Clinical Trials最新文献

{"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><strong>Background/aims: </strong>Randomized 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.</p><p><strong>Methods: </strong>We 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).</p><p><strong>Results: </strong>We 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.</p><p><strong>Conclusion: </strong>Stratified 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":"17407745241304065"},"PeriodicalIF":2.2,"publicationDate":"2025-01-15","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":"Investigating the presence of surgical learning in the Timing of Primary Surgery for cleft palate randomised trial.","authors":"Elizabeth J Conroy, Jane M Blazeby, Girvan Burnside, Jonathan A Cook, Carrol Gamble","doi":"10.1177/17407745241302488","DOIUrl":"https://doi.org/10.1177/17407745241302488","url":null,"abstract":"<p><strong>Background/aims: </strong>When conducting a randomised controlled trial in surgery, it is important to consider surgical learning, where surgeons' familiarity with one, or both, of the interventions increases during the trial. If present, learning may compromise trial validity. We demonstrate a statistical investigation into surgical learning within a trial of cleft palate repair.</p><p><strong>Methods: </strong>The Timing of Primary Surgery compared primary surgery, using the Sommerlad technique, for cleft palate repair delivered at 6 or 12 months of age. Participating surgeons had varying levels of experience with the intervention and in repair across the age groups. Trial design aimed to reduce the surgical learning via pre-trial surgical technique training and balancing the randomisation process by surgeon. We explore residual learning effects by applying visual methods and statistical models to a surgical outcome (fistula formation) and a process indicator (operation time).</p><p><strong>Results: </strong>Notably, 26 surgeons operated on 521 infants. As the trial progressed, operation time reduced for surgeons with no pre-trial Sommerlad experience (n = 2), before plateauing at 30 operations, whereas it remained stable for those with prior experience. Fistula rates remained stable regardless of technique experience. Pre-trial age for primary surgery experience had no impact on either measures.</p><p><strong>Conclusion: </strong>Managing learning effects through design was not fully achieved but balanced between trial arms, and residual effects were minimal. This investigation explores the presence of learning, within a randomised controlled trial that may be valuable for future trials. We recommend such investigations are undertaken to aid trial interpretation and generalisability, and determine success of trial design measures.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241302488"},"PeriodicalIF":2.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945660","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":"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":"https://doi.org/10.1177/17407745241307948","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;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":"17407745241307948"},"PeriodicalIF":2.2,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of correlation structure on sample size requirements of statistical methods for multiple binary outcomes: A simulation study.","authors":"Kanako Fuyama, Kentaro Sakamaki, Kohei Uemura, Isao Yokota","doi":"10.1177/17407745241304706","DOIUrl":"https://doi.org/10.1177/17407745241304706","url":null,"abstract":"<p><strong>Background: </strong>In randomized clinical trials, multiple-testing procedures, composite endpoints, and prioritized outcome approaches are increasingly used to analyze multiple binary outcomes. Previous studies have shown that correlations between outcomes influence their sample size requirements. Although sample size is an important factor affecting the choice of statistical methods, the power and required sample sizes of methods for analyzing multiple binary outcomes have yet to be compared under the influence of outcome correlations.</p><p><strong>Methods: </strong>We conducted simulations to evaluate the power of co-primary and multiple primary endpoints, composite endpoints, and prioritized outcome approaches based on generalized pairwise comparisons with varying correlations, marginal proportions, treatment effects, and number of outcomes. We then conducted a case study on sample size using a clinical trial of a migraine treatment as an example.</p><p><strong>Results: </strong>The correlations significantly affected the statistical power and sample size of composite endpoints. The power and sample size of co-primary endpoints remained relatively stable across different correlations, though their power declined substantially when treatment effects were opposite on some components or more than two components were present. While the correlations influenced the power and sample size of all methods assessed, their direction and degree of influence varied between methods. Notably, the method with the greatest power and smallest sample size also differed depending on the correlations. When the correlations were the same between arms, prioritized outcome approaches usually had higher power and smaller sample sizes than other methods.