Clinical Trials最新文献

{"title":"Salvaging information from paused or stopped clinical studies.","authors":"Davey Smith, Thomas Fleming, Sara Gianella, Elizabeth Halloran, Sharon Hillier, Ira Longini, Laura Smeaton, Victor DeGruttola","doi":"10.1177/17407745251353429","DOIUrl":"https://doi.org/10.1177/17407745251353429","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251353429"},"PeriodicalIF":2.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144616591","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":"Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs.","authors":"Weng Kee Wong, Yevgen Ryeznik, Oleksandr Sverdlov, Ping-Yang Chen, Xinying Fang, Ray-Bing Chen, Shouhao Zhou, J Jack Lee","doi":"10.1177/17407745251346396","DOIUrl":"https://doi.org/10.1177/17407745251346396","url":null,"abstract":"<p><p>Metaheuristics are commonly used in computer science and engineering to solve optimization problems, but their potential applications in clinical trial design have remained largely unexplored. This article provides a brief overview of metaheuristics and reviews their limited use in clinical trial settings. We focus on nature-inspired metaheuristics and apply one of its exemplary algorithms, the particle swarm optimization (PSO) algorithm, to find phase I/II designs that jointly consider toxicity and efficacy. As a specific application, we demonstrate the utility of PSO in designing optimal dose-finding studies to estimate the optimal biological dose (OBD) for a continuation-ratio model with four parameters under multiple constraints. Our design improves existing designs by protecting patients from receiving doses higher than the unknown maximum tolerated dose and ensuring that the OBD is estimated with high accuracy. In addition, we show the effectiveness of metaheuristics in addressing more computationally challenging design problems by extending Simon's phase II designs to more than two stages and finding more flexible Bayesian optimal phase II designs with enhanced power.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251346396"},"PeriodicalIF":2.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144616590","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":"Seamless monotherapy-combination phase I dose-escalation model-based design.","authors":"Libby Daniells, Thomas Jaki, Alimu Dayimu, Nikos Demiris, Basu Bristi, Stefan Symeonides, Pavel Mozgunov","doi":"10.1177/17407745251350604","DOIUrl":"https://doi.org/10.1177/17407745251350604","url":null,"abstract":"<p><p>Phase I dose-escalation studies for a single-agent and combination of anti-cancer agents have explored various model-based designs to guide identification of a maximum tolerated dose and recommended phase II dose. This work describes a parallel approach to dose escalation to expedite identification of maximum tolerated doses both for an anti-cancer agent as monotherapy and in combination with another agent. We develop a three-parameter Bayesian logistic regression model that allows for more efficient use of information between monotherapy and combination parts of the study. The model allows the monotherapy and combination data to drive dose escalation of the combination of treatments, reflecting the known dose-toxicity relationship between the monotherapy and combination setting. Through a thorough simulation study in which the proposed model is compared to two comparative approaches, the three-parameter Bayesian logistic regression model is shown to accurately select doses in the target toxicity interval, performing similar to comparative approaches in terms of proportion of target dose/combination selection, while more than halving the proportion of doses selected that were greater than the target toxicity, thereby improving safety concerns.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251350604"},"PeriodicalIF":2.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144616592","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":"17407745251350596"},"PeriodicalIF":2.2,"publicationDate":"2025-07-08","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}

{"title":"Dose-response characterization: A key to success in drug development.","authors":"Frank Bretz, Björn Bornkamp, Thomas Dumortier","doi":"10.1177/17407745251350289","DOIUrl":"https://doi.org/10.1177/17407745251350289","url":null,"abstract":"<p><p>Dose selection is a key component of drug development, yet inadequate dose-response characterization remains a major challenge, contributing to late-stage attrition and post-marketing regulatory commitments. Effective dose-response characterization for both efficacy and safety supports benefit-risk assessments of therapeutic interventions and relies on two main elements: Trial design and trial analysis. In trial design, selecting an appropriate dose range, determining the number of dose levels, and ensuring proper dose spacing are essential to capture both the steep and plateau regions of a dose-response curve. Adaptive trial designs provide additional flexibility to address uncertainties during trial planning and execution, increasing the chances of identifying optimal doses and improving trial efficiency. In trial analysis, modeling approaches support dose-response characterization by utilizing data across dose levels to fit a continuous curve rather than analyzing each dose level separately. Model-based methods, such as Emax modeling or MCP-Mod (which combines multiple comparison procedures and modeling), incorporate assumptions about the dose-response relationship to improve the precision of dose-response and target dose estimation. Additional precision can often be achieved by modeling dose-exposure-response relationships, recognizing that exposure (e.g. drug concentration in the plasma) often mediates the relationship between dose and clinical response. Dose-exposure response models may also enable the prediction of dose-response relationships of alternative regimens (e.g. when applying a different frequency of administration than the tested ones). This article reviews key considerations for the design and analysis of dose-response trials, focusing on strategies to improve decision-making and regulatory alignment.