koscher-Kien, Irma Klerings, Joerg J Meerpohl, Barbara Nussbaumer-Streit, Brigitte Strahwald, Eva A Rehfuess","doi":"10.1017/rsm.2025.10051","DOIUrl":"10.1017/rsm.2025.10051","url":null,"abstract":"<p><p>Effective public health decision-making relies on rigorous evidence synthesis and transparent processes to facilitate its use. However, existing methods guidance has primarily been developed within clinical medicine and may not sufficiently address the complexities of public health, such as population-level considerations, multiple evidence streams, and time-sensitive decision-making. This work contributes to the European Centre for Disease Prevention and Control initiative on methods guidance development for evidence synthesis and evidence-based public health advice by systematically identifying and mapping guidance from health and health-related disciplines.Structured searches were conducted across multiple scientific databases and websites of key institutions, followed by screening and data coding. Of the 17,386 records identified, 247 documents were classified as 'guidance products' providing a set of principles or recommendations on the overall process of developing evidence synthesis and evidence-based advice. While many were classified as 'generic' in scope, a majority originated from clinical medicine and focused on systematic reviews of intervention effects. Only 41 documents explicitly addressed public health. Key gaps included approaches for rapid evidence synthesis and decision-making and methods for synthesising evidence from laboratory research, disease burden, and prevalence studies.The findings highlight a need for methodological development that aligns with the realities of public health practice, particularly in emergency contexts. This review provides a key repository for methodologists, researchers, and decision-makers in public health, as well as clinical medicine and health care in Europe and worldwide, supporting the evolution of more inclusive and adaptable approaches to public health evidence synthesis and decision-making.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 2","pages":"240-264"},"PeriodicalIF":8.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12873621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111538","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":"Guidance for manuscript submissions testing the use of generative AI for systematic review and meta-analysis.","authors":"Oluwaseun Farotimi, Adam Dunn, Caspar J Van Lissa, Joshua Richard Polanin, Dimitris Mavridis, Terri D Pigott","doi":"10.1017/rsm.2025.10058","DOIUrl":"10.1017/rsm.2025.10058","url":null,"abstract":"","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 2","pages":"237-239"},"PeriodicalIF":6.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12873612/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111552","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":"Shiny-MAGEC: A Bayesian R shiny application for meta-analysis of censored adverse events.","authors":"Zihan Zhou, Zizhong Tian, Christine Peterson, Le Bao, Shouhao Zhou","doi":"10.1017/rsm.2025.10052","DOIUrl":"10.1017/rsm.2025.10052","url":null,"abstract":"<p><p>Accurate assessment of adverse event (AE) incidence is critical in clinical research for drug safety. While meta-analysis serves as an essential tool to comprehensively synthesize the evidence across multiple studies, incomplete AE reporting in clinical trials remains a persistent challenge. In particular, AEs occurring below study-specific reporting thresholds are often omitted from publications, leading to left-censored data. Failure to account for these censored AE counts can result in biased AE incidence estimates. We present an R Shiny application that implements a Bayesian meta-analysis model specifically designed to incorporate censored AE data into the estimation process. This interactive tool provides a user-friendly interface for researchers to conduct AE meta-analyses and estimate the AE incidence probability using an unbiased approach. It also enables direct comparisons between models that either incorporate or ignore censoring, highlighting the biases introduced by conventional approaches. This tutorial demonstrates the Shiny application's functionality through an illustrative example on meta-analysis of PD-1/PD-L1 inhibitor safety and highlights the importance of this tool in improving AE risk assessment. Ultimately, the new Shiny app facilitates more accurate and transparent drug safety evaluations. The Shiny-MAGEC app is available at: https://zihanzhou98.shinyapps.io/Shiny-MAGEC/.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 2","pages":"378-388"},"PeriodicalIF":8.