Pub Date : 2025-10-18DOI: 10.1016/j.jclinepi.2025.112025
KM Saif-Ur-Rahman , Kerry Waddell , John N. Lavis , Nikita N. Burke , Marie Tierney , Barbara Whelan , Declan Devane
<div><h3>Background and Objectives</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>Our experience highlights the importance of applying systematic approaches to maximize transparency, reproducibility, and credibility. We found that policy documents often lacked abstracts, standardized 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 (eg, guidelines, modeling, evaluations) were cited in preparedness plans, the role of evidence in shaping decisions was often unclear, revealing a key limitation of current practice.</div></div><div><h3>Conclusion</h3><div>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 recognizing 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.</div></div><div><h3>Plain Language Summary</h3><div>Jurisdictional scans are a way to see how different countries, regions, or organizations 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.</div><div>Our paper explains issues to consider in how to do a jurisdictional scan. This incl
{"title":"Jurisdictional scans: methodological considerations for systematically analyzing and comparing policy approaches across different jurisdictions","authors":"KM 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":"10.1016/j.jclinepi.2025.112025","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>Our experience highlights the importance of applying systematic approaches to maximize transparency, reproducibility, and credibility. We found that policy documents often lacked abstracts, standardized 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 (eg, guidelines, modeling, evaluations) were cited in preparedness plans, the role of evidence in shaping decisions was often unclear, revealing a key limitation of current practice.</div></div><div><h3>Conclusion</h3><div>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 recognizing 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.</div></div><div><h3>Plain Language Summary</h3><div>Jurisdictional scans are a way to see how different countries, regions, or organizations 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.</div><div>Our paper explains issues to consider in how to do a jurisdictional scan. This incl","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"189 ","pages":"Article 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}
Pub Date : 2025-10-18DOI: 10.1016/j.jclinepi.2025.112023
S Dhanya Dedeepya, Vaishali Goel, Nivedita Nikhil Desai
{"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":"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}
Objectives: To 1) assess the frequency of overlapping systematic reviews (SRs) on the same topic including overlap in outcomes, 2) assess whether SRs meet some key methodological characteristics, and 3) describe discrepancies in results.
Study design and setting: 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 six 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 Grading of Recommendations, Assessment, Development, and Evaluations assessment.
Results: 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 (IQR 2-6; range 2-19). Of the 230 SRs, 15 (6.5%) met the minimum set of six 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.
Conclusion: 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.
{"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":"10.1016/j.jclinepi.2025.112018","url":null,"abstract":"<p><strong>Objectives: </strong>To 1) assess the frequency of overlapping systematic reviews (SRs) on the same topic including overlap in outcomes, 2) assess whether SRs meet some key methodological characteristics, and 3) 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 six 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 Grading of Recommendations, Assessment, Development, and Evaluations 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 (IQR 2-6; range 2-19). Of the 230 SRs, 15 (6.5%) met the minimum set of six 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>Conclusion: </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}
Pub Date : 2025-10-14DOI: 10.1016/j.jclinepi.2025.112017
Howard Bauchner
{"title":"Systematic reviews and meta-analysis: continued failure to achieve research integrity.","authors":"Howard Bauchner","doi":"10.1016/j.jclinepi.2025.112017","DOIUrl":"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}
Pub Date : 2025-10-10DOI: 10.1016/j.jclinepi.2025.112013
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
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.
{"title":"In humble defense 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":"10.1016/j.jclinepi.2025.112013","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"189 ","pages":"Article 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}
Pub Date : 2025-10-10DOI: 10.1016/j.jclinepi.2025.112014
A. Dobrescu , B. Nussbaumer-Streit , G. Wagner , A. Sharifan , A. Gadinger , I. Klerings , C. Nowak , G. Gartlehner
Background and Objectives
Methodological guidance recommends including both published and unpublished data in systematic reviews to enhance reliability and reduce potential bias. The objective of the study was 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.
Methods
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 PubMed Central, and Embase.com, and conference abstracts from the past 5 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 with 95% CI, COE assessments, and conclusions.
Results
Seventeen (6735 participants) of 37 eligible unpublished trials had posted results and were analyzed, with most (59%) identified via trial registries. In addition, 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, although 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.
