Prognostic and predictive signatures for treatment decisions
Un Jung Lee1, ShengLi Tzeng2, Yu-Chuan Chen3
1Division of Biochemical Toxicology, National Center for Toxicological Research, US FDA, 3900 NCTR Road, Jefferson, AR 72079, USA.
Aim:
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.
Methods:
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.
Results:
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.
Conclusion:
The proposed model provides more comprehensive characterization of patient subpopulations, and better accuracy in patient treatment assignment.
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