Clinical Trials最新文献

{"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":"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":"367-373"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199287/pdf/","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":"OA","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;p&gt;BackgroundThere 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.MethodsPrEPVacc 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.ResultsA 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.ConclusionIn 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 period of trial follow-up. Other limitations are discussed, along with suggestions for mitiga","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"289-300"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092936/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pivotal trial characteristics and types of endpoints used to support Food and Drug Administration rare disease drug approvals between 2013 and 2022.","authors":"Kyungwan Hong, Bridget Nugent, Abbas Bandukwala, Robert Schuck, York Tomita, Salvatore Pepe, Mary Doi, Scott Winiecki, Kerry Jo Lee","doi":"10.1177/17407745241309318","DOIUrl":"10.1177/17407745241309318","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Background/aimsRare disease drug development faces unique challenges, such as genotypic and phenotypic heterogeneity within small patient populations and a lack of established outcome measures for conditions without previously successful drug development programs. These challenges complicate the process of selecting the appropriate trial endpoints and conducting clinical trials in rare diseases. In this descriptive study, we examined novel drug approvals for non-oncologic rare diseases by the U.S. Food and Drug Administration's Center for Drug Evaluation and Research over the past decade and characterized key regulatory and trial design elements with a focus on the primary efficacy endpoint utilized as the basis of approval.MethodsUsing the Food and Drug Administration's Data Analysis Search Host database, we identified novel new drug applications and biologics license applications with orphan drug designation that were approved between 2013 and 2022 for non-oncologic indications. From Food and Drug Administration review documents and other external databases, we examined characteristics of pivotal trials for the included drugs, such as therapeutic area, trial design, and type of primary efficacy endpoints. Differences in trial design elements associated with primary efficacy endpoint type were assessed such as randomization and blinding. Then, we summarized the primary efficacy endpoint types utilized in pivotal trials by therapeutic area, approval pathway, and whether the disease etiology is well defined.ResultsOne hundred and seven drugs that met our inclusion criteria were approved between 2013 and 2022. Assessment of the 107 drug development programs identified 150 pivotal trials that were subsequently analyzed. The pivotal trials were mostly randomized (80%) and blinded (69.3%). Biomarkers (41.1%) and clinical outcomes (42.1%) were commonly utilized as primary efficacy endpoints. Analysis of the use of clinical trial design elements across trials that utilized biomarkers, clinical outcomes, or composite endpoints did not reveal statistically significant differences. The choice of primary efficacy endpoint varied by the drug's therapeutic area, approval pathway, and whether the indicated disease etiology was well defined. For example, biomarkers were commonly selected as primary efficacy endpoints in hematology drug approvals (70.6%), whereas clinical outcomes were commonly selected in neurology drug approvals (69.6%). Further, if the disease etiology was well defined, biomarkers were more commonly used as primary efficacy endpoints in pivotal trials (44.7%) than if the disease etiology was not well defined (27.3%).DiscussionIn the past 10 years, numerous novel drugs have been approved to treat non-oncologic rare diseases in various therapeutic areas. To demonstrate their efficacy for regulatory approval, biomarkers and clinical outcomes were commonly utilized as primary efficacy endpoints. Biomarkers were not only frequently used as s","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"352-360"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143036919","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":"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":"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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12143638/pdf/","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":"OA","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}