Conclusions
In this case study, adding unpublished data had minimal impact on results and conclusions, with only minor changes in network geometry and COE.
{"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":"10.1016/j.jclinepi.2025.112014","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Methodological guidance recommends including both published and unpublished data in systematic reviews to enhance reliability and reduce potential bias. The objective of the study was 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.</div></div><div><h3>Methods</h3><div>We supplemented the search of a recent systematic review with targeted searches for unpublished data on <span><span>ClinicalTrials.gov</span><svg><path></path></svg></span>, the World Health Organization International Clinical Trials Registry Platform, regulatory agency reports, The Preprints Citation Index, Europe PubMed Central, and <span><span>Embase.com</span><svg><path></path></svg></span>, and conference abstracts from the past 5 years. Two independent reviewers selected eligible studies, verified publication status, extracted data, and reassessed certainty of evidence (COE).</div><div>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 with 95% CI, COE assessments, and conclusions.</div></div><div><h3>Results</h3><div>Seventeen (6735 participants) of 37 eligible unpublished trials had posted results and were analyzed, with most (59%) identified via trial registries. In addition, 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, although 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.</div></div><div><h3>Conclusions</h3><div>In this case study, adding unpublished data had minimal impact on results and conclusions, with only minor changes in network geometry and COE.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"189 ","pages":"Article 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}
Pub Date : 2025-10-10DOI: 10.1016/j.jclinepi.2025.112012
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
Objectives
To investigate the methodological and reporting quality of noninferiority (NI) or equivalence trials in breast cancer radiotherapy, providing suggestions for future NI/equivalence trials.
Study Design and Setting
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 prespecified NI margins as absolute risk differences (ARDs) were reevaluated using margins as relative risks.
Results
Twenty-one studies were included. The number of publications increased over time. Trial interventions mainly involved dose fractionation and radiation volume. The primary end point was local or locoregional recurrence in 15, toxicity in 5, and both in 1 study. Reporting gaps included the following: 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); and no reporting of P values for NI/equivalence tests (n = 12) or margins with CIs (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 prespecified margins as ARDs, 4 comparisons were classified as inconclusive when using the margins as relative risks.
Conclusion
Recently, NI/equivalence trials have dramatically increased in breast cancer radiotherapy; however, there is substantial room for improvement in their methodological and reporting quality.
{"title":"Methodological and reporting rigor in noninferiority 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":"10.1016/j.jclinepi.2025.112012","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate the methodological and reporting quality of noninferiority (NI) or equivalence trials in breast cancer radiotherapy, providing suggestions for future NI/equivalence trials.</div></div><div><h3>Study Design and Setting</h3><div>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 prespecified NI margins as absolute risk differences (ARDs) were reevaluated using margins as relative risks.</div></div><div><h3>Results</h3><div>Twenty-one studies were included. The number of publications increased over time. Trial interventions mainly involved dose fractionation and radiation volume. The primary end point was local or locoregional recurrence in 15, toxicity in 5, and both in 1 study. Reporting gaps included the following: not specifying the trial as NI/equivalence in the title/abstract (<em>n</em> = 6); inadequate justification for the NI/equivalence design rationale (<em>n</em> = 10) or margins (<em>n</em> = 12); absence of both intention-to-treat and per-protocol analysis (<em>n</em> = 12); and no reporting of <em>P</em> values for NI/equivalence tests (<em>n</em> = 12) or margins with CIs (<em>n</em> = 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 prespecified margins as ARDs, 4 comparisons were classified as inconclusive when using the margins as relative risks.</div></div><div><h3>Conclusion</h3><div>Recently, NI/equivalence trials have dramatically increased in breast cancer radiotherapy; however, there is substantial room for improvement in their methodological and reporting quality.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"189 ","pages":"Article 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}
Pub Date : 2025-10-10DOI: 10.1016/j.jclinepi.2025.112015
Dongquan Bi , Andrew Copas , Fan Li , Brennan C. Kahan
Objectives
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 randomized trials. Cluster randomized trials (CRTs), in which groups of individuals are randomized, 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.
Study Design and Setting
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.
Results
From 46 eligible articles, we identified eight 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 (eg, 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.
Conclusion
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.
