Journal of Clinical Epidemiology最新文献

{"title":"Data management and sharing","authors":"Claude Pellen ,&nbsp;Nchangwi Syntia Munung ,&nbsp;Anna Catharina Armond ,&nbsp;Daniel Kulp ,&nbsp;Ulrich Mansmann ,&nbsp;Maximilian Siebert ,&nbsp;Florian Naudet","doi":"10.1016/j.jclinepi.2025.111680","DOIUrl":"10.1016/j.jclinepi.2025.111680","url":null,"abstract":"<div><div>Guided by the FAIR principles (Findable, Accessible, Interoperable, Reusable), responsible data sharing requires well-organized, high-quality datasets. However, researchers often struggle with implementing Data Management and Sharing Plans due to lack of knowledge on how to do this, time constraints, and legal, technical, and financial challenges, particularly concerning data ownership and privacy. While patients support data sharing, researchers and funders may hesitate, fearing the loss of intellectual property or competitive advantage. Although some journals and institutions encourage or mandate data sharing, further progress is needed. Additionally, global solutions are vital to ensure equitable participation from low- and middle-income countries. Ultimately, responsible data sharing requires strategic planning, cultural shifts in research, and coordinated efforts from all stakeholders to become standard practice in biomedical research.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111680"},"PeriodicalIF":7.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025463","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":"You wait ages, and then two arrive at once: reporting guidelines should not be like buses","authors":"William T. Gattrell,&nbsp;David Tovey,&nbsp;Patricia Logullo,&nbsp;Amy Price,&nbsp;Paul Blazey,&nbsp;Christopher C. Winchester,&nbsp;Esther J. van Zuuren,&nbsp;Niall Harrison","doi":"10.1016/j.jclinepi.2025.111682","DOIUrl":"10.1016/j.jclinepi.2025.111682","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111682"},"PeriodicalIF":7.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025481","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":"Using artificial intelligence to semi-automate trustworthiness assessment of randomized controlled trials: a case study","authors":"Ling Shan Au ,&nbsp;Lizhen Qu ,&nbsp;Jeremy Nielsen ,&nbsp;Zongyuan Ge ,&nbsp;Lyle C. Gurrin ,&nbsp;Ben W. Mol ,&nbsp;Rui Wang","doi":"10.1016/j.jclinepi.2025.111672","DOIUrl":"10.1016/j.jclinepi.2025.111672","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Randomized controlled trials (RCTs) are the cornerstone of evidence-based medicine. Unfortunately, not all RCTs are based on real data. This serious breach of research integrity compromises the reliability of systematic reviews and meta-analyses, leading to misinformed clinical guidelines and posing a risk to both individual and public health. While methods to detect problematic RCTs have been proposed, they are time-consuming and labor-intensive. The use of artificial intelligence large language models (LLMs) has the potential to accelerate the data collection needed to assess the trustworthiness of published RCTs.</div></div><div><h3>Methods</h3><div>We present a case study using ChatGPT powered by OpenAI's GPT-4o to assess an RCT paper. The case study focuses on applying the trustworthiness in randomised controlled trials (TRACT checklist) and automating data table extraction to accelerate statistical analysis targeting the trustworthiness of the data. We provide a detailed step-by-step outline of the process, along with considerations for potential improvements.</div></div><div><h3>Results</h3><div>ChatGPT completed all tasks by processing the PDF of the selected publication and responding to specific prompts. ChatGPT addressed items in the TRACT checklist effectively, demonstrating an ability to provide precise “yes” or “no” answers while quickly synthesizing information from both the paper and relevant online resources. A comparison of results generated by ChatGPT and the human assessor showed an 84% level of agreement of (16/19) TRACT items. This substantially accelerated the qualitative assessment process. Additionally, ChatGPT was able to extract efficiently the data tables as Microsoft Excel worksheets and reorganize the data, with three out of four extracted tables achieving an accuracy score of 100%, facilitating subsequent analysis and data verification.</div></div><div><h3>Conclusion</h3><div>ChatGPT demonstrates potential in semiautomating the trustworthiness assessment of RCTs, though in our experience this required repeated prompting from the user. Further testing and refinement will involve applying ChatGPT to collections of RCT papers to improve the accuracy of data capture and lessen the role of the user. The ultimate aim is a completely automated process for large volumes of papers that seems plausible given our initial experience.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111672"},"PeriodicalIF":7.