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12873611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146111634","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":"Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria.","authors":"Theodoros Evrenoglou, Adriani Nikolakopoulou, Guido Schwarzer, Gerta Rücker, Anna Chaimani","doi":"10.1017/rsm.2026.10071","DOIUrl":"10.1017/rsm.2026.10071","url":null,"abstract":"<p><p>A key output of network meta-analysis (NMA) is the relative ranking of treatments; nevertheless, it has attracted substantial criticism. Existing ranking methods often lack clear interpretability and fail to adequately account for uncertainty, overemphasizing small differences in treatment effects. We propose a novel framework to estimate treatment hierarchies in NMA using a probabilistic model, focusing on a clinically relevant treatment-choice criterion (TCC). Initially, we define a TCC based on smallest worthwhile differences (SWD), converting NMA relative treatment effects into treatment preference format. These data are then synthesized using a probabilistic ranking model, assigning each treatment a latent \"ability\" parameter, representing its propensity to yield clinically important and beneficial true treatment effects relative to the rest of the treatments in the network. Parameter estimation relies on the maximum likelihood theory, with standard errors derived asymptotically from the Hessian matrix. To facilitate the use of our methods, we launched the R package mtrank. We applied our method to two clinical datasets: one comparing 18 antidepressants for major depression and another comparing 6 antihypertensives for the incidence of diabetes. Our approach provided robust, interpretable treatment hierarchies that account for a concrete TCC. We further examined the agreement between the proposed method and existing ranking metrics in 153 published networks, concluding that the degree of agreement depends on the precision of the NMA estimates. Our framework offers a valuable alternative for NMA treatment ranking, mitigating overinterpretation of minor differences. This enables more reliable and clinically meaningful treatment hierarchies.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-20"},"PeriodicalIF":8.0,"publicationDate":"2026-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224651","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":"Authorship network bias in meta-analysis.","authors":"Marvin Rieck, Anne-Christine Mupepele, Carsten F Dormann","doi":"10.1017/rsm.2025.10063","DOIUrl":"10.1017/rsm.2025.10063","url":null,"abstract":"<p><p>1. Meta-analyses are a reliable method for a quantitative research synthesis. They are, however, prone to specific biases that can be introduced in the process. Such a bias could exist if primary literature produces similar results if coming from the same authors. Authorship network bias is the non-independence of effect sizes introduced by the overlap of authors of primary studies. If not accounted for, it can severely impact the quality of meta-analysis and the conclusions drawn from it.2. To account for such non-independence, multilevel models with author clusters as an additional hierarchy level were recently suggested. We propose a new method for the detection of non-independent effect sizes based on authorship networks and for their correction.3. An analysis of simulated data demonstrates the effectiveness of the here-suggested new method. We further applied our new method to nine exemplary meta-analyses.4. Our new method for detection and effective correction can be easily integrated in existing meta-analysis workflows, using the functionality already offered by R's metafor package.5. Our goal is to enhance the reliability of meta-analyses by highlighting potential authorship network bias and offering a method to address this often-overlooked bias.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-16"},"PeriodicalIF":8.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146211796","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":"Synthesis challenges in complex evidence: A critical analysis of systematic reviews of face mask efficacy.","authors":"Trisha Greenhalgh, Sahanika Ratnayake, Rebecca Helm, Luana Poliseli, Jon Williamson","doi":"10.1017/rsm.2026.10072","DOIUrl":"10.1017/rsm.2026.10072","url":null,"abstract":"<p><p>The evaluation of the role of face masks in preventing respiratory infections is a paradigm case in synthesising complex evidence (i.e. extensive, diverse, technically specialised, and with multilevel chains of causality). Primary studies have assessed different mask types, diseases, populations, and settings using different research designs. Numerous review teams have attempted to synthesise this literature, in which observational (case-control, cohort, cross-sectional) and ecological studies predominate. Their findings and conclusions vary widely.This article critically examines how 66 systematic reviews dealt with mask efficacy studies. Risk-of-bias tools produced unreliable assessments when-as was often the case-review teams lacked methodological expertise or topic-specific understanding. This was