Journal of Clinical Epidemiology最新文献

{"title":"Comment on \"GRADE Concept Paper 9: Rationale and process for creating a GRADE Ontology\".","authors":"S Dhanya Dedeepya, Vaishali Goel, Nivedita Nikhil Desai","doi":"10.1016/j.jclinepi.2025.112023","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112023","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112023"},"PeriodicalIF":5.2,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Jurisdictional scans: methodological considerations for systematically analysing and comparing policy approaches across different jurisdictions.","authors":"K M Saif-Ur-Rahman, Kerry Waddell, John N Lavis, Nikita N Burke, Marie Tierney, Barbara Whelan, Declan Devane","doi":"10.1016/j.jclinepi.2025.112025","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112025","url":null,"abstract":"<p><strong>Background: </strong>Jurisdictional scans are used to inform policy by systematically comparing how different jurisdictions define problems, design policies, and implement strategies. They provide insights into policy options, implementation experiences, and gaps in preparedness, making them valuable tools for evidence-informed decision-making. However, no established methodological standards exist currently. This article provides an overview of the methodological considerations of conducting jurisdictional scans, drawing on the authors' methodological experience.</p><p><strong>Methods: </strong>We outline issues for consideration in conducting jurisdictional scans, drawing on our experience from a recent jurisdictional scan of public health preparedness mechanisms. Our methodological reflections are informed by established evidence synthesis principles, adapted to the unique features of a jurisdictional scan. A worked example illustrates key stages, including defining scope, searching, screening, data extraction, and synthesis.</p><p><strong>Results: </strong>Our experience highlights the importance of applying systematic approaches to maximise transparency, reproducibility, and credibility. We found that policy documents often lacked abstracts, standardised structures, or clear evidence use, making screening and extraction challenging. Iterative refinement of inclusion criteria, piloting of search strategies, keyword searching, and structured frameworks for data extraction were essential for achieving consistency. Importantly, while multiple forms of evidence (e.g., guidelines, modelling, evaluations) were cited in preparedness plans, the role of evidence in shaping decisions was often unclear, revealing a key limitation of current practice.</p><p><strong>Conclusion: </strong>With the growing importance of evidence-informed policymaking, there is an urgent need to establish robust methodological standards and reporting guidelines for jurisdictional scans. This paper provides methodological considerations for jurisdictional scans, offering practical guidance while recognising ongoing challenges. By clarifying the value, limitations, and distinct role of jurisdictional scans, we aim to strengthen their contribution to policy processes and support future methodological development. Future research is warranted to refine the methodological and reporting standards of the process while maintaining flexibility for different policy contexts.</p><p><strong>Plain language summary: </strong>Jurisdictional scans are a way to see how different countries, regions, or organisations handle the same problem. They help show what choices governments have, how plans work in real life, and where the weaknesses are. Jurisdictional scans gather information from official documents, rules, and reports to learn from what others are doing. Our paper explains issues to consider in how to do a jurisdictional scan. This includes choosing which places to look at, ","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112025"},"PeriodicalIF":5.2,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic reviews on the same topic are common but often fail to meet key methodological standards: a research-on-research study.","authors":"Wilfred Kwok, Titiane Dallant, Guillaume Martin, Gabriel Fournier, Blandine Kervennic, Ophélie Pingeon, Agnès Dechartres","doi":"10.1016/j.jclinepi.2025.112018","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112018","url":null,"abstract":"<p><strong>Objectives: </strong>To assess 1)the frequency of overlapping systematic reviews (SRs) on the same topic including overlap in outcomes, 2)whether SRs meet key methodological characteristics and 3)to describe discrepancies in results.</p><p><strong>Study design and setting: </strong>For this research-on-research study, we gathered a random sample of SRs with meta-analysis (MA) published in 2022, identified the questions they addressed and, for each question, searched all SRs with MA published from 2018 to 2023 to assess the frequency of overlap. We assessed whether SRs met a minimum set of 6 key methodological characteristics: protocol registration, search of major electronic databases, search of trial registries, double selection and extraction, use of the Cochrane Risk-of-Bias tool and GRADE assessment.</p><p><strong>Results: </strong>From a sample of 107 SRs with MA published in 2022, we extracted 105 different questions and identified 123 other SRs with MA published from 2018 to 2023. There were overlapping SRs for 33 questions (31.4%, 95% CI: 22.9-41.3), with a median of three overlapping SRs per question (interquartile range 2-6; range 2-19). Of the 230 SRs, 15 (6.5%) met the minimum set of 6 key methodological characteristics, and 12 (11.4%) questions had at least one SR meeting this criterion. Among the 33 questions with overlapping SRs, for 7 (21.2%), the SRs had discrepant results.