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015518","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":"Incomplete reporting of adverse events in duloxetine trials: a meta-research survey of randomized controlled trials vs placebo","authors":"P. Rolland ,&nbsp;A. Jutel ,&nbsp;K. Douget ,&nbsp;F. Naudet ,&nbsp;J.C. Roy","doi":"10.1016/j.jclinepi.2025.111677","DOIUrl":"10.1016/j.jclinepi.2025.111677","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Relying on published data alone might be insufficient for meta-analyses to be reliable and trustworthy since selective outcome reporting is common, especially for adverse events (AEs). We investigated the existence of selective reporting and its potential for bias in a case study exploring AEs of duloxetine in adults.</div></div><div><h3>Study Design and Setting</h3><div>We systematically searched all previous meta-analyses/pooled analyses on duloxetine published on PubMed for seven indications approved by the American and European health authorities. We included all randomized controlled trials (RCTs) vs placebo. For each RCT, we extracted the number of serious adverse events (SAEs), AEs, drop-outs (DOs) and drop-outs for safety reasons (DOSRs) using four information sources: published articles, clinical study registries, clinical study reports and data available in meta-analyses/pooled analyses. To assess the range of differences resulting from these four extraction strategies, we performed 4 meta-analyses using random effect models as well as a complete meta-analysis combining all sources.</div></div><div><h3>Results</h3><div>A total of <em>70</em> RCTs (including 24,330 patients) were included. Of those, SAEs were identified for 42 studies (61%) in published articles, 58 (84%) in study reports (8 study reports were not retrieved), 24 (34.7%) in registries, and 21 (30.4%) in meta-analyses/pooled analyses. For 2 (2.9%), 2 (2.9%), 2 (2.9%) and 1 (1.4%) studies, we found respectively no data on SAEs, AEs, DOs, and DOSRs in any sources. Discrepant results across sources were found in 24 (34.5%), 20 (28.5%), 13 (18.6%), and 9 (12.8%) studies, respectively for SAEs, AEs, DOs, and DOSRs. Despite variations in point estimates and their 95% confidence intervals, we did not find different results in the conclusions of meta-analyses depending on the different information sources used, except for DOs, for which no effect was found using results published in registries, in contrast to other information sources.</div></div><div><h3>Conclusion</h3><div>None of the four information sources provided complete retrieval of safety results for duloxetine in adults across various indications. However, we did not find strong evidence that this underreporting leads to different conclusions in meta-analyses. Nonetheless, this finding remains uncertain, as we were unable to obtain complete information for all studies despite extensive searches.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111677"},"PeriodicalIF":7.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015508","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 simulation study showed that linear regression and Mann-Whitney test can be used to analyze the days alive and at home by day 30 outcome in a randomized controlled trial","authors":"Jonathan Alistair Cook","doi":"10.1016/j.jclinepi.2025.111674","DOIUrl":"10.1016/j.jclinepi.2025.111674","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>The aims of the work were to consider the properties of the days alive and at home by day 30 (DAH30) from a statistical perspective, and to conduct a simulation study exploring the use of simple (unadjusted) linear regression and Mann-Whitney test as the method of analysis reflect realized analysis options.</div></div><div><h3>Study Design and Setting</h3><div>The days alive and at home by day 30 (DAH30) has been proposed a patient-centric outcome, and clinically relevant outcome suitable for clinical trials. It has unusual statistical properties, and suitability of standard statistical analysis methods is unclear. The properties of DAH30 were reviewed. Simulations based upon 1:1 allocation in an randomized controlled trial (RCT) based upon empirical data were conducted reflecting different additive and realized (reflecting the DAH30) treatment effects, sample sizes and distributions with varying and central locations and zero value level. A variety of metrics were used to assess performance (including bias, coverage, and rejection rate).</div></div><div><h3>Results</h3><div>Linear regression provided a valid estimate of the unadjusted average treatment effect with an additive treatment. This was confirmed in terms of bias, estimation of variance, rejection rate in the absence of an effect, and coverage of the 95% confidence interval for the true realized effect. Mann-Whitney provided greater (power) than linear regression in some situations.