ComPAS: A Bayesian drug combination platform trial design with adaptive shrinkage
By R. Tang, Jing Shen, Ying Yuan. 2018
17 citations recorded by the source corpus.
Combining different treatment regimens provides an effective approach to induce a synergistic treatment effect and overcome resistance to monotherapy. The challenge is that, given the large number of existing monotherapies, the number of possible combinations is huge and new potentially more efficacious compounds may become available any time during drug development. To address this challenge, we propose a flexible Bayesian drug combination platform design with adaptive shrinkage (ComPAS), which allows for dropping futile combinations, graduating effective combinations, and adding new combinations during the course of the trial. A new adaptive shrinkage method is developed to adaptively borrow information across combinations and efficiently identify the efficacious combinations based on Bayesian model selection and hierarchical models. Simulation studies show that ComPAS identifies the effective combinations with higher probability than some existing designs. ComPAS provides an efficient and flexible platform to accelerate drug development in a seamless and timely fashion.
Subfields: Computational Theory and Mathematics, Immunology, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
Citations in the corpus
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, background): “This model allows for the dropping of ineffective drugs and the addition of new combinations to ongoing clinical pipelines based on accumulating trial data in an adaptive and seamless fashion [159].”