Clinical Trials最新文献

{"title":"A multilevel framework for recruitment and retention in implementation trials: An illustrative example.","authors":"Nathaniel J Williams, Alexandra E Gomes, Nallely R Vega, Susan Esp, Mimi Choy-Brown, Rinad S Beidas","doi":"10.1177/17407745241307948","DOIUrl":"10.1177/17407745241307948","url":null,"abstract":"<p><strong>Background: </strong>Implementation and hybrid effectiveness-implementation trials aspire to speed the translation of science into practice by generating crucial evidence for improving the uptake of effective health interventions. By design, they pose unique recruitment and retention challenges due to their aims, units of analysis, and sampling plans, which typically require many clinical sites (i.e. often 20 or more) and participation by individuals who are related across multiple levels (e.g. linked organizational leaders, clinicians, and patients). In this article, we present a new multilevel, theory-informed, and relationship-centered framework for conceptualizing recruitment and retention in implementation and hybrid effectiveness-implementation trials which integrates and builds on prior work on recruitment and retention strategies in patient-focused trials. We describe the framework's application in the Working to Implement and Sustain Digital Outcome Measures hybrid type III trial, which occurred in part during the COVID-19 pandemic.</p><p><strong>Methods: </strong>Recruitment for the Working to Implement and Sustain Digital Outcome Measures trial occurred from October 2019 to February 2022. Development of recruitment and retention strategies was guided by a newly developed multilevel framework, which targeted the capability, opportunity, and motivation of organizational leaders, clinicians, patient-facing administrative staff, and patients to engage in research. A structured assessment guide was developed and applied to refine recruitment and retention approaches throughout the trial. We describe the framework and its application amid the onset of the COVID-19 pandemic which required rapid adjustments to address numerous barriers.</p><p><strong>Results: </strong>The Working to Implement and Sustain Digital Outcome Measures trial enrolled 21 outpatient clinics in three US states, incorporating 252 clinicians and 686 caregivers of youth (95% of patient recruitment target) across two distinct phases. Data completion rates for organizational leaders and clinicians averaged 90% over five waves spanning 18 months, despite the onset of the COVID pandemic. Caregiver completion rates of monthly follow-up assessments ranged from 80%-88% across 6 months. This article presents the multilevel framework, assessment guide, and strategies used to achieve recruitment and retention targets at each level.</p><p><strong>Conclusion: </strong>We conducted a multi-state hybrid type III effectiveness-implementation trial that maintained high recruitment and retention across all relevant levels amid a global pandemic. The newly developed multilevel recruitment and retention framework and assessment guide presented here, which integrates behavioral theory, a relationship-focused lens, and evidence-based strategies for participant recruitment and retention at multiple levels, can be adapted and used by other researchers for implementation, hybrid, and m","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"325-341"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12094903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142964033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the impact of stratification on the power and cross-arm balance of randomized phase 2 clinical trials.","authors":"Anna Moseley, Michael LeBlanc, Boris Freidlin, Rory M Shallis, Amer M Zeidan, David A Sallman, Harry P Erba, Richard F Little, Megan Othus","doi":"10.1177/17407745241304065","DOIUrl":"10.1177/17407745241304065","url":null,"abstract":"<p><p>Background/aimsRandomized clinical trials often use stratification to ensure balance between arms. Analysis of primary endpoints of these trials typically uses a \"stratified analysis,\" in which analyses are performed separately in each subgroup defined by the stratification factors, and those separate analyses are weighted and combined. In the phase 3 setting, stratified analyses based on a small number of stratification factors can provide a small increase in power. The impact on power and type-1 error of stratification in the setting of smaller sample sizes as in randomized phase 2 trials has not been well characterized.MethodsWe performed computational studies to characterize the power and cross-arm balance of modestly sized clinical trials (less than 170 patients) with varying numbers of stratification factors (0-6), sample sizes, randomization ratios (1:1 vs 2:1), and randomization methods (dynamic balancing vs stratified block).ResultsWe found that the power of unstratified analyses was minimally