especially true when datasets were large and heterogeneous, with multiple biases playing out in different ways and requiring nuanced adjustments. In such circumstances, tools were sometimes used crudely and reductively rather than to support close reading of primary studies and guide expert judgments. Various moves by reviewers-excluding observational evidence altogether, assessing risk but not direction of biases, omitting distinguishing details of primary studies, and producing meta-analyses that combined studies of different designs or included studies at critical risk of bias-served to obscure important aspects of heterogeneity, resulting in bland and unhelpful summary statements.We draw on philosophy to question the formulaic use of generic risk-of-bias tools, especially when the primary evidence demands expert understanding and tailoring of study quality questions to the topic. We call for more rigorous training and oversight of reviewers of complex evidence and for new review methods designed specifically for such evidence.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":" ","pages":"1-20"},"PeriodicalIF":8.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13311340/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123007","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":"How to conduct an individual participant data meta-analysis in response to an emerging pathogen: Lessons learned from Zika and COVID-19.","authors":"Lauren Maxwell, Priya Shreedhar, Laura Merson, Brooke Levis, Thomas P A Debray, Valentijn Marnix Theodoor de Jong, Ricardo Arraes de Alencar Ximenes, Thomas Jaenisch, Paul Gustafson, Mabel Carabali","doi":"10.1017/rsm.2025.10029","DOIUrl":"10.1017/rsm.2025.10029","url":null,"abstract":"<p><p>Sharing, harmonizing, and analyzing participant-level data is of central importance in the rapid research response to emerging pathogens. Individual participant data meta-analyses (IPD-MAs), which synthesize participant-level data from related primary studies, have several advantages over pooling study-level effect estimates in a traditional meta-analysis. IPD-MAs enable researchers to more effectively separate spurious heterogeneity related to differences in measurement from clinically relevant heterogeneity from differences in underlying risk or distribution of factors that modify disease progression. This tutorial describes the steps needed to conduct an IPD-MA of an emerging pathogen and how IPD-MAs of emerging pathogens differ from those of well-studied exposures and outcomes. We discuss key statistical issues, including participant- and study-level missingness and complex measurement error, and present recommendations. We review how IPD-MAs conducted during the COVID-19 response addressed these statistical challenges when harmonizing and analyzing participant-level data related to an emerging pathogen. The guidance presented here is based on lessons learned in our conduct of IPD-MAs in the research response to emerging pathogens, including Zika virus and COVID-19.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"1-29"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823201/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103267","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":"Lord's Paradox and two network meta-analysis models.","authors":"Yu-Kang Tu, James S Hodges","doi":"10.1017/rsm.2025.10036","DOIUrl":"10.1017/rsm.2025.10036","url":null,"abstract":"<p><p>The contrast-based model (CBM) is the most popular network meta-analysis (NMA) method, although alternative approaches, e.g., the baseline model (BM), have been proposed but seldom used. This article aims to illuminate the difference between the CBM and BM and explores when they produce different results. These models differ in key assumptions: The CBM assumes treatment contrasts are exchangeable across trials and models the reference (baseline) treatment's outcome levels as fixed effects, while the BM further assumes that the baseline treatment's outcome levels are exchangeable across trials and treats them as random effects. We show algebraically and graphically that the difference between the CBM and BM is analogous to the difference between the two analyses in a statistical conundrum called Lord's Paradox, in which the <i>t</i>-test and analysis of covariance (ANCOVA) yield conflicting conclusions about the group difference in weight gain. We show that this conflict arises because the <i>t</i>-test compares the <i>observed</i> weight change, whereas ANCOVA compares an <i>adjusted</i> weight change. In NMA, analogously, the CBM compares observed treatment contrasts, while the BM compares adjusted treatment contrasts. We demonstrate how the difference in modeling baseline effects can cause the CBM and BM to give different results. The analogy of Lord's Paradox provides insights into the different assumptions of the CBM and BM regarding the relationship between baseline effects and treatment contrasts. When these two models produce substantially different results, it may indicate a violation of the transitivity assumption. Therefore, we should be cautious in interpreting the results from either model.