Clinical Trials最新文献

{"title":"Building a professionally recognised clinical trial workforce: Is it time for an education and accreditation strategy?","authors":"Simone Spark, Prudence Perry, Thobekile Mthethwa-Pitt, Dragan Ilic, Anne Woollett, Sophia Zoungas, Marina Skiba","doi":"10.1177/17407745251328287","DOIUrl":"10.1177/17407745251328287","url":null,"abstract":"<p><p>Evidence-based medicine relies heavily on well-conducted clinical trials. Australia lacks a discipline-specific education pathway to provide the specialist skills necessary to conduct clinical trials to the highest standards. Unlike allied health professionals, clinical trialists who currently possess the specialist skills to conduct clinical trials do not receive professional recognition. The National Health and Medical Research Council defines 'clinical trialist' to include site staff as well as investigators. In this perspective piece, we explore the importance of discipline-specific education in creating a job-ready workforce of clinical trialists; the need for recognition of clinical trialists as an allied health profession in concert with their existing medical, nursing and other professional qualifications and outline a proposed specialist education and accreditation strategy.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"511-516"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12476468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143718174","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":"Design considerations for randomized comparisons of neoadjuvant-adjuvant versus adjuvant-only cancer immunotherapy when tumor measurement schedules do not align (SWOG S1801).","authors":"Megan Othus, Elad Sharon, Michael C Wu, Vernon K Sondak, Antoni Ribas, Sapna P Patel","doi":"10.1177/17407745251321371","DOIUrl":"10.1177/17407745251321371","url":null,"abstract":"<p><p>BackgroundIn 2022, SWOG S1801 was the first trial to demonstrate that single-agent anti-PD-1 checkpoint inhibition used as neoadjuvant-adjuvant therapy leads to significantly improved outcomes compared to adjuvant-only therapy. Endpoints in trials comparing neoadjuvant-adjuvant to adjuvant strategies need special consideration to ensure that event measurement timing is appropriately accounted for in analyses to avoid biased comparisons artificially favoring one arm over another.MethodsThe S1801 trial is used a case study to evaluate the issues involved in selecting endpoints for trials comparing neoadjuvant-adjuvant versus adjuvant-only strategies.ResultsDefinitions and timing of measurement of events is provided. Trial scenarios when recurrence-free versus event-free survival should be used are provided.ConclusionsIn randomized trials comparing neoadjuvant-adjuvant to adjuvant-only strategies, event-free survival endpoints measured from randomization are required for unbiased comparison of the arms. The time at which events can be measured on each arm needs to be carefully considered. If measurement of events occurs at different times on the randomized arms, modified definitions of event-free survival must be used to avoid bias.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"571-577"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12353050/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656289","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":"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":"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":"547-558"},"PeriodicalIF":2.2,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355196/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scoping review of family caregiver roles in cancer clinical trial decision-making.","authors":"Eric C Blackstone, Barbara J Daly, Mark P Aulisio, Jennifer A Dorth, Sana Loue","doi":"10.1177/17407745251366317","DOIUrl":"https://doi.org/10.1177/17407745251366317","url":null,"abstract":"<p><p>BackgroundCancer clinical trials are vital for improving treatments. Clinical trial decision-making has been examined from the perspectives of patients and oncologists, but caregiver perspectives on clinical trials and roles in patient enrollment decisions remain understudied.MethodsThis scoping review assessed the state of current research on caregiver roles in cancer trial enrollment decision-making. A review of empirical literature was conducted in January 2024 using PubMed and Embase. Articles were evaluated using a review instrument to determine the aspect of decision-making evaluated, the roles of caregivers in clinical trial enrollment decision-making, and recommendations based on study results.ResultsA total of 23 articles were included in the review. Studies focused on awareness and attitudes about clinical trials (7 articles), hypothetical willingness to participate in a trial (6 articles), and experiences with decision-making (10 articles). Caregiver roles included supporting and deferring to patient autonomy, communicating with clinicians, and taking on burden to facilitate participation in the trial. Researchers recommended including caregivers in clinical trial enrollment discussions and educational outreach, developing interventions to reduce caregiver burden, and future research on caregiver clinical trial decision-making using the framework of relational autonomy.ConclusionEmpirical research on caregiver roles in clinical trial enrollment decision-making is limited. Findings of this review suggest that caregivers experience tension between their perceived role of supporting the patient's autonomy and their own well-being. More research is needed to understand how caregivers navigate these challenges and identify best practices for their inclusion in clinical trial consent.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251366317"},"PeriodicalIF":2.