impacted by the number of stratification factors used in randomization. Analyses stratified by 1-3 factors maintained power over 80%, while power dropped below 80% when four or more stratification factors were used. These trends held regardless of sample size, randomization ratio, and randomization method. For a given randomization ratio and sample size, increasing the number of factors used in randomization had an adverse impact on cross-arm balance. Stratified block randomization performed worse than dynamic balancing with respect to cross-arm balance when three or more stratification factors were used.ConclusionStratified analyses can decrease power in the setting of phase 2 trials when the number of patients in a stratification subgroup is small.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"361-366"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of adaptive seamless Phase 2/3 designs for dose selection in clinical trials with multiple endpoints.","authors":"Man Jin","doi":"10.1177/17407745251338592","DOIUrl":"https://doi.org/10.1177/17407745251338592","url":null,"abstract":"<p><p>Adaptive seamless Phase 2/3 designs provide possible pathways to expedite drug development by combining dose selection and confirmatory evaluation on the selected dose with the control group in the same trial. Various methods have been developed to demonstrate the potential advantages compared to conventional development plan with separate Phase 2 and 3 trials. More practical and complicated situations occur when we want to achieve the goal of combining dose selection and confirmatory evaluation in clinical trials with multiple endpoints. Examples of multiple endpoints include multiple efficacy endpoints needed in the final stage for regulatory submissions. In this article, a few inferential adaptive seamless Phase 2/3 designs have been proposed which can combine dose selection and confirmatory stage in clinical trials evaluating multiple endpoints, including adaptive graph-based multiple testing procedure, adaptive seamless design with graph-based combination test, and seamless design with rank-based Dunnett-adjusted test. Simulations are conducted to confirm the control of the familywise type I error rate with an illustrated example design and assess the power. These designs can preserve the familywise type I error rate, and adaptive graph-based multiple testing procedure is more powerful than the others.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251338592"},"PeriodicalIF":2.2,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision medicine evaluation of heterogeneity of treatment effect for a time-to-event outcome with application in a trial of Initial treatment for people living with HIV.","authors":"Yu Zheng, Judy S Currier, Michael D Hughes","doi":"10.1177/17407745251338558","DOIUrl":"https://doi.org/10.1177/17407745251338558","url":null,"abstract":"<p><p>BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of treatment effect analysis offers a promising framework for heterogeneity of treatment effect estimation by simultaneously considering multiple patient characteristics and their interactions with treatment to predict differences in outcomes between randomized treatments. However, its implementation in clinical research remains limited and so we provide a detailed example of its application in a randomized trial that compared raltegravir-based vs darunavir/ritonavir-based therapy as initial antiretroviral treatments for people living with HIV.MethodsThe heterogeneity of treatment effect analysis used a two-step procedure, in which a working proportional hazards model was first selected to construct an index score for ranking the treatment difference for individuals, and then a second calibration step used a non-parametric kernel approach to estimate the true treatment difference for participants with similar index scores. Sensitivity and supplemental analyses were conducted to evaluate the robustness of the results. We further explored the impact of covariates on heterogeneity of treatment effect and the choice between treatments.ResultsThe heterogeneity of treatment effect analysis showed that while there is a clear trend favoring raltegravir-based therapy over darunavir/ritonavir-based therapy for the vast majority of the target population, there were a small subset of patients, characterized by more advanced HIV disease status, for whom the choice between the two treatments might be equivocal.ConclusionsThrough this example, we illustrate how an exploratory heterogeneity of treatment effect analysis might provide further insights into the comparative efficacy of treatments evaluated in a randomized trial. We also highlight some of the issues in implementing and interpreting effect modeling analyses in randomized trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251338558"},"PeriodicalIF":2.