</div></div><div><h3>Conclusion</h3><div>Simple linear regression is a reasonable analytic option for the DAH30 for estimating the average treatment effect in the RCT cohort (ie, an intention to treat, or “treatment policy” estimand) where zero-inflation is relatively low. Mann-Whitney test in some circumstances (small effects and smaller samples sizes) provides better ability (like for like) to detect a difference between the groups.</div></div><div><h3>Plain Language Summary</h3><div>Simple linear regression can be used to analyze DAH30 outcome in a randomized trial for a range of scenarios which were considered in this study (including relatively proportions of zero values). The DAH30's properties affect the treatment effect than can be estimated. Mann-Whitney test offered better ability to detect a difference of a smaller magnitude for smaller samples sizes.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111674"},"PeriodicalIF":7.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015291","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":"High-impact trials with genetic and -omics information focus on cancer mutations, are industry-funded, and less transparent","authors":"Luigi Russo ,&nbsp;Leonardo M. Siena ,&nbsp;Sara Farina ,&nbsp;Roberta Pastorino ,&nbsp;Stefania Boccia ,&nbsp;John P.A. Ioannidis","doi":"10.1016/j.jclinepi.2025.111676","DOIUrl":"10.1016/j.jclinepi.2025.111676","url":null,"abstract":"<div><h3>Objectives</h3><div>To assess how genetics and -omics information is used in the most cited recent clinical trials and to evaluate industry involvement and transparency patterns.</div></div><div><h3>Study Design and Setting</h3><div>This is a meta-research evaluation using a previously constructed database of the 600 most cited clinical trials published from 2019 to 2022. Trials that utilized genetic or -omics characterization of participants in the trial design, analysis, and results were considered eligible.</div></div><div><h3>Results</h3><div>132 (22%) trials used genetic or -omics information, predominantly for detection of cancer mutations (<em>n</em> = 101). Utilization included eligibility criteria (<em>n</em> = 59), subgroup analysis (<em>n</em> = 82), and stratification factor in randomization (<em>n</em> = 14). Authors addressed the relevance in the conclusions in 82 studies (62%). 102 studies (77%) provided data availability statements and six had data already available. Most studies had industry funding (<em>n</em> = 111 [84.0%]). Oncology trials were more likely to be industry-funded (90.1% vs 64.5%, <em>P</em> = .001), to have industry-affiliated analysts (43.6% vs 22.6%, <em>P</em> = .036), and to favor industry-sponsored interventions (83.2% vs 58.1% <em>P</em> = .004). When compared to other trials, genetic and -omics trials were more likely to be funded by industry (84% vs 63.9%, <em>P</em> &lt; .001) and tended to be less likely to have full protocols (<em>P</em> = .018) and statistical plans (<em>P</em> = .04) available.</div></div><div><h3>Conclusion</h3><div>Our study highlights the current underutilization of genetic and -omics technologies beyond testing for cancer mutations. Industry involvement in these trials appears to be more substantial and transparency is more limited, raising concerns about potential bias.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111676"},"PeriodicalIF":7.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015502","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":"Funding matters: time to update preferred reporting items for systematic reviews and meta-analyses?","authors":"James Burgert,&nbsp;Georgia C. Richards","doi":"10.1016/j.jclinepi.2025.111678","DOIUrl":"10.1016/j.jclinepi.2025.111678","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111678"},"PeriodicalIF":7.3,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015358","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":"How should we assess trustworthiness of randomized controlled trials?","authors":"Jack Wilkinson, David Tovey","doi":"10.1016/j.jclinepi.2025.111670","DOIUrl":"10.1016/j.jclinepi.2025.111670","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111670"},"PeriodicalIF":7.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015506","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":"Exploring the use and usefulness of living guidelines for consumers: international online survey of patients' and carers' views","authors":"Anneliese Synnot ,&nbsp;Samantha Chakraborty ,&nbsp;Jessica Xue ,&nbsp;Hui Zhen Cheng ,&nbsp;Danielle Berkovic ,&nbsp;Tari Turner","doi":"10.1016/j.jclinepi.2025.111671","DOIUrl":"10.1016/j.jclinepi.2025.111671","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Living guidelines contain continually updated, and potentially changing, clinical recommendations. The implications of living guidelines for consumers (eg, patients, carers, and people with lived experience) - particularly how living guidelines should be developed and disseminated – are yet to be established. The objective of this study was to explore consumers’ views about how best to support the use and usefulness of living guidelines to consumers.