A population-finding design with non-parametric Bayesian response model
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About this item
| Description: |
Mueller, P (University of Texas at Austin)
Monday 6th July 2015, 16:15 - 17:00 |
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| Created: | 2015-07-08 16:11 |
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| Collection: | Design and Analysis of Experiments |
| Publisher: | Isaac Newton Institute |
| Copyright: | Mueller, P |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
|
| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Targeted therapies on the basis of genomic aberrations analysis of the tumor have become a mainstream direction of cancer prognosis and treatment. Studies that match patients to targeted therapies for their particular genomic aberrations, across different cancer types, are known as basket trials. For such trials it is important to find and identify the subgroup of patients who can most benefit from an aberration-specific targeted therapy, possibly across multiple cancer types.
We propose an adaptive Bayesian clinical trial design for such subgroup identification and adaptive patient allocation. We start with a decision theoretic approach, then construct a utility function and a flexible non-parametric Bayesian response model. The main features of the proposed design and population finding methods are that we allow for variable sets of covariates to be recorded by different patients and, at least in principle, high order interactions of covariates. The separation of the decision problem and the probability model allows for the use of highly flexible response models. Another important feature is the adaptive allocation of patients to an optimal treatment arm based on posterior predictive probabilities. The proposed approach is demonstrated via extensive simulation studies. |
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