Chen, Xijin and Mozgunov, Pavel and Baird, Richard D. and Jaki, Thomas (2025) Using circulating tumor DNA as a novel biomarker of efficacy for dose-finding designs in oncology. STATISTICAL METHODS IN MEDICAL RESEARCH, 34 (8). pp. 1665-1683. ISSN 0962-2802, 1477-0334
Full text not available from this repository. (Request a copy)Abstract
Dose-finding trials are designed to identify a safe and potentially effective drug dose and schedule during the early phase of clinical trials. Historically, Bayesian adaptive dose-escalation methods in Phase I trials in cancer have mainly focussed on toxicity endpoints rather than efficacy endpoints. This is partly because efficacy readouts are often not available soon enough for dose escalation decisions. In the last decade, 'liquid biopsy' technologies have been developed, which may provide a readout of treatment response much earlier than conventional endpoints. This paper develops a novel design that uses a biomarker, circulating tumour DNA (ctDNA), with toxicity and activity outcomes in dose-finding studies. We compare the proposed approach based on repeated ctDNA measurement with existing Bayesian adaptive approaches under various scenarios of dose-toxicity, dose-efficacy relationship, and trajectories of regular ctDNA values over time. Simulation results show that the proposed approach can yield significantly shorter trial duration and may improve identification of the target dose. In addition, this approach has the potential to minimise the time individual patients spend on potentially inactive trial therapies. Using two different dose-finding designs, we demonstrate that the way we incorporate biomarker information is broadly applicable across different dose-finding designs and yields notable benefit in both cases.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | II CLINICAL-TRIALS; CONTINUAL REASSESSMENT METHOD; PHASE-I/II; TOXICITY; BINARY; ONSET; MODELS; CRM; Bayesian adaptive trial design; dose-finding studies; biomarker; circulating tumour DNA |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 11 Aug 2026 07:44 |
| Last Modified: | 11 Aug 2026 07:44 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66730 |
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