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• W2884974857 abstract "We develop a subgroup selection procedure using both prognostic and predictive biomarkers to identify four patient subpopulations: low- and high-risk responders, and low- and high-risk nonresponders.We utilize three regression models to identify three sets of biomarkers: S, prognostic biomarkers; T, predictive biomarkers; and U, prognostic and predictive biomarkers. The prognostic signature C(S) combines with a predictive signature, either C(T) or C(U), to develop two procedures C(S,T) and C(S,U) for identification of four subgroups.Simulation experiment showed that proposed models for identifying the biomarker sets S and U performed well, as did the procedure C(S,U) for subgroup identification.The proposed model provides more comprehensive characterization of patient subpopulations, and better accuracy in patient treatment assignment." @default.
• W2884974857 created "2018-08-03" @default.
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• W2884974857 date "2018-08-01" @default.
• W2884974857 modified "2023-09-23" @default.
• W2884974857 title "Prognostic and predictive signatures for treatment decisions" @default.
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• W2884974857 doi "https://doi.org/10.2217/bmm-2017-0320" @default.
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