</div></div><div><h3>Methods</h3><div>This study used a qualitative (online survey) design. We invited consumers who were familiar with guidelines (living or conventional) to participate in the study. The survey was distributed globally. Recruitment was conducted via the Australian and international networks of the Australian Living Evidence Collaboration. We invited consumers who were familiar with guidelines (living or conventional) to participate in the study. The survey was distributed globally. Recruitment was conducted via the Australian and international networks of the Australian Living Evidence Collaboration. The 5–10 minute survey collected demographic data then, after introducing the living guidelines concept, asked questions about what living guidelines mean for consumers, how we might make them easy for consumers to find and use, and potential challenges to their use. We analyzed the data using inductive thematic analysis.</div></div><div><h3>Results</h3><div>Forty-five people (71% women) from 12 countries completed the survey. Participants were enthusiastic about the concept of living guidelines and what they might mean for consumers' ability to make informed health-care decisions and receive best care. They also identified potential challenges related to living guideline dissemination, such as low public awareness of guidelines and confusion about updated recommendations. Participants described practical strategies to support consumers’ awareness and use of, and access to, living guidelines. These included: meaningful involvement of consumers in the development and dissemination of living guidelines; raising awareness by promoting the guidelines widely through trusted health information sources and on social media; and using user-centered formatting and design principles (eg, considering accessibility needs, and publishing lay summaries with plain and culturally-appropriate language).</div></div><div><h3>Conclusion</h3><div>Consumers suggested a comprehensive range of dissemination strategies to support the use and usefulness of living guidelines to consumers, which largely reflect best practice in conventional guideline dissemination. Promoting and explaining the living nature of guideline recommendations might support their use by consumers. There should also be a close link between the living guidelines and any versions or additional content created for both consumers and clinicians.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111671"},"PeriodicalIF":7.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015356","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":"Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review","authors":"Biruk Tsegaye ,&nbsp;Kym I.E. Snell ,&nbsp;Lucinda Archer ,&nbsp;Shona Kirtley ,&nbsp;Richard D. Riley ,&nbsp;Matthew Sperrin ,&nbsp;Ben Van Calster ,&nbsp;Gary S. Collins ,&nbsp;Paula Dhiman","doi":"10.1016/j.jclinepi.2025.111675","DOIUrl":"10.1016/j.jclinepi.2025.111675","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods within oncology and compared the sample size used to develop the models with the minimum required sample size needed when developing a regression-based model (N_min).</div></div><div><h3>Methods</h3><div>We searched the Medline (via OVID) database for studies developing a prediction model using ML methods published in December 2022. We reviewed how sample size was justified. We calculated N_min, which is the N_min, and compared this with the sample size that was used to develop the models.</div></div><div><h3>Results</h3><div>Only one of 36 included studies justified their sample size. We were able to calculate N_min for 17 (47%) studies. 5/17 studies met N_min, allowing to precisely estimate the overall risk and minimize overfitting. There was a median deficit of 302 participants with the event (<em>n</em> = 17; range: −21,331 to 2298) when developing the ML models. An additional three out of the 17 studies met the required sample size to precisely estimate the overall risk only.</div></div><div><h3>Conclusion</h3><div>Studies developing a prediction model using ML in oncology seldom justified their sample size and sample sizes were often smaller than N_min. As ML models almost certainly require a larger sample size than regression models, the deficit is likely larger. We recommend that researchers consider and report their sample size and at least meet the minimum sample size required when developing a regression-based model.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111675"},"PeriodicalIF":7.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015515","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}