Burdon, Abigail J. and Baird, Richard D. and Jaki, Thomas (2024) Adaptive enrichment trial designs using joint modelling of longitudinal and time-to-event data. STATISTICAL METHODS IN MEDICAL RESEARCH, 33 (11-12). pp. 2098-2114. ISSN 0962-2802, 1477-0334
Full text not available from this repository. (Request a copy)Abstract
Adaptive enrichment allows for pre-defined patient subgroups of interest to be investigated throughout the course of a clinical trial. These designs have gained attention in recent years because of their potential to shorten the trial's duration and identify effective therapies tailored to specific patient groups. We describe enrichment trials which consider long-term time-to-event outcomes but also incorporate additional short-term information from routinely collected longitudinal biomarkers. These methods are suitable for use in the setting where the trajectory of the biomarker may differ between subgroups and it is believed that the long-term endpoint is influenced by treatment, subgroup and biomarker. Methods are most promising when the majority of patients have biomarker measurements for at least two time points. We implement joint modelling of longitudinal and time-to-event data to define subgroup selection and stopping criteria and we show that the familywise error rate is protected in the strong sense. To assess the results, we perform a simulation study and find that, compared to the study where longitudinal biomarker observations are ignored, incorporating biomarker information leads to increases in power and the (sub)population which truly benefits from the experimental treatment being enriched with higher probability at the interim analysis. The investigations are motivated by a trial for the treatment of metastatic breast cancer and the parameter values for the simulation study are informed using real-world data where repeated circulating tumour DNA measurements and HER2 statuses are available for each patient and are used as our longitudinal data and subgroup identifiers, respectively.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | SUBGROUP SELECTION; END-POINT; Efficient designs; enrichment; joint modelling; longitudinal data; time-to-event data |
| Subjects: | 000 Computer science, information & general works > 004 Computer science 300 Social sciences > 310 General statistics |
| 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 07:41 |
| Last Modified: | 10 Dec 2025 07:41 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64736 |
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