</p><p><strong>Conclusions: </strong>One-third of the SRs published in 2022 had at least one overlapping SR published from 2018 to 2023, and most did not meet a minimum set of methodological standards. For one-fifth of the questions, overlapping SRs provided discrepant results.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112018"},"PeriodicalIF":5.2,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145330918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic Reviews and Meta-Analysis: Continued Failure to Achieve Research Integrity.","authors":"Howard Bauchner","doi":"10.1016/j.jclinepi.2025.112017","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112017","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112017"},"PeriodicalIF":5.2,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145310051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In humble defence of unexplainable black box prediction models in healthcare.","authors":"Florien S van Royen, Hilde J P Weerts, Anne A H de Hond, Geert-Jan Geersing, Frans H Rutten, Karel G M Moons, Maarten van Smeden","doi":"10.1016/j.jclinepi.2025.112013","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112013","url":null,"abstract":"<p><p>The increasing complexity of prediction models for healthcare purposes - whether developed with or without artificial intelligence (AI) techniques - drives the urge to open complex 'black box' models using eXplainable AI (XAI) techniques. In this paper, we argue that XAI may not necessarily provide insights relevant to decision-making in the medical setting and can lead to misplaced trust and misinterpretation of the model's usability. An important limitation of XAI is the difficulty in avoiding causal interpretation, which may result in confirmation bias or false dismissal of the model when explanations conflict with clinical knowledge. Rather than expecting XAI to generate trust in black box prediction models to patients and healthcare providers, trust should be grounded in rigorous prediction model validations and model impact studies assessing the model's effectiveness on medical shared decision-making. In this paper, we therefore humbly defend the 'unexplainable' prediction models in healthcare.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112013"},"PeriodicalIF":5.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Impact of Unpublished Data on Network Meta-Analysis Outcomes in Outpatient Adults with Acute Migraine: A Study within a Review.","authors":"A Dobrescu, B Nussbaumer-Streit, G Wagner, A Sharifan, A Gadinger, I Klerings, C Nowak, G Gartlehner","doi":"10.1016/j.jclinepi.2025.112014","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112014","url":null,"abstract":"<p><strong>Background: </strong>Methodological guidance recommends including both published and unpublished data in systematic reviews to enhance reliability and reduce potential bias.</p><p><strong>Objective: </strong>To evaluate the impact of unpublished data from double-blind, pharmacologic randomized controlled trials on the results of network meta-analysis (NMA) of migraine treatments.</p><p><strong>Methods: </strong>We supplemented the search of a recent systematic review with targeted searches for unpublished data on ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform, regulatory agency reports, The Preprints Citation Index, Europe PMC, and Embase.com, and conference abstracts from the past five years. Two independent reviewers selected eligible studies, verified publication status, extracted data, and reassessed certainty of evidence (COE). We reproduced the original NMA including unpublished data for four dichotomous outcomes. We compared our results with the original NMA in terms of risk ratio (RR), absolute risk difference (ARD) with 95% confidence interval (CI), COE assessments, and conclusions.</p><p><strong>Results: </strong>Seventeen (6,735 participants) out of 37 eligible unpublished trials had posted results and were analyzed, with most (59%) identified via trial registries. Additionally, unpublished outcome data were retrieved for four published trials from the original analysis. These unpublished trials added two previously unrepresented interventions to the NMA, increasing the number of direct comparisons and closed loops. Comparisons of RRs (95% CI) with the original analysis showed all effects maintained the same direction, though six CIs newly crossed the null effect (RR = 1). Among 144 COE assessments, four changed meaningfully: one was rated down from high to moderate due to imprecision, and three were rated up from very low/no evidence to low COE based on new direct evidence. Overall conclusions remained unchanged after including unpublished data.</p><p><strong>Conclusions: </strong>In this case study, adding unpublished data had minimal impact on results and conclusions, with only minor changes in network geometry and COE.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112014"},"PeriodicalIF":5.