Barnett, Helen and George, Matthew and Skanji, Donia and Saint-Hilary, Gaelle and Jaki, Thomas and Mozgunov, Pavel (2024) A comparison of model-free phase I dose escalation designs for dual-agent combination therapies. STATISTICAL METHODS IN MEDICAL RESEARCH, 33 (2). pp. 203-226. ISSN 0962-2802, 1477-0334
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It is increasingly common for therapies in oncology to be given in combination. In some cases, patients can benefit from the interaction between two drugs, although often at the risk of higher toxicity. A large number of designs to conduct phase I trials in this setting are available, where the objective is to select the maximum tolerated dose combination. Recently, a number of model-free (also called model-assisted) designs have provoked interest, providing several practical advantages over the more conventional approaches of rule-based or model-based designs. In this paper, we demonstrate a novel calibration procedure for model-free designs to determine their most desirable parameters. Under the calibration procedure, we compare the behaviour of model-free designs to model-based designs in a comprehensive simulation study, covering a number of clinically plausible scenarios. It is found that model-free designs are competitive with the model-based designs in terms of the proportion of correct selections of the maximum tolerated dose combination. However, there are a number of scenarios in which model-free designs offer a safer alternative. This is also illustrated in the application of the designs to a case study using data from a phase I oncology trial.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DRUG-COMBINATION; CLINICAL-TRIALS; REGRESSION; Dose-finding; combination therapies; model-free designs; phase I trials |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 300 Social sciences > 310 General statistics 600 Technology > 610 Medical sciences Medicine |
| 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: | 10 Dec 2025 07:38 |
| Last Modified: | 10 Dec 2025 07:38 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64596 |
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