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991636","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":"Automated data collection from an electronic medical record for a prospective real-world study in patients with retinal disease (VOYAGER).","authors":"Clare Bailey, Ian Pearce, Christiana Dinah, Melanie Dodds, Laia Vidal-Brime, Adam Wilson, Juliet Ellis, Jason Hall, Richard Pohler, Beijue Shi, Dimitar Toshev, Robyn Guymer","doi":"10.1177/17407745251358235","DOIUrl":"https://doi.org/10.1177/17407745251358235","url":null,"abstract":"<p><p>Background/AimsVOYAGER is a prospective, real-world study of treatment patterns and outcomes in retinal diseases. Data collection often requires double entry of routinely captured clinical data, into both site electronic medical records and VOYAGER electronic Case Report Forms (eCRFs), posing a significant time and resource burden and risk of transcription errors. To overcome these challenges, an electronic medical record-to-electronic data capture solution (EMR-to-EDC) was implemented to automate the direct transfer of electronic medical record data into the VOYAGER electronic data capture. This pilot study aimed to establish whether EMR-to-EDC could reduce data entry burden and improve data accuracy.MethodsEMR-to-EDC automatically retrieved study-specific data variables from patients in the mediSIGHT EMR (Medisoft) to pre-populate corresponding eCRF fields within the VOYAGER electronic data capture. Once pre-population of a visit was completed, site staff reviewed the eCRFs and, if required, edited erroneous fields and manually filled in fields that were not pre-populated. This study analyzed eCRF data from two UK VOYAGER sites, collected from patients for whom data were entered manually and patients for whom data were collected using EMR-to-EDC for ~6 months. Outcomes to assess the impact of EMR-to-EDC on data entry burden and accuracy were proportions of eCRF fields which were pre-populated and manually entered for pre-populated visits, and proportion of pre-populated fields overwritten by site staff. Site staff completed surveys to evaluate end-user satisfaction and acceptance of EMR-to-EDC.ResultsOverall, 49 baseline and 143 follow-up visits were registered, of which 146 (baseline: 39; follow-up: 107) were pre-populated by EMR-to-EDC, encompassing 5,017 baseline and 7,371 follow-up visit eCRF fields in total. Of these, 27.9% baseline and 20.5% follow-up visit fields were pre-populated by EMR-to-EDC. A low proportion of pre-populated baseline (8.1%) and follow-up (1.6%) fields were overwritten by site staff. Mean number of queries generated by the electronic data capture per visit was lower for pre-populated patients versus patients whose data were entered manually (baseline: 17.1 versus 22.0 (p = 0.22); follow-up: 4.1 versus 7.1 (p < 0.05)). Survey results demonstrated that site staff generally agreed that EMR-to-EDC helped reduce study data entry burden and collect high quality data. Most staff estimated that EMR-to-EDC saved 11-20 min and 0-10 min per patient for baseline and follow-up visit data entry, respectively, by the end of the study. Main reported benefits of EMR-to-EDC were time-saving and quality data collection; main challenges were high number of system queries generated and pull-through of study-irrelevant data.ConclusionThese results support EMR-to-EDC as an innovative tool to efficiently transfer large amounts of electronic medical record data into study databases while maintaining data quality, with potential to faci","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251358235"},"PeriodicalIF":2.2,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945709","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":"Evaluating re-identification risks scores in publicly available clinical trial datasets: Insights and implications.","authors":"Aryelly Rodriguez, Linda J Williams, Stephanie C Lewis, Pamela Sinclair, Sandra Eldridge, Tracy Jackson, Christopher J Weir","doi":"10.1177/17407745251356423","DOIUrl":"https://doi.org/10.1177/17407745251356423","url":null,"abstract":"<p><p>BackgroundThe motivations to share anonymised datasets from clinical trials within the scientific community are increasing. Many anonymised datasets are now publicly available for secondary research. However, it is uncertain whether they pose a privacy risk to the involved participants.MethodsWe located a broad sample of publicly available, de-identified/anonymised randomised clinical trial datasets from human participants and contacted their owners to request access, following their local procedures. We classified personal data within these datasets, including unique direct identifiers such as date of birth and other personal data that, on their own, does not identify an individual but may do so when combined with each other, such as sex, age and race (indirect identifiers). Combining indirect identifiers forms strata, and adding more identifiers increases granularity by dividing the data into a larger number of smaller strata. The re-identification risk score equations evaluate membership in these strata in three ways: first, by measuring the proportions of participants in strata above predetermined risk threshold levels (Ra); second, by locating the smallest stratum (Rb); third, by estimating the average membership across all strata in a dataset (Rc). The risk scores range from 0 (lowest risk) to 1 (highest risk); they do not aim to re-identify individuals in the datasets and are used for routinely collected health records. If a dataset contained a direct identifier, it automatically scored 1 in all metrics. Conversely, if a dataset contained no direct or up to one indirect identifier, it automatically scored 0 in all metrics. Finally, we explored which characteristics of the datasets were associated with the risk scores and compared the risk scores and their usability.ResultsSeventy datasets from 14 data sources were analysed. Thirty-one datasets were shared with minimal restrictions (open access), while 39 were shared with varying levels of restrictions before access was granted (controlled access). Datasets had, on average, four identifiers and mean risk scores ranging from 0.47 to 0.91. The most common pieces of information present in the datasets that, when combined, may indirectly identify a participant were sex (80%) and age (72.9%).ConclusionsThis study confirms that clinical trial datasets are rich in personal details and that using re-identification risk scores as a measure of this richness is feasible. These scores could inform the anonymisation process of clinical trials datasets regarding their level of granularity prior to releasing them for secondary research. We propose a strategy for employing these scores in the decision-making process for releasing clinical trials datasets.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251356423"},"PeriodicalIF":2.