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological and Reporting Rigor in Non-Inferiority and Equivalence Trials for Multimodality Cancer Treatment: Lessons from Breast Cancer Radiotherapy.","authors":"Shi-Jia Wang, Chun-Nan Liu, Yu Tang, Hao Jing, Hui Fang, Yi-Rui Zhai, Si-Ye Chen, Guang-Yi Sun, Xu-Ran Zhao, Yu-Chun Song, Yong-Wen Song, Yue-Ping Liu, Bo Chen, Shu-Nan Qi, Yuan Tang, Ning-Ning Lu, Wen-Wen Zhang, Julia R White, Ye-Xiong Li, Shu-Lian Wang, Chen Hu","doi":"10.1016/j.jclinepi.2025.112012","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112012","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the methodological and reporting quality of non-inferiority (NI) or equivalence trials in breast cancer radiotherapy, providing suggestions for future NI/equivalence trials.</p><p><strong>Study design and setting: </strong>Prospective phase III randomized controlled trials comparing different radiation modalities in breast cancer and designed as NI/equivalence were identified. Extracted data included the trial design, analysis, and reporting characteristics. The relationship between trial numbers and publication year was assessed. Trials with pre-specified NI margins as absolute risk differences (ARDs) were reevaluated using margins as relative risks.</p><p><strong>Results: </strong>Twenty-one studies were included. The number of publications increased over time. Trial interventions mainly involved dose fractionation and radiation volume. The primary endpoint was local or locoregional recurrence in 15, toxicity in 5, and both in 1 study. Reporting gaps included: Not specifying the trial as NI/equivalence in the title/abstract (n = 6); inadequate justification for the NI/equivalence design rationale (n = 10) or margins (n = 12); absence of both intention-to-treat and per-protocol analysis (n = 12); no reporting of p values for NI/equivalence tests (n = 12) or margins with confidence intervals (n = 5). Fifteen studies failed to meet their planned accrual target, mostly owing to overestimation of event rates in the control group. Among 8 trials with 9 comparisons claiming NI with pre-specified margins as ARDs, 4 comparisons were classified as inconclusive when using the margins as relative risks.</p><p><strong>Conclusion: </strong>Recently, NI/equivalence trials have dramatically increased in breast cancer radiotherapy; however, there is substantial room for improvement in their methodological and reporting quality.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112012"},"PeriodicalIF":5.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping review identified additional considerations for defining estimands in cluster randomised trials.","authors":"Dongquan Bi, Andrew Copas, Fan Li, Brennan C Kahan","doi":"10.1016/j.jclinepi.2025.112015","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112015","url":null,"abstract":"<p><strong>Objective: </strong>An estimand is a clear description of the treatment effect a study aims to quantify. The ICH E9(R1) addendum lists five attributes that should be described when defining an estimand. However, the addendum was primarily developed for individually randomised trials. Cluster randomised trials (CRTs), in which groups of individuals are randomised, have additional considerations for defining estimands, such as the population of clusters and how individuals and clusters are weighted. We aimed to identify a list of additional items that may need to be considered when defining estimands in CRTs.</p><p><strong>Study design and setting: </strong>We conducted a systematic search of multiple databases as well as the authors' personal libraries to identify articles that described an aspect of an estimand definition for CRTs which was not explicitly covered by one of the five attributes listed in the ICH E9 (R1) addendum. From this, we generated a list of items that may require consideration when defining estimands for CRTs beyond the five attributes listed in the ICH E9(R1) addendum.</p><p><strong>Results: </strong>From 46 eligible articles, we identified 8 items that may need to be considered when defining estimands in CRTs: (i) population of clusters; (ii) population of individuals under selection bias; (iii) exposure time of individuals/clusters on treatment; (iv) how individuals and clusters are weighted (e.g. individual-average vs. cluster-average); (v) whether summary measures are marginal or cluster-specific; (vi) strategies used to handle cluster-level intercurrent events; (vii) how interference/spillover is handled; and (viii) how individuals who leave or change clusters are handled.</p><p><strong>Conclusion: </strong>This review has identified additional items that may need to be considered when defining estimands for CRTs. Study investigators undertaking CRTs should consider these items when defining estimands for their trials, to ensure estimands are unambiguous and relevant for end-users such as clinicians, patients, and policy makers.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112015"},"PeriodicalIF":5.2,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reversing the burden of evidence: a needed evolution in GRADE to address publication bias.","authors":"A Gougeon, J C Lega, B Kassaï, F Gueyffier, R Boussageon, G Grenet","doi":"10.1016/j.jclinepi.2025.112010","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112010","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112010"},"PeriodicalIF":5.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply: Reversing the burden of evidence: a needed evolution in GRADE to address publication bias.","authors":"Holger J Schünemann, Elie A Akl, Ignacio Neumann, Joerg J Meerpohl","doi":"10.1016/j.jclinepi.2025.112011","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.112011","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"112011"},"PeriodicalIF":5.2,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}