</p><p><strong>Conclusions: </strong>Anticipated correlations and their uncertainty should be considered when selecting statistical methods. Overall, co-primary endpoints remain a reliable option for evaluating the superiority of all components, although they are unsuitable for assessing the balance between treatment effects pointing in different directions. Generalized pairwise comparisons offer a useful alternative to deal with multiple prioritized outcomes, often providing the smallest sample sizes when the correlation structures are shared between the arms.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241304706"},"PeriodicalIF":2.2,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920978","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":"A framework for sequential monitoring of individual N-of-1 trials and combining results across a series of sequentially monitored N-of-1 trials.","authors":"Subodh Selukar, David K Prince, Susanne May","doi":"10.1177/17407745241304284","DOIUrl":"10.1177/17407745241304284","url":null,"abstract":"<p><strong>Background: </strong>N-of-1 trials compare two or more treatment options for a single participant. These trials have been used to study options for chronic conditions such as arthritis and attention deficit hyperactivity disorder. In addition, they have been suggested as a means to study interventions in rare populations that may not be tractable to include in standard clinical trials, such as treatment options for HIV-positive patients in need of organ transplant. Sequential monitoring of accruing data has been well-studied in traditional clinical trials, but these methods have not yet been implemented in N-of-1 trials. However, the option to validly stop an N-of-1 trial early could deliver faster decisions that could directly improve the patient's health.</p><p><strong>Methods: </strong>In this work, we propose and evaluate a framework to (1) facilitate sequential monitoring in individual N-of-1 trials with a continuous outcome and (2) combine results across a series of already-completed sequentially monitored N-of-1 trials. By employing the block structure common to N-of-1 trials, we suggest that existing approaches to sequential monitoring may be employed when data from one N-of-1 trial are analyzed with a linear mixed-effects model. To combine results across a series of already-completed sequentially monitored N-of-1 trials, we propose combining the naive estimates from constituent trials in a random-effects model with inverse-variance weighting. We evaluate these proposals via simulation.</p><p><strong>Results: </strong>We find that type 1 error can be substantially inflated for N-of-1 trials with a small number of planned blocks but can reach the nominal rate for trials with more planned blocks or those with larger numbers of periods per block or by using a <math><mrow><mi>t</mi></mrow></math>-value correction. For those settings with acceptable type 1 error, sequential monitoring results in similar power and on average earlier stopping compared with trials with no sequential monitoring. And, as expected, we find that including a larger number of constituent trials in a series reduces the mean-squared error of the combined point estimator.</p><p><strong>Conclusion: </strong>Under suitable design considerations, our proposed framework for sequential monitoring can support clinicians in providing important decisions earlier, on average, for patients engaged in N-of-1 trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241304284"},"PeriodicalIF":2.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913822","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":"Central statistical monitoring in clinical trial management: A scoping review.","authors":"Maciej Fronc, Michał Jakubczyk, Sharon B Love, Susan Talbot, Timothy Rolfe","doi":"10.1177/17407745241304059","DOIUrl":"https://doi.org/10.1177/17407745241304059","url":null,"abstract":"<p><strong>Background: </strong>Clinical trials handle a huge amount of data which can be used during the trial to improve the ongoing study conduct. It is suggested by regulators to implement the remote approach to evaluate clinical trials by analysing collected data. Central statistical monitoring helps to achieve that by employing quantitative methods, the results of which are a basis for decision-making on quality issues.</p><p><strong>Methods: </strong>This article presents a scoping review which is based on a systematic and iterative approach to identify and synthesise literature on central statistical monitoring methodology. In particular, we investigated the decision-making processes (with emphasis on quality issues) of central statistical monitoring methodology and its place in the clinical trial workflow. We reviewed papers published over the last 10 years in two databases (Scopus and Web of Science) with a focus on data mining algorithms of central statistical monitoring and its benefit to the quality of trials.