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251350289"},"PeriodicalIF":2.2,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583299","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":"From the ASPREE investigators: Response to Wittes et al.","authors":"John J McNeil, Andrew M Tonkin, Anne B Newman, Jeff D Williamson, Robyn L Woods, Andrew T Chan, Geoffrey A Donnan, Christopher M Reid, Mark R Nelson, Sara E Espinoza, Walter P Abhayaratna, Raj C Shah, Peter Gibbs, Michael E Ernst, Nigel P Stocks, Lawrence J Beilin, Brenda Kirpach, Joanne Ryan, Rory Wolfe, Anne M Murray, Karen L Margolis","doi":"10.1177/17407745251344560","DOIUrl":"https://doi.org/10.1177/17407745251344560","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251344560"},"PeriodicalIF":2.2,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567251","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":"Dose finding in early-phase human immunodeficiency virus type 1 prevention monoclonal antibody clinical trials.","authors":"Yunda Huang, Bo Zhang, Lily Zhang, Bryan T Mayer, Troy Martin, William Hahn, Ollivier Hyrien, Huub C Gelderblom","doi":"10.1177/17407745251347280","DOIUrl":"https://doi.org/10.1177/17407745251347280","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Human immunodeficiency virus type 1 remains a major public health burden with 39 million people living with human immunodeficiency virus type 1 and 1.3 million new diagnoses in 2023, despite the recent approval of multiple antiretroviral-based prevention products. While the development of a safe and effective human immunodeficiency virus type 1 vaccine remains the ultimate goal for controlling the worldwide pandemic, progress has been hindered by unprecedented challenges, including the extraordinary genetic diversity of human immunodeficiency virus type 1, the inability of current vaccines to induce broadly reactive antibody responses, and the lack of clear immune correlates of protection to serve as benchmarks for vaccine development. Passive administration of broadly neutralizing monoclonal antibodies that are engineered versions of naturally occurring antibodies has emerged as a potential complement to current human immunodeficiency virus type 1 prevention modalities. These antibodies are isolated from people with human immunodeficiency virus type 1 and can neutralize a broad range of human immunodeficiency virus type 1 viruses. Importantly, advances in antibody engineering have improved the pharmacokinetics of these monoclonal antibodies, offering potential for lower levels and/or less frequent monoclonal antibody dosing with greater feasibility and accessibility for human immunodeficiency virus type 1 prevention. Evaluating monoclonal antibody candidates in human immunodeficiency virus type 1 prevention trials, dose-finding and optimization requires a careful balance between virus-neutralization coverage, cost considerations, and practical constraints. To achieve this, pharmacokinetic modeling of antibody concentrations over time, combined with pharmacodynamics modeling of the relationship between neuralization titers and prevention efficacy, serves as a core of the statistical framework. In addition, for human immunodeficiency virus type 1 monoclonal antibodies administered to individuals without human immunodeficiency virus type, neutralization titers can be reliably predicted from antibody concentrations, owning to the preservation of neutralization function post-administration of these monoclonal antibodies. Within this framework, the antibody-mediated prevention efficacy trials of VRC01, an human immunodeficiency virus type 1 monoclonal antibody, and a meta-analysis of 16 different monoclonal antibodies in non-human primates provided consistent evidence that neutralization titer is a potential pharmacodynamics biomarker of monoclonal antibody prevention efficacy. These findings support the use of integrated pharmacokinetics/pharmacodynamics modeling as a foundation for dose finding of human immunodeficiency virus type 1 monoclonal antibodies. However, in the context of combination monoclonal antibody regimens, additional challenges arise. The total dose cost, operational feasibility, and the influence of dosing ratios on neutraliz","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251347280"},"PeriodicalIF":2.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567250","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":"Sixteenth Annual University of Pennsylvania conference on statistical issues in clinical trial/optimizing dose-finding across the clinical trials spectrum (morning panel discussion).","authors":"Ken Cheung, Elizabeth Garrett-Mayer","doi":"10.1177/17407745251351291","DOIUrl":"https://doi.org/10.1177/17407745251351291","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251351291"},"PeriodicalIF":2.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567252","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":"Standardising management of consent withdrawal and other clinical trial participation changes: The UKCRC Registered Clinical Trials Unit Network's PeRSEVERE project.","authors":"William J Cragg, Laura Clifton-Hadley, Jeremy Dearling, Susan J Dutton, Katie Gillies, Pollyanna Hardy, Daniel Hind, Søren Holm, Kerenza Hood, Anna Kearney, Rebecca Lewis, Sarah Markham, Lauren Moreau, Tra My Pham, Amanda Roberts, Sharon Ruddock, Mirjana Sirovica, Ratna Sohanpal, Puvan Tharmanathan, Rejina Verghis","doi":"10.1177/17407745251344524","DOIUrl":"https://doi.org/10.1177/17407745251344524","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background/aims: &lt;/strong&gt;Existing regulatory and ethical guidance does not address real-life complexities in how clinical trial participants' level of participation may change. If these complexities are inappropriately managed, there may be negative consequences for trial participants and the integrity of trials they participate in. These concerns have been highlighted over many years, but there remains no single, comprehensive guidance for managing participation changes in ways that address real-life complexities while maximally promoting participant interests and trial integrity. Motivated by the lack of agreed standards, and observed variability in practice, representatives from academic clinical trials units and linked organisations in the United Kingdom initiated the PeRSEVERE project (PRincipleS for handling end-of-participation EVEnts in clinical trials REsearch) to agree on guiding principles and explore how these principles should be implemented.