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Ondra, Thomas, Jobjörnsson, Sebastian, Beckman, Robert A., Burman, Carl-Fredrik, König, Franz, Stallard, Nigel and Posch, Martin (2019) Optimized adaptive enrichment designs. Statistical Methods in Medical Research, 28 (7). pp. 2096-2111. 096228021774731. doi:10.1177/0962280217747312
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WRAP-optimized-adaptive-enrichment-designs-Stallard-2017.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons: Attribution-No Derivative Works 4.0. Download (702Kb) | Preview |
Official URL: http://doi.org/10.1177/0962280217747312
Abstract
Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.
| Item Type: | Journal Article | |||||||||
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| Subjects: | R Medicine > R Medicine (General) | |||||||||
| Divisions: | Faculty of Medicine > Warwick Medical School | |||||||||
| SWORD Depositor: | Library Publications Router | |||||||||
| Library of Congress Subject Headings (LCSH): | Clinical trials -- Statistical methods, Bayesian statistical decision theory, Experimental design, Adaptive sampling (Statistics), Cancer -- Treatment | |||||||||
| Journal or Publication Title: | Statistical Methods in Medical Research | |||||||||
| Publisher: | SAGE Publications | |||||||||
| ISSN: | 1477-0334 | |||||||||
| Official Date: | 1 July 2019 | |||||||||
| Dates: |
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| Volume: | 28 | |||||||||
| Number: | 7 | |||||||||
| Page Range: | pp. 2096-2111 | |||||||||
| Article Number: | 096228021774731 | |||||||||
| DOI: | 10.1177/0962280217747312 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Publisher Statement: | ** From Crossref via Jisc Publications Router. | |||||||||
| Access rights to Published version: | Open Access | |||||||||
| RIOXX Funder/Project Grant: |
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