ReviewFrontiers in pharmacology2023
Optimizing dose-schedule regimens with bayesian adaptive designs: opportunities and challenges.
Review in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Endothelial dysfunction: A central mechanism linking autosomal dominant polycystic kidney disease and intracranial aneurysms (Review).International journal of molecular medicine · 2026Review
- Guided immunotherapy for residual solid tumor: integrating platelets and CAR T cells to reduce post-surgical recurrence.Biomarker research · 2026Review
- Optimizing Subcutaneous Antibody Dosing Regimens Through Operating Space Maps: rHuPH20 Case Study.Journal of pharmacokinetics and pharmacodynamics · 2026Article
- Harnessing Dietary Tryptophan: Bridging the Gap Between Neurobiology and Psychiatry in Depression Management.International journal of molecular sciences · 2026Review
- From Microbial Switches to Metabolic Sensors: Rewiring the Gut-Brain Kynurenine Circuit.Biomedicines · 2025Review
- The data scientist as a mainstay of the tumor board: global implications and opportunities for the global south.Frontiers in digital health · 2025Article
- Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data.Frontiers in microbiology · 2024Article
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Authors and funding
5 authors.
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Abstract
Due to the small sample sizes in early-phase clinical trials, the toxicity and efficacy profiles of the dose-schedule regimens determined for subsequent trials may not be well established. The recent development of novel anti-tumor treatments and combination therapies further complicates the problem. Therefore, there is an increasing recognition of the essential place of optimizing dose-schedule regimens, and new strategies are now urgently needed. Bayesian adaptive designs provide a potentially effective way to evaluate several doses and schedules simultaneously in a single clinical trial with higher efficiency, but real-world implementation examples of such adaptive designs are still few. In this paper, we cover the critical factors associated with dose-schedule optimization and review the related innovative Bayesian adaptive designs. The assumptions, characteristics, limitations, and application scenarios of those designs are introduced. The review also summarizes some unresolved issues and future research opportunities for dose-schedule optimization.
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Registered trials
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