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144119117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal inference in randomized trials with partial clustering.","authors":"Joshua R Nugent, Elijah Kakande, Gabriel Chamie, Jane Kabami, Asiphas Owaraganise, Diane V Havlir, Moses Kamya, Laura B Balzer","doi":"10.1177/17407745251333779","DOIUrl":"https://doi.org/10.1177/17407745251333779","url":null,"abstract":"<p><strong>Background: </strong>Participant dependence, if present, must be accounted for in the analysis of randomized trials. This dependence, also referred to as \"clustering,\" can occur in one or more trial arms. This dependence may predate randomization or arise after randomization. We examine three trial designs: one \"fully clustered\" (where all participants are dependent within clusters or groups) and two \"partially clustered\" (where some participants are dependent within clusters and some participants are completely independent of all others).</p><p><strong>Methods: </strong>For these three designs, we (1) use causal models to non-parametrically describe the data generating process and formalize the dependence in the observed data distribution; (2) develop a novel implementation of targeted minimum loss-based estimation for analysis; (3) evaluate the finite-sample performance of targeted minimum loss-based estimation and common alternatives via a simulation study; and (4) apply the methods to real-data from the SEARCH-IPT trial.</p><p><strong>Results: </strong>We show that the two randomization schemes resulting in partially clustered trials have the same dependence structure, enabling use of the same statistical methods for estimation and inference of causal effects. Our novel targeted minimum loss-based estimation approach leverages covariate adjustment and machine learning to improve precision and facilitates estimation of a large set of causal effects. In simulations, we demonstrate that targeted minimum loss-based estimation achieves comparable or markedly higher statistical power than common alternatives for these partially clustered designs. Finally, application of targeted minimum loss-based estimation to real data from the SEARCH-IPT trial resulted in 20%-57% efficiency gains, demonstrating the real-world consequences of our proposed approach.ConclusionsPartially clustered trial analysis can be made more efficient by implementing targeted minimum loss-based estimation, assuming care is taken to account for the dependent nature of the observed data.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251333779"},"PeriodicalIF":2.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143989232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and mitigation of a systematic analysis error in a multicenter dual-energy x-ray absorptiometry study.","authors":"Gayle M Lorenzi, Barbara H Braffett, Ionut Bebu, Victoria R Trapani, Jye-Yu C Backlund, Kaleigh Farrell, Rose A Gubitosi-Klug, Ann V Schwartz","doi":"10.1177/17407745251328257","DOIUrl":"https://doi.org/10.1177/17407745251328257","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background/AimsData integrity in multicenter and longitudinal studies requires implementation of standardized reproducible methods throughout the data collection, analysis, and reporting process. This requirement is heightened when results are shared with participants that may influence health care decisions. A quality assurance plan provides a framework for ongoing monitoring and mitigation strategies when errors occur.MethodsThe Diabetes Control and Complications Trial (1983-1993) and its follow-up study, the Epidemiology of Diabetes Interventions and Complications (1994-present), have characterized risk factors and long-term complications in a type 1 diabetes cohort followed for over 40 years. An ancillary study to assess bone mineral density was implemented across 27 sites, using one of two dual x-ray absorptiometry scanner types. Centrally generated reports were distributed to participants by the sites. A query from a site about results that were incongruent with a single participant's clinical history prompted reevaluation of this scan, revealing a systematic error in the reading of hip scans from one of the two scanner types. A mitigation plan was implemented to correct and communicate the errors to ensure participant safety, particularly among those originally identified as having low bone mineral density scores for whom antiresorptive treatment may have been initiated based on these results.ResultsThe error in the analysis of hip scans from the identified scanner type resulted in lower bone mineral density scores in scans requiring manual deletion of the ischium bone. Hip scans with original T-score ≤ -2.5 (n = 84) acquired on either scanner were reviewed, and reanalyzed if the error was detected. Fourteen scans were susceptible to this error and reanalyzed: nine scans were reclassified from osteoporosis to low bone mineral density, one from