Afternoon discussion: Statistical issues in clinical trials conference on dose finding...University of Pennsylvania 16th Annual Conference on Statistical Issues in Clinical Trials, April 8, 2024, Philadelphia, Pennsylvania
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 bar...
| Publicado en: | Clinical Trials Vol. 22; no. 4; pp. 452 - 458 |
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| Autores principales: | , |
| Formato: | proceedings Journal Article |
| Publicado: |
Sage Publications, Ltd.
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187099431&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187099431 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17407745 AB6 jtl: Clinical Trials issn: 17407745 maglogo: N pubinfo: dt: Aug2025 vid: 22 iid: 4 pid: 33180 pub: Sage Publications, Ltd. place: <Blank> artinfo: ui: 187099431 186183379 187099431 187099431 10.1177/17407745251350598 187099431 ppf: 452 ppct: 6 formats: tig: atl: Afternoon discussion: Statistical issues in clinical trials conference on dose finding...University of Pennsylvania 16th Annual Conference on Statistical Issues in Clinical Trials, April 8, 2024, Philadelphia, Pennsylvania aug: au: Heath, Anna Kidwell, Kelley M affil: Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, ON, Canada sug: subj: Discussion Statistics Clinical Trials Study Design Research Subject Recruitment Dose-Response Relationship Drugs Congresses and Conferences Pennsylvania Pennsylvania Research Ethics Pediatric Care ab: 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. pubtype: Academic Journal doctype: proceedings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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