Journal of Clinical Epidemiology最新文献

{"title":"Carbon emissions associated with clinical trials: a scoping review","authors":"Frank You , Taylor Coffey , Daniel Powell , Paula R. Williamson , Katie Gillies","doi":"10.1016/j.jclinepi.2025.111733","DOIUrl":"10.1016/j.jclinepi.2025.111733","url":null,"abstract":"<div><h3>Objectives</h3><div>To review and synthesize available evidence on carbon emissions associated with clinical trials to inform future research on design and delivery of greener trials.</div></div><div><h3>Study Design and Setting</h3><div>We performed a scoping review by following the Joanna Briggs Institute guidance and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. A systematic search was conducted on MEDLINE (Ovid) from January 1, 2007, to April 15, 2024, with no geographic and language restrictions complemented by forward and backward citation analysis (snowballing). We included all types of research literature within the context of clinical trials reporting any aspect related to trial specific carbon emissions.</div></div><div><h3>Results</h3><div>Twenty-two articles were identified as eligible and included in the review. Most included studies (<em>n</em> = 17, 77%) were published between 2020 and 2024. Over half of the included studies (<em>n</em> = 13, 59%) were primary research articles with the majority reporting carbon audits of trials and their associated processes. The remaining literature comprised secondary studies (<em>n</em> = 3, 14%) and opinion pieces (<em>n</em> = 6, 27%). Diverse and evolving approaches to studying trial-related carbon emissions were identified alongside several carbon hotspots including those associated with trial-related travel, trial facilities, and sample lifecycle.</div></div><div><h3>Conclusion</h3><div>The literature on carbon emissions associated with clinical trials has focused on studies reporting carbon audits of trials and their associated processes. Efforts have been made to quantify the trial carbon output with variability in methods and carbon output. Despite the development and evolution of carbon measurement tools, strategies to mitigate trial specific carbon emissions are still much in need.</div></div><div><h3>Plain Language Summary</h3><div>Clinical trials are important to the development of medicine and health care but they have great unintended environmental impacts, especially in the form of carbon emissions. We looked at the literature to understand how carbon emissions generated by clinical trials were measured, which components across trials were carbon heavy, and what could be done to reduce the carbon output of clinical trials. We found 22 relevant articles of which 13 were primary research studies. Twelve of these primary studies measured carbon output of a range of trials. Their results varied considerably because of the variability of a host of factors, such as the number of trials analyzed, trial duration, geographical scope, trial processes measured and methods for quantifying carbon emissions. Despite varied definitions of carbon hotspots, several trial activities, including trial-related travels and meetings, trial facilities, and sample and laboratory activities, were found to be carbon heavy across studies. The ","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111733"},"PeriodicalIF":7.3,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143494768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Shortcomings in reporting country-level participation in multicenter randomized controlled trials involving Ireland as a collaborating partner: a metaresearch study","authors":"James Larkin , Uchechukwu Alanza , Vikneswaran Raj Nagarajan , Maurice Collins , Sami Termanini , Emmet Farrington , Barbara Clyne , Tom Fahey , Frank Moriarty","doi":"10.1016/j.jclinepi.2025.111728","DOIUrl":"10.1016/j.jclinepi.2025.111728","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Multi-center randomized controlled trials (RCTs) provide vital information about healthcare interventions. Reporting on country-level participation is important for understanding the context of multicenter RCTs. This study aimed to examine multicenter RCT reporting of country-level participation, using Ireland as a case study.</div></div><div><h3>Study Design and Setting</h3><div>This meta-research study included RCTs identified in a previous study of Irish RCTs. The previous study involved searching six databases (inception-2018) for RCTs with participants recruited in Ireland: PubMed, Embase, Scopus, CINAHL, PsychINFO, and the Cochrane Register of Controlled Trials. This current study focuses on multicenter RCTs conducted on humans in healthcare settings with <80% of participants recruited in Ireland. Outcome variables were trial characteristics and reporting rates for several variables, including the number of Irish centers, number of participants recruited in Ireland, and reporting the use of relevant reporting guidelines. Descriptive statistics were used for analysis.