low to normal bone mineral density, and four were unchanged. All errors occurred on one scanner type. An integrated communication and intervention plan was implemented. The nine participants whose scans were reclassified from osteoporosis to low bone mineral density were contacted; five were using antiresorptive treatment, all of whom had other risk factors for fracture beyond these scan results. Review of all hip scans with a T-score &gt; -2.5 (n = 371) using this scanner type identified 27 additional hip scans that required reanalysis and potential reclassification: 1 scan was reclassified from osteoporosis to low bone mineral density, 11 from low to normal bone mineral density, and 15 were unchanged.ConclusionThe impact of an analysis error on participant safety, specifically when the initiation of unnecessary treatment may result, necessitated implementation of a coordinated communication and mitigation plan across all clinical centers to ensure consistent messaging and accurate results are provided to participants and their local care providers. This framework may serve as a resource for othe","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251328257"},"PeriodicalIF":2.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143971581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aspirin in primary prevention: Undue reliance on an uninformative trial led to misinformed clinical guidelines.","authors":"Janet Wittes, David L DeMets, KyungMann Kim, Dennis G Maki, Marc A Pfeffer, J Michael Gaziano, Panagiota Kitsantas, Charles H Hennekens, Sarah K Wood","doi":"10.1177/17407745251324866","DOIUrl":"https://doi.org/10.1177/17407745251324866","url":null,"abstract":"<p><p>Best practices for design, conduct, analysis, and interpretation of randomized controlled trials should adhere to rigorous statistical principles. The reliable detection of small effects of treatment should be based on results reported from the primary pre-specified endpoints of large-scale randomized trials designed a priori to test relevant hypotheses. Inference about treatment should not be based on undue reliance on individual small trials, meta-analyses of small trials, subgroups, or post hoc analyses. Failure to follow these principles can lead to conclusions inconsistent with the totality of evidence and to inappropriate recommendations made by guideline committees. The American Heart Association/American College of Cardiology Task Force published guidelines to restrict aspirin for primary prevention of cardiovascular disease to patients below 70 years of age, and the United States Preventive Services Task Force to below 60 years. These guidelines were both unduly influenced by the Aspirin in Reducing Events in the Elderly trial, the results of which were uninformative; they did not provide evidence that aspirin showed no benefit in these age groups. We present several major methodological pitfalls in interpreting the results from the Aspirin in Reducing Events in the Elderly trial of aspirin in the primary prevention of cardiovascular disease. We believe that undue reliance on this uninformative trial has led to misinformed guidelines. Furthermore, given the totality of evidence, we believe that general guidelines for aspirin in the primary prevention of cardiovascular disease are unwarranted. Prescription should be based on an assessment of an individual's benefit to risk; age should be only one component of that assessment.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251324866"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on Wittes et al: Aspirin for primary prevention of CV events - Rationally robust? Statistically significant? Clinically convincing?","authors":"John Gf Cleland, Danyaal Anzar","doi":"10.1177/17407745251324865","DOIUrl":"https://doi.org/10.1177/17407745251324865","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251324865"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consent to recontact for future research using linked primary healthcare data: Outcomes and general practice perceptions from the ATHENA COVID-19 study.","authors":"Kim Greaves, Amanda King, Zoltan Bourne, Jennifer Welsh, Mark Morgan, Maria Ximena Tolosa, Trisha Johnston, Carissa Bonner, Tony Stanton, Rosemary Korda","doi":"10.1177/17407745241304094","DOIUrl":"https://doi.org/10.1177/17407745241304094","url":null,"abstract":"<p><strong>Background: </strong>The ATHENA COVID-19 study was set up to recruit a cohort of patients with linked health information willing to be recontacted in future to participate in clinical trials and also to investigate the outcomes of people with COVID-19 in Queensland, Australia, using consent. This report describes how patients were recruited, their primary care data extracted, proportions consenting, outcomes of using the recontact method to recruit to a study, and experiences interacting with general practices requested to release the primary care data.