Barnett, Helen and Boix, Oliver and Kontos, Dimitris and Jaki, Thomas (2026) Joint time-to-event partial order continual reassessment method and Joint time-to-event Bayesian logistic regression model: Statistical designs for dual agent phase I/II dose finding studies with late-onset toxicity and activity outcomes. STATISTICAL METHODS IN MEDICAL RESEARCH, 35 (1). pp. 186-204. ISSN 0962-2802, 1477-0334
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Dual agent dose-finding trials study the effect of a combination of more than one agent, where the objective is to find the Maximum Tolerated Dose Combination, the combination of doses of the two agents that is associated with a pre-specified risk of being unsafe. In a Phase I/II setting, the objective is to find a dose combination that is both safe and active, the Optimal Biological Dose, that optimises a criterion based on both safety and activity. Since Oncology treatments are typically given over multiple cycles, both the safety and activity outcome can be considered as late-onset, potentially occurring in the later cycles of treatment. This work proposes two model-based designs for dual-agent dose finding studies with late-onset activity and late-onset toxicity outcomes, the Joint time-to-event (TITE) partial order continual reassessment method and the Joint TITE Bayesian logistic regression model. Their performance is compared alongside a model-assisted comparator in a comprehensive simulation study motivated by a real trial example, with an extension to consider alternative sized dosing grids. It is found that both model-based methods outperform the model-assisted design. Whilst on average the two model-based designs are comparable, this comparability is not consistent across scenarios.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TRIALS; dose-finding; late-onset toxicities; late-onset activity; dual agent; phase I trials; model-based |
| 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 07:43 |
| Last Modified: | 11 Aug 2026 07:43 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66668 |
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