</p><p><strong>Results: </strong>As a result, 24 scientific papers were selected for this review, and they consider central statistical monitoring at two levels. First, the perspective of the central statistical monitoring process and its location in the study conduct in terms of quality issues. Second, central statistical monitoring methods categorised into practices applied in the industry, and innovative methods in development. The established methods are discussed through the prism of categories of their usage. In turn, the innovations refer to either research on new methods or extensions to existing ones.</p><p><strong>Discussion: </strong>Our review suggests directions for further research into central statistical monitoring methodology - including increased application of multivariate analysis and using advanced distance metrics - and guidance on how central statistical monitoring operates in response to regulators' requirements.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241304059"},"PeriodicalIF":2.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913824","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":"Exclusion of people from oncology clinical trials based on functional status.","authors":"Nicole D Agaronnik, Mary Linton B Peters, Lisa I Iezzoni","doi":"10.1177/17407745241304114","DOIUrl":"https://doi.org/10.1177/17407745241304114","url":null,"abstract":"<p><strong>Background/aims: </strong>People with disability have higher rates of cancer, excluding skin cancer, compared with people without disability. Food and Drug Administration draft guidelines from 2024 address use of performance status criteria to determine eligibility for clinical trials, advocating for less restrictive thresholds. We examined the exclusion of people with disability from clinical trials based on performance status and other criteria.</p><p><strong>Methods: </strong>We reviewed eligibility criteria in approved interventional Phase III and Phase IV oncology clinical trials listed on ClinicalTrails.gov between 1 January 2019 and 31 December 2023. Functional status thresholds were assessed using the Eastern Cooperative Oncology Group Performance Status Scale and Karnofsky Performance Scale in clinical trial eligibility criteria. Qualitative analysis was used to review eligibility criteria relating to functional impairments or disability.</p><p><strong>Results: </strong>Among 96 oncology clinical trials, approximately 40% had restrictive Eastern Cooperative Oncology Group and Karnofsky Performance Scale thresholds, explicitly including only patients with Eastern Cooperative Oncology Group 0 or 1, or equivalent Karnofsky Performance Scale 70 or greater. Only 20% of studies included patients with Eastern Cooperative Oncology Group 2 and Karnofsky Performance Scale 60. Multiple studies contained miscellaneous eligibility criteria that could potentially exclude people with disability. No studies described making accommodations for people with disability to participate in the clinical trial.</p><p><strong>Conclusion: </strong>Draft Food and Drug Administration guidelines recommend including patients with Eastern Cooperative Oncology Group scores of 2 and Karnofsky Performance Scale scores of 60 in oncology clinical trials. We found that oncology clinical trials often exclude people with more restrictive performance status scores than the draft Food and Drug Administration guidelines, as well as other criteria that relate to disability. These estimates provide baseline information for assessing how the 2024 Food and Drug Administration guidance, if finalized, might affect the inclusion of people with disability in future trials.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241304114"},"PeriodicalIF":2.2,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142913834","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":"Challenges in estimating the counterfactual placebo HIV incidence rate from a registration cohort: The PrEPVacc trial.","authors":"Sheila Kansiime, Christian Holm Hansen, Eugene Ruzagira, Sheena McCormack, Richard Hayes, David Dunn","doi":"10.1177/17407745241304721","DOIUrl":"10.1177/17407745241304721","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;There is increasing recognition that the interpretation of active-controlled HIV prevention trials should consider the counterfactual placebo HIV incidence rate, that is, the rate that would have been observed if the trial had included a placebo control arm. The PrEPVacc HIV vaccine and pre-exposure prophylaxis trial (NCT04066881) incorporated a pre-trial registration cohort partly for this purpose. In this article, we describe our attempts to model the counterfactual placebo HIV incidence rate from the registration cohort.