{"title":"A scoping review identified additional considerations for defining estimands in cluster randomized trials","authors":"Dongquan Bi , Andrew Copas , Fan Li , Brennan C. Kahan","doi":"10.1016/j.jclinepi.2025.112015","DOIUrl":"10.1016/j.jclinepi.2025.112015","url":null,"abstract":"<div><h3>Objectives</h3><div>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 randomized trials. Cluster randomized trials (CRTs), in which groups of individuals are randomized, 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.</div></div><div><h3>Study Design and Setting</h3><div>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.</div></div><div><h3>Results</h3><div>From 46 eligible articles, we identified eight 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 (eg, 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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"189 ","pages":"Article 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}
Pub Date : 2025-10-09DOI: 10.1016/j.jclinepi.2025.112007
Yeyi Zhu , Emily Z. Wang , Amanda N. Ngo , Mara B. Greenberg , Assiamira Ferrara
<div><h3>Objectives</h3><div>Hypertensive disorders of pregnancy (HDP), including chronic hypertension, gestational hypertension, and preeclampsia/eclampsia, is a leading cause of maternal and perinatal morbidity. Accurate identification of individual HDP subtypes in electronic health records (EHRs) is critical for research and surveillance but remains a challenge. We aimed to develop and validate EHR-based case-ascertainment algorithms for individual HDP conditions using medical chart review.</div></div><div><h3>Study Design and Setting</h3><div>We conducted a validation study within the Blood Pressure in Pregnancy, Obesity, Diabetes and Perinatal Outcomes (BIPOD) cohort at Kaiser Permanente Northern California, comprising 441,147 singleton pregnancies from 2011 to 2021. Using a stratified sampling approach, we selected 980 pregnancies for medical chart review: 200 chronic hypertension, 280 gestational hypertension, 300 preeclampsia/eclampsia, and 200 normotensive pregnancies. Following the American College of Obstetricians and Gynecologists diagnosis criteria, we developed HDP case-ascertainment algorithms incorporating clinician diagnosis codes, antihypertensive medications, systolic/diastolic blood pressure, and laboratory test results. Normotension was defined as not meeting HDP definitions throughout pregnancy. Positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity were calculated, with weighting to account for sampling design. Minimum validity thresholds were set as 80% PPV, 90% NPV, 80% sensitivity, and 90% specificity.</div></div><div><h3>Results</h3><div>Algorithms for chronic and gestational hypertension demonstrated high diagnostic validity across all definitions, with all performance statistics exceeding the minimum thresholds. All definitions were retained in the final algorithms for chronic hypertension [weighted PPV: 87.0% (95% confidence interval (CI) 86.4%–87.6%), NPV: 99.5% (99.5%–99.5%); sensitivity: 84.9% (84.2%–85.5%); and specificity 99.6% (99.6%–99.6%)] and gestational hypertension [weighted PPV 91.4% (91.1%–91.7%); NPV: 99.5% (99.5%–99.5%); sensitivity: 94.0% (93.7%–94.2%); and specificity: 99.3% (99.2%–99.3%)]. For preeclampsia/eclampsia, only the definition using inpatient diagnosis had acceptable validity (PPV: 94.9%), while definitions using outpatient diagnoses or laboratory results had poor PPV (0.0%–8.0%). Weighted performance for the preeclampsia/eclampsia final algorithm using inpatient diagnosis was high: PPV 94.9% (94.5%–95.2%); NPV 99.5% (99.5%–99.5%); sensitivity 88.8% (88.3%–89.3%); and specificity 99.8% (99.8%–99.8%). Similarly, normotensive had high validation performance: PPV 99.5% (99.5%–99.5%); NPV 91.4% (91.2%–91.7%); sensitivity 98.6% (98.6%–98.7%); and specificity 96.7% (96.5%–96.8%).</div></div><div><h3>Conclusion</h3><div>EHR-based case-ascertainment algorithms for HDP demonstrated high validity in a large, diverse population. These algorithms can facilitate
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Pub Date : 2025-10-08DOI: 10.1016/j.jclinepi.2025.112010
Arthur Gougeon, Jean-Christophe Lega, Behrouz Kassaï, François Gueyffier, Rémy Boussageon, Guillaume Grenet
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