</p><p><strong>Methods: </strong>Patients diagnosed with COVID-19 from 1 January 2020 to 31 December 2020 were systematically approached to gain consent to have their primary healthcare data extracted from their general practice into a Queensland Health database and linked to other datasets for ethically approved research. Patients were also asked to consent to allow future recontact to discuss participation in clinical trials and other research studies. Patients who consented to recontact were later approached to recruit to a long-COVID study. Patients' general practices were contacted to export the patient files. All patient and general practice interactions were recorded. Outcome measures were proportions of patients consenting to data extraction and research, permission to recontact, proportions of general practices agreeing to participate. A thematic analysis was conducted to assess attitudes regarding export of healthcare data, and the proportions consenting to participate in the long-COVID study were also reported.</p><p><strong>Results: </strong>Of 1212 patients with COVID-19, contact details were available for 1155; 995 (86%) were successfully approached, and 842 (85%) reached a consent decision. Of those who reached a decision, 581 (69%), 615 (73%) and 629 (75%) patients consented to data extraction, recontact, and both, respectively. In all, 382 general practices were contacted, of whom 347 (91%) had an electronic medical record compatible for file export. Of these, 335 (88%) practices agreed to participate, and 12 (3%) declined. In total, 526 patient files were exported. The majority of general practices supported the study and accepted electronic patient consent as legitimate. For the long-COVID study, 376 (90%) of those patients recontacted agreed to have their contact details passed onto the long-COVID study team and 192 (53%) consented to take part in their study.</p><p><strong>Conclusion: </strong>This report describes how primary care data were successfully extracted using consent, and that the majority of patients approached gave permission for their healthcare information to be used for research and be recontacted. The consent-to-recontact concept demonstrated its effectiveness to recruit to new research studies. The majority of general practices were willing to export identifiable patient healthcare data for linkage provided consent had been obtained.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"22 2","pages":"188-199"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143976742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing institutional responsibility in scientific misconduct: A case study of enoximone research by Joachim Boldt.","authors":"Christian J Wiedermann","doi":"10.1177/17407745241297162","DOIUrl":"10.1177/17407745241297162","url":null,"abstract":"<p><strong>Background: </strong>Enoximone, a phosphodiesterase III inhibitor, was approved in Germany in 1989 and initially proposed for heart failure and perioperative cardiac conditions. The research of Joachim Boldt and his supervisor Gunter Hempelmann came under scrutiny after investigations revealed systematic scientific misconduct leading to numerous retractions. Therefore, early enoximone studies by Boldt and Hempelmann from 1988 to 1991 were reviewed.</p><p><strong>Methods: </strong>PubMed-listed publications and dissertations on enoximone from the Justus-Liebig-University of Giessen were analyzed for study design, participant demographics, methods, and outcomes. The data were screened for duplications and inconsistencies.</p><p><strong>Results: </strong>Of seven randomized controlled trial articles identified, two were retracted. Five of the non-retracted articles reported similarly designed studies and included similar patient cohorts. The analysis revealed overlap in patient demographics and reported outcomes and inconsistencies in cardiac index values between trials, suggesting data duplication and manipulation. Several other articles have been retracted. The analysis results of misconduct and co-authorship of retracted studies during Boldt's late formative years indicate inadequate mentorship. The university's slow response in supporting the retraction of publications involving scientific misconduct indicates systemic oversight problems.</p><p><strong>Conclusion: </strong>All five publications analyzed remained active and warrant retraction to maintain the integrity of the scientific record. This analysis highlights the need for improved institutional supervision. The current guidelines of the Committee on Publication Ethics for retraction are inadequate for large-scale scientific misconduct. Comprehensive ethics training, regular audits, and transparent reporting are essential to ensure the credibility of published research.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"239-247"},"PeriodicalIF":2.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}