</div></div><div><h3>Results</h3><div>Overall, 239 RCTs were included. The most common intervention was a drug (74.9% of RCTs). The most common clinical domain was the cardiovascular system (18.0%). The number of Irish centers was reported in 75.3% of RCTs and the number of participants recruited in Ireland in 27.2%. Among RCTs that were published after the Consolidated Standards of Reporting in Trials (CONSORT) reporting guideline was published, 8.3% reported using a relevant reporting guideline.</div></div><div><h3>Conclusion</h3><div>Our findings show deficits in reporting for multicenter RCTs, particularly in reporting the number of participants in Ireland and reporting the use of relevant reporting guidelines. The development of a multicenter trial extension to existing reporting guidelines may partly address country-level reporting issues.</div></div><div><h3>Plain Language Summary</h3><div>A randomized controlled trial is a way of assessing the benefits of a medical intervention (like a treatment for a health issue). Randomized trials have at least two different treatment groups. The people taking part are put into one of the groups at random. This process is called “randomization” and is usually done by a computer. These trials are often conducted with people recruited from multiple countries, and Ireland is a country that sometimes plays a role. The key details about trials, such as the number of participants from each country, are often missing from publications related to trials. Information about the number of participants from each country is important, because it shows how applicable the results are to that country or similar countries. We want to find out if trials involving Ireland provide details of how many people from Ireland or how many healthcare centers in Ireland took part in the trial. We did this by looking ","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111728"},"PeriodicalIF":7.3,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A scoping review of the assessment reports of genetic or genomic tests reveals inconsistent consideration of key dimensions of clinical utility","authors":"Angelo Maria Pezzullo , Angelica Valz Gris , Nicolò Scarsi , Diego Maria Tona , Martina Porcelli , Matteo Di Pumpo , Peter Piko , Roza Adany , Pragathy Kannan , Markus Perola , Maria Luis Cardoso , Alexandra Costa , Astrid M. Vicente , Anu Reigo , Mariliis Vaht , Andres Metspalu , Mark Kroese , Roberta Pastorino , Stefania Boccia","doi":"10.1016/j.jclinepi.2025.111729","DOIUrl":"10.1016/j.jclinepi.2025.111729","url":null,"abstract":"<div><h3>Objectives</h3><div>Genetic and genomic tests are the cornerstone of personalized preventive approaches. Inconsistency in evaluating their clinical utility is often cited as a reason for their limited implementation in clinical practice. Previous reviews have primarily focused on theoretical frameworks used for clinical utility evaluations of genetic tests, rather than actual assessments and examined dimensions, rather than specific indicators within these dimensions. We aimed to review the dimensions and the specific indicators measured in published assessment reports of genetic or genomic tests.</div></div><div><h3>Study Design and Setting</h3><div>We conducted a scoping review of assessment reports of genetic and genomic tests used for prevention, searching through PubMed, Web of Science, Scopus, the websites of 20 different organizations, Google, and Google Scholar. From the included assessments, we extracted the reported indicators of clinical utility, compiling a list of disease-specific indicators that detailed their numerator, denominator, and calculation methods. We analyzed the extracted indicators by stratifying them according to ten comprehensive dimensions of clinical utility, the assessment framework used, and the type of indicator (categorized as quantitative, qualitative, reference, or no evidence reported). From these indicators, we then distilled a list of general indicators.</div></div><div><h3>Results</h3><div>We reviewed 3054 unique references and 12,000 results from gray literature searches, ultimately selecting 57 assessment reports. The reference frameworks used were health technology assessment (HTA) (42%), Evaluation of Genomic Applications in Practice and Prevention (EGAPP) (25%), ACCE (21%), and others (12%). We identified 951 disease-specific indicators. The dimensions most frequently evaluated (ie, had at least one indicator) were analytic validity (60%), clinical validity (79%), clinical efficacy (79%), and economic impact (58%). Only 12 assessments compared health outcomes between tested and untested groups, and fewer than 15% of the assessments addressed equity, acceptability, legitimacy, and personal value.