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed the PeRSEVERE principles through discussion and debate within a large, multidisciplinary collaboration, including research professionals and public contributors. We took an inclusive approach to drafting the principles, incorporating new ideas if they were within project scope. Our draft principles were scrutinised through an international consultation survey which focussed on the principles' clarity, feasibility, novelty and acceptability. Survey responses were analysed descriptively (for category questions) and using a combination of deductive and inductive analysis (for open questions). We used predefined rules to guide feedback handling. After finalising the principles, we developed accompanying implementation guidance from several sources.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In total, 280 people from 9 countries took part in the consultation survey. Feedback showed strong support for the principles with 96% of respondents agreeing with the principles' key messages. Based on our predefined rules, it was not necessary to amend our draft principles, but comments were nonetheless used to enhance the final project outputs. Our 17 finalised principles comprise 7 fundamental, 'overarching' principles, 6 about trial design and setup, 2 covering data collection and monitoring, and 2 on trial analysis and reporting.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;We devised a comprehensive set of guiding principles, with detailed practical recommendations, to aid the management of clinical trial participation changes, building on existing ethical and regulatory texts. Our outputs reflect the contributions of a substantial number of individuals, including public contributors and research professionals with various specialisms. This lends weight to our recommendations, which have implications for everyone who designs, funds, conducts, oversees or participates in trials. We suggest our principles could lead to improved standards in clinical trials and better exper","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251344524"},"PeriodicalIF":2.2,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144559362","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":"Afternoon discussion: Statistical issues in clinical trials conference on dose finding.","authors":"Anna Heath, Kelley M Kidwell","doi":"10.1177/17407745251350598","DOIUrl":"https://doi.org/10.1177/17407745251350598","url":null,"abstract":"<p><p>The adoption of innovative, model-based, and computationally intensive clinical trial designs is challenged by barriers including clinician engagement, regulatory acceptance, dissemination beyond major research institutions, and patient accrual. This session explored strategies to overcome these barriers. Key approaches discussed included the development of user-friendly software and interactive platforms to enhance transparency, open sharing of algorithms, and recognition of software contributions in academic publishing. Building collaborations with stakeholders predisposed to innovation, fostering interdisciplinary communication, and producing complementary methodological and clinical publications were emphasized as essential steps. Practical considerations for trials with small sample sizes included the use of adaptive designs, individualized trials, and alternative optimization strategies when traditional theoretical assumptions are infeasible. A major theme of the discussion was the importance of model assumptions in innovative designs. Questions were raised about the sensitivity of results to these assumptions and the robustness of methods, particularly under limited sample sizes. Addressing this requires extensive simulation studies across varied scenarios to assess operating characteristics. The focus should be on achieving clinically meaningful goals-such as identifying effective dose regions-rather than perfect model specification. Speakers emphasized the need to acknowledge and, when feasible, test assumptions post hoc, integrating such verification as secondary objectives in trial design. An iterative scientific process was encouraged, recognizing that trials not only serve immediate clinical goals but also advance broader scientific understanding. Assumptions provide a principled foundation for methodology, but thoughtful scrutiny of their realism was urged, given the risk of relying on overly strong or untestable premises. The potential of metaheuristic algorithms was highlighted for efficiently identifying optimal designs across different model assumptions, supporting robustness evaluations. Practical implementation should adapt optimal designs to stakeholder needs while preserving acceptable statistical efficiency. In sum, advancing the adoption of innovative designs requires improved communication, infrastructure, and methodological transparency, alongside careful evaluation of model assumptions and robustness.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745251350598"},"PeriodicalIF":2.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144505000","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}