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• W2767071947 abstract "Covariate-adaptive treatment allocation is considered in the situation when a compromise must be made between information (about the dependency of the probability of success of each treatment upon influential covariates) and cost (in terms of number of subjects receiving the poorest treatment). Information is measured through a design criterion for parameter estimation, the cost is additive and is related to the success probabilities. Within the framework of approximate design theory, the determination of optimal allocations forms a compound design problem. We show that when the covariates are i.i.d. with a probability measure $mu$, its solution possesses some similarities with the construction of optimal design measures bounded by $mu$. We characterize optimal designs through an equivalence theorem and construct a covariate-adaptive sequential allocation strategy that converges to the optimum. Our new optimal designs can be used as benchmarks for other, more usual, allocation methods. A response-adaptive implementation is possible for practical applications with unknown model parameters. Several illustrative examples are provided." @default.
• W2767071947 created "2017-11-10" @default.
• W2767071947 creator A5001127202 @default.
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• W2767071947 date "2017-10-01" @default.
• W2767071947 modified "2023-10-14" @default.
• W2767071947 title "Information-regret compromise in covariate-adaptive treatment allocation" @default.
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• W2767071947 doi "https://doi.org/10.1214/16-aos1518" @default.
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