</div></div><div><h3>Conclusion</h3><div>Our study illustrates that, although dimensions such as equity and acceptability, are significantly emphasized in traditional evaluation frameworks, these are often not considered in the assessments. Additionally, our study has underscored a significant dearth of reported primary evidence concerning the clinical efficacy of these tests.</div></div><div><h3>Plain Language Summary</h3><div>Genetic and genomic tests analyze a person's genes to predict health risks and guide healthcare decisions, potentially identifying who might benefit from certain treatments or check-ups. However, determining whether these tests are genuinely useful for wide use in health services is complex, because there is no standard way to define “clinical utility” of a genetic test. To unde","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111729"},"PeriodicalIF":7.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to 'Methodological systematic review recommends improvements to conduct and reporting when meta-analyzing interrupted time series studies'. Journal of Clinical Epidemiology 145 (2022) 55-69.","authors":"Elizabeth Korevaar, Amalia Karahalios, Simon L Turner, Andrew B Forbes, Monica Taljaard, Allen C Cheng, Jeremy M Grimshaw, Lisa Bero, Joanne E McKenzie","doi":"10.1016/j.jclinepi.2025.111706","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111706","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111706"},"PeriodicalIF":7.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450901","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":"The need for further guidance on the handling of multiple outcomes in randomized controlled trials: a scoping review of the methodological literature","authors":"Hadeel Hussein ,&nbsp;Rod S. Taylor ,&nbsp;Anthony Muchai Manyara ,&nbsp;Anthony Purvis ,&nbsp;Richard Emsley ,&nbsp;Rui Duarte ,&nbsp;Valerie Wells ,&nbsp;Yimin Jiang ,&nbsp;Grace O. Dibben","doi":"10.1016/j.jclinepi.2025.111724","DOIUrl":"10.1016/j.jclinepi.2025.111724","url":null,"abstract":"<div><h3>Objectives</h3><div>To review current methodological guidance for handling and reporting of multiple outcomes (MOCs) in randomized controlled trials (RCTs).</div></div><div><h3>Study Design and Setting</h3><div>A scoping review with bibliographic database searches including Embase, PubMed, and Web of Science up to January 16, 2025 was conducted. Inclusion criteria were articles that: (1) provide advice on the design, analysis, or reporting of RCTs using MOCs; and/or (2) discuss statistical approaches for handling MOCs in RCTs. Six specific websites were also checked for formal and reporting guidelines. Included articles were summarized using thematic analysis.</div></div><div><h3>Results</h3><div>Searches retrieved 1716 articles of which 123 were included with additional 25 articles from updated search. Eight additional articles were identified by the specific website search. Six main subthemes on methodological recommendations for using MOCs were identified from 74 of 123 articles (60%): (1) need to prespecify outcomes and analysis, (2) multiplicity adjustment, (3) power and sample size implications, (4) secondary outcomes multiplicity, (5) considerations of MOCs correlation, and (6) specific applications of MOCs. Recommendations on coprimary and composite outcomes were also identified, including their features, analyses methods, reporting, and challenges. Statistical methods for analyzing MOCs were discussed in 53 of 123 articles (43%), with the majority describing modifications of pre-existing statistical approaches.</div></div><div><h3>Conclusion</h3><div>Current recommendations on using MOCs in RCTs focus primarily on statistical considerations and trials of licensing drugs or medical devices. Areas for further methodological research and guidance include reporting of the rationale for the use and selection of MOCs in RCTs and considerations for trials undertaken in nonregulatory setting, including complex interventions.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111724"},"PeriodicalIF":7.