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"111-122"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103365","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":"Methods of multi-indication meta-analysis for health technology assessment: A simulation study.","authors":"David Glynn, Pedro Saramago, Janharpreet Singh, Sylwia Bujkiewicz, Sofia Dias, Steve Palmer, Marta Ferreira Oliveira Soares","doi":"10.1017/rsm.2025.10037","DOIUrl":"10.1017/rsm.2025.10037","url":null,"abstract":"<p><p>A growing number of oncology treatments, such as bevacizumab, are used across multiple indications. However, in health technology assessment (HTA), their clinical and cost-effectiveness are typically appraised within a single target indication. This approach excludes a broader evidence base across other indications. To address this, we explored multi-indication meta-analysis methods that share evidence across indications.We conducted a simulation study to evaluate alternative multi-indication synthesis models. This included univariate (mixture and non-mixture) methods synthesizing overall survival (OS) data and bivariate surrogacy models jointly modeling treatment effects on progression-free survival (PFS) and OS, pooling surrogacy parameters across indications. Simulated datasets were generated using a multistate disease progression model under various scenarios, including different levels of heterogeneity within and between indications, outlier indications, and varying data on OS for the target indication. We evaluated the performance of the synthesis models applied to the simulated datasets in terms of their ability to predict OS in a target indication.The results showed univariate multi-indication methods could reduce uncertainty without increasing bias, particularly when OS data were available in the target indication. Compared with univariate methods, mixture models did not significantly improve performance and are not recommended for HTA. In scenarios where OS data in the target indication is absent and there are also outlier indications, bivariate surrogacy models showed promise in correcting bias relative to univariate models, though further research under realistic conditions is needed.Multi-indication methods are more complex than traditional approaches but can potentially reduce uncertainty in HTA decisions.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"93-110"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103342","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":"Examining covariate-specific treatment effects in individual participant data meta-analysis: Framing aggregation bias in terms of trial-level confounding and funnel plots.","authors":"Lianne K Siegel, Joseph S Koopmeiners, Jamie Hartmann-Boyce, Peter J Godolphin, Abdel G Babiker, Giota Touloumi, Kirk U Knowlton, Richard D Riley","doi":"10.1017/rsm.2025.10043","DOIUrl":"10.1017/rsm.2025.10043","url":null,"abstract":"<p><p>To understand a treatment's potential impact at the individual level, it is crucial to explore whether the effect differs across patient subgroups and covariate values. Meta-analysis provides an important tool for detecting treatment-covariate interactions, as it can improve power compared to a single study. However, aggregation bias can occur when estimating individual-level treatment-covariate interactions in meta-analysis, due to trial-level confounding. This refers to when the association between the covariate and treatment effect <i>across</i> trials (at the aggregate level) differs from that observed <i>within</i> trials (at the individual level). It is, thus, recommended that heterogeneity in the treatment effect at the individual level should be disentangled from that at the trial level, ideally using an individual participant data (IPD) meta-analysis. Here, we explain this issue and provide new intuition about how trial-level confounding is impacted by differences in within-trial distributions of covariates and how this corresponds to asymmetry in subgroup-specific funnel plots in the case of categorical covariates. We then propose a sensitivity analysis to assess the robustness of interaction estimates to potential trial-level confounding. We illustrate these concepts using simulated and real data from an IPD meta-analysis of trials conducted on the TICO/ACTIV-3 platform, which assessed passive immunotherapy treatments for inpatients with COVID-19.</p>","PeriodicalId":226,"journal":{"name":"Research Synthesis Methods","volume":"17 1","pages":"194-209"},"PeriodicalIF":8.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12823212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146103291","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}