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945762","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":"Incorporating data from multiple ongoing trials for Bayesian two-stage phase II single-arm studies.","authors":"Susan Halabi, Taehwa Choi, Elizabeth Garrett-Mayer, Richard L Schilsky, Lorenzo Trippa","doi":"10.1177/17407745251358233","DOIUrl":"https://doi.org/10.1177/17407745251358233","url":null,"abstract":"<p><strong>Background/aim: </strong>Basket designs have been utilized in recent oncology clinical trials due to an increased interest in precision medicine. One current successful basket trial is the American Society for Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) study, a pragmatic phase II trial where patients are matched based on their tumor genomic profile to treatments that target specific genomic alterations. Despite its success, recruiting patients with rare genomic alterations remains challenging. This study aims to introduce and evaluate a Bayesian approach for integrating data from ongoing independent basket trials that share similar primary aims to improve interim decisions and final analyses and reduce necessary to evaluate treatments.</p><p><strong>Methods: </strong>We introduce a Bayesian two-stage phase II single-arm trial specifically for rare cancers utilizing a hierarchical Bayesian random effects model that incorporate data from ongoing trials. We compare this approach with the standard Simon two-stage design through extensive numerical simulations and apply it to real-world scenarios.</p><p><strong>Results: </strong>Simulation results demonstrate that in rare populations our Bayesian approach has attractive operating characteristics. The simulations show that our approach performs well across a broad set of scenarios with fixed and variable numbers of trials.</p><p><strong>Conclusion: </strong>Our proposed Bayesian two-stage approach effectively integrates data from multiple ongoing basket trials, enhancing the ability to recruit and analyze patients with rare genomic alterations. This approach improves the timing of interim decision-making and final analysis, making it a valuable tool for trials with slow accrual rates.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251358233"},"PeriodicalIF":2.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12373003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144945745","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":"Training the next generation of clinical trial biostatisticians.","authors":"Sameer Parpia, Jacquelyn Dobinson, Anna Heath, Hubert Wong, Kevin Thorpe, Tolulope Sajobi, Shirin Golchi, Lawrence Mbuagbaw, Shun Fu Lee, Thi Ho, Valerie Bishop","doi":"10.1177/17407745251361741","DOIUrl":"https://doi.org/10.1177/17407745251361741","url":null,"abstract":"<p><p>BackgroundThere is a critical shortage of biostatistics expertise and targeted training programs in clinical trials across Canada.MethodsThe Canadian Network for Statistical Training in Trials (CANSTAT), a pan-Canadian, multi-institutional training platform for biostatisticians in clinical trials, was developed to increase clinical trial biostatistics capacity in Canada.ResultsCANSTAT's training program integrates experiential learning through mentorship and placements at clinical trial sites, online workshops, and capacity-building meetings. The curriculum is designed to equip fellows with essential knowledge of clinical trials, technical skills, and practical experience necessary for their growth into professional trial biostatisticians, with several specific and measurable objectives set to achieve this goal. Educational materials, including CANSTAT competencies, reflective exercises, and individual development plans, are provided to monitor progress and ensure that fellows are meeting their academic and professional goals. Currently, CANSTAT has enrolled 19 fellows.ConclusionCANSTAT has developed a training program that equips fellows with essential skills in clinical trial design, conduct and analysis, and interprofessional communication, preparing them to effectively lead biostatistical efforts in clinical trials. By training a new generation of clinical trial biostatisticians, CANSTAT is strengthening Canada's clinical trial enterprise and improving health outcomes.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251361741"},"PeriodicalIF":2.2,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144798429","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":"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":"458-461"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","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":"BARD: A seamless two-stage dose optimization design integrating backfill and adaptive randomization.","authors":"Yixuan Zhao, Rachael Liu, Jianchang Lin, Ying Yuan","doi":"10.1177/17407745251350596","DOIUrl":"10.1177/17407745251350596","url":null,"abstract":"<p><p>One common approach for dose optimization is a two-stage design, which initially conducts dose escalation to identify the maximum tolerated dose, followed by a randomization stage where patients are assigned to two or more doses to further assess and compare their risk-benefit profiles to identify the optimal dose. A limitation of this approach is its requirement for a relatively large sample size. To address this challenge, we propose a seamless two-stage design, BARD (Backfill and Adaptive Randomization for Dose Optimization), which incorporates two key features to reduce sample size and shorten trial duration. The first feature is the integration of backfilling into the stage 1 dose escalation, enhancing patient enrollment and data generation without prolonging the trial. The second feature involves seamlessly combining patients treated in stage 1 with those in stage 2, enabled by covariate-adaptive randomization, to inform the optimal dose and thereby reduce the sample size. Our simulation study demonstrates that BARD reduces the sample size, improves the accuracy of identifying the optimal dose, and maintains covariate balance in randomization, allowing for unbiased comparisons between doses. BARD designs offer an efficient solution to meet the dose optimization requirements set by Project Optimus, with software freely available at www.trialdesign.org.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"393-404"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240483/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583298","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}