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The GIN-McMaster Guideline Development Checklist extension for engagement","authors":"Jennifer Petkovic ,&nbsp;Olivia Magwood ,&nbsp;Thomas W. Concannon ,&nbsp;Elie A. Akl ,&nbsp;Joanne Khabsa ,&nbsp;Lyubov Lytvyn ,&nbsp;Vivian Welch ,&nbsp;Marc T. Avey ,&nbsp;Soumyadeep Bhaumik ,&nbsp;Angus Gunn ,&nbsp;Ana Marusic ,&nbsp;Lawrence Mbuagbaw ,&nbsp;Zachary Munn ,&nbsp;Marisha E. Palm ,&nbsp;Danielle Pollock ,&nbsp;Michael Saginur ,&nbsp;Imad Bou Akl ,&nbsp;Thurayya Arayssi ,&nbsp;Asma Ben Brahem ,&nbsp;Tammy Clifford ,&nbsp;Peter Tugwell","doi":"10.1016/j.jclinepi.2025.111727","DOIUrl":"10.1016/j.jclinepi.2025.111727","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;Better engagement of diverse groups of interest-holders in the development of health guidelines has been proposed to improve their usefulness, implementability, and acceptability. Guidelines shape clinical or public health practice decision-making. Trustworthy guidelines are systematically developed documents that include actionable statements based on evidence and a formal, structured and transparent decision process. This paper describes the Guidelines International Network (GIN)-McMaster Guideline Development Checklist (GDC) Extension for Engagement to assist developers with engaging multiple interest-holders throughout all topics of guideline development.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;To produce this checklist extension, we conducted a 3-phase mixed methods study. First, we identified 10 groups of interest-holders to be engaged in health guideline development: patients, the public, providers, program managers, principal investigators, payers/purchasers of health services, payers/funders of health research, policymakers, peer-reviewed journal editors, and product makers identified in previous work and recruited coleads to represent these groups (&lt;em&gt;n&lt;/em&gt; = 26 total).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;We conducted a series of reviews to identify existing methods and barriers/facilitators for engagement, approaches to managing conflicts of interest, and describe the impacts of engagement on the guideline development process. The results of these reviews informed the development of an online survey for which we received 195 responses. We clarified these results through 43 key informant interviews with interest-holders. The final GDC extension checklist was determined based on consensus methods with our coleads.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;This paper presents the GIN-McMaster GDC Extension for Engagement. This checklist provides interest-holder–informed recommendations for providing advice/feedback or participating in decision-making in guideline development.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;Health-care guidelines are formal recommendations for diagnosing and managing medical conditions or public health concerns. The steps involved in developing guidelines include from planning the process, defining the focus of the guideline and the questions it should answer, and summarizing the evidence. The Guidelines International Network-McMaster Guideline Development checklist includes 146 steps of guideline development grouped into 18 topics. Our work extends this checklist to provide guidance on how to engage with people from different groups of ‘interest-holders’ throughout guideline development. These interest-holders include (1) patients, (2) members of the public, (3) health-care providers, (4) people who manage and operate health programs, (5) researchers, (6) people who pay for or purchase health services, (7) policymakers, (8) people who pay for health research","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111727"},"PeriodicalIF":7.3,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of the quality of reporting of statistical analysis plans for cluster randomized trials","authors":"Jacqueline Y. Thompson ,&nbsp;Julia Shaw ,&nbsp;Samuel I. Watson ,&nbsp;Yixin Wang ,&nbsp;Clare Robinson ,&nbsp;Monica Taljaard ,&nbsp;Karla Hemming","doi":"10.1016/j.jclinepi.2025.111726","DOIUrl":"10.1016/j.jclinepi.2025.111726","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>The guideline for the content of Statistical Analysis Plans (SAPs) outlines recommendations for items to be included in SAPs. As yet there is no specific tailoring of this guideline for Cluster Randomized Trials (CRTs). There has also been no assessment of reporting quality of SAPs against this guideline. Our intention is to identify how well a sample of SAPs for CRTs are adhering to the reporting of key items in the current guidelines, as well as additional analysis aspects considered to be important in CRTs.</div></div><div><h3>Methods</h3><div>We include (i) fully published standalone SAPs identified via Ovid-MEDLINE and (ii) SAPs published as supplementary material or appendices to the final published report identified by searching an existing database of nearly 800 CRTs.