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;PrEPVacc was conducted at four study sites in three African countries. During the set up of the trial, potential participants were invited to join a registration cohort, which included HIV testing every 3 months. The trial included a non-inferiority comparison of two daily, oral pre-exposure prophylaxis regimens (emtricitabine/tenofovir disoproxil fumarate, emtricitabine/tenofovir alafenamide fumarate), administered for a target duration of 26 weeks (until 2 weeks after the third of four vaccinations). We developed a multi-variable Poisson regression model to estimate associations in the registration cohort between HIV incidence and baseline predictors (socio-demographic and behavioural variables) and time-dependent predictors (calendar time, time in follow-up). We then used the estimated regression coefficients together with participant characteristics in the active-controlled pre-exposure prophylaxis trial to predict a counterfactual placebo incidence rate. Sensitivity analyses regarding the effect of calendar period were conducted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 3255 participants were followed up in the registration cohort between July 2018 and October 2022, and 1512 participants were enrolled in the trial between December 2020 and March 2023. In the registration cohort, 106 participants were diagnosed with HIV over 3638 person-years of follow-up (incidence rate = 2.9/100 person-years of follow-up (95% confidence interval: 2.4-3.5)). The final statistical model included terms for study site, gender, age, occupation, sex after using recreational drugs, time in follow-up, and calendar period. The estimated effect of calendar period was very strong, an overall 37% (95% confidence interval: 19-51) decline per year in adjusted analyses, with evidence that this effect varied by study site. In sensitivity analyses investigating different assumptions about the precise effect of calendar period, the predicted counterfactual placebo incidence ranged between 1.2 and 3.7/100 person-years of follow-up.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;In principle, the use of a registration cohort is one of the most straightforward and reliable methods for estimating the counterfactual placebo HIV incidence. However, the predictions in PrEPVacc are complicated by an implausibly large calendar time effect, with uncertainty as to whether this can be validly extrapolated over the","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241304721"},"PeriodicalIF":2.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028103","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":"An open-source SQL database schema for integrated clinical and translational data management in clinical trials.","authors":"Umar Niazi, Charlotte Stuart, Patricia Soares, Vincent Foure, Gareth Griffiths","doi":"10.1177/17407745241304331","DOIUrl":"https://doi.org/10.1177/17407745241304331","url":null,"abstract":"<p><p>Unlocking the power of personalised medicine in oncology hinges on the integration of clinical trial data with translational data (i.e. biospecimen-derived molecular information). This combined analysis allows researchers to tailor treatments to a patient's unique biological makeup. However, current practices within UK Clinical Trials Units present challenges. While clinical data are held in standardised formats, translational data are complex, diverse, and requires specialised storage. This disparity in format creates significant hurdles for researchers aiming to curate, integrate and analyse these datasets effectively. This article proposes a novel solution: an open-source SQL database schema designed specifically for the needs of academic trial units. Inspired by Cancer Research UK's commitment to open data sharing and exemplified by the Southampton Clinical Trials Unit's CONFIRM trial (with over 150,000 clinical data points), this schema offers a cost-effective and practical 'middle ground' between raw data and expensive Secure Data Environments/Trusted Research Environments. By acting as a central hub for both clinical and translational data, the schema facilitates seamless data sharing and analysis. Researchers gain a holistic view of trials, enabling exploration of connections between clinical observations and the molecular underpinnings of treatment response. Detailed instructions for setting up the database are provided. The open-source nature and straightforward design ensure ease of implementation and affordability, while robust security measures safeguard sensitive data. We further showcase how researchers can leverage popular statistical software like R to directly query the database. This approach fosters collaboration within the academic discovery community, ultimately accelerating progress towards personalised cancer therapies.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241304331"},"PeriodicalIF":2.2,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884787","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":"https://doi.org/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":"17407745241297162"},"PeriodicalIF":2.2,"publicationDate":"2024-12-18","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}