</div></div><div><h3>Results</h3><div>The search identified 85 unique SAPs: 26 were published in standalone format and 59 were supplementary material to the full trial report. There was mixed clarity in reporting of items related to the current guideline (eg, most (61/85, 72%) reported what covariates will be included in any adjustment; but fewer (26/85, 31%) reported what method will be used to estimate the absolute measure of effect). Considering additional aspects important for CRTs, the majority (79/85, 93%) included a plan to allow for clustering in the analysis; but fewer (10/40, 25%) reported how a small number of clusters would be accommodated (this was only considered relevant for the subset of CRTs with fewer than 40 clusters). Few (5/85, 6%) reported how the intracluster correlation would be estimated. Few clearly reported statistical targets of inference: in only two SAPs (2/85, 2%) it was clear whether the objectives were related to the individual or cluster-level average; in trials where relevant, only three (3/70, 4%) clearly reported whether the objectives were related to the marginal or cluster-specific effect.</div></div><div><h3>Conclusion</h3><div>This review has identified specific areas of poor quality of reporting that might need additional consideration when developing the guidance for the reporting of SAPs for CRTs.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111726"},"PeriodicalIF":7.3,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Author’s reply: “The importance of properly specifying your target trial emulation: commentary on Mésidor et al.”","authors":"Miceline Mésidor,&nbsp;Caroline Sirois,&nbsp;Jason Robert Guertin,&nbsp;Paul Poirier,&nbsp;Claudia Blais,&nbsp;Denis Talbot","doi":"10.1016/j.jclinepi.2025.111723","DOIUrl":"10.1016/j.jclinepi.2025.111723","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111723"},"PeriodicalIF":7.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426697","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":"\"Treatment effects properly defined are not due to placebo: response to Schmidt et al\": author's reply.","authors":"Stefan Schmidt, Martin Loef, Thomas Ostermann, Harald Walach","doi":"10.1016/j.jclinepi.2025.111721","DOIUrl":"10.1016/j.jclinepi.2025.111721","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111721"},"PeriodicalIF":7.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426694","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":"Quantitative assessment of inconsistency in meta-analysis using decision thresholds with two new indices","authors":"Bernardo Sousa-Pinto ,&nbsp;Ignacio Neumann ,&nbsp;Rafael José Vieira ,&nbsp;Antonio Bognanni ,&nbsp;Manuel Marques-Cruz ,&nbsp;Sara Gil-Mata ,&nbsp;Simone Mordue ,&nbsp;Clareece Nevill ,&nbsp;Gianluca Baio ,&nbsp;Paul Whaley ,&nbsp;Guido Schwarzer ,&nbsp;James Steele ,&nbsp;Gavin Stewart ,&nbsp;Holger J. Schünemann ,&nbsp;Luís Filipe Azevedo","doi":"10.1016/j.jclinepi.2025.111725","DOIUrl":"10.1016/j.jclinepi.2025.111725","url":null,"abstract":"<div><h3>Objectives</h3><div>In evidence synthesis, inconsistency is typically assessed visually and with the <em>I</em>² and the Q statistics. However, these measures have important limitations (i) if there are few primary studies of small sample sizes or (ii) if there are multiple studies with precise estimates. In addition, with the increasing use of decision thresholds (DT), for example in Grading of Recommendations Assessment, Development and Evaluation evidence to decision (EtD) frameworks, inconsistency judgments can be anchored around DTs. In this article, we developed quantitative measures to assess inconsistency based on DTs.</div></div><div><h3>Study Design and Setting</h3><div>We developed two measures to quantify inconsistency based on DTs – the decision inconsistency (<em>DI</em>) and the across-studies inconsistency (<em>ASI</em>) indices. The <em>DI</em> and the <em>ASI</em> are based on the distribution of the posterior samples studies’ effect sizes (ES) across interpretation categories defined by DTs. We developed these indices for the Bayesian context, followed by a frequentist extension.</div></div><div><h3>Results</h3><div>The <em>DI</em> informs on the <em>overall inconsistency of ESs</em> across interpretation categories, while the <em>ASI</em> quantifies how <em>different</em> studies are compared to each other (in relation to interpretation categories) based on absolute effects. A <em>DI</em> ≥ 50% and an ASI ≥ 25% are suggestive of important inconsistency. We provide an R package (metainc) and a web tool (<span><span>https://metainc.med.up.pt/</span><svg><path></path></svg></span>) to support the computation of the <em>DI</em> and <em>ASI</em>, including in the context of sensitivity analyses assessing the impact of potential uncertainty in inconsistency.</div></div><div><h3>Conclusion</h3><div>The <em>DI</em> and the <em>ASI</em> can contribute to quantitatively assess inconsistency, particularly as DTs are gaining recognition in evidence synthesis and health decision-making.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"181 ","pages":"Article 111725"},"PeriodicalIF":7.3,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426738","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}