Prognostic and predictive signatures for treatment decisions. (19th July 2018)

• Record Type: Journal Article
• Authors: Lee, Un Jung; Tzeng, ShengLi; Chen, Yu-Chuan; Chen, James J
• Is Part Of: Biomarkers in medicine. Volume 12:Number 8(2018)
• Page Start: 849
• Page End: 859
• Publication Date: 2018-07-19
• Subjects: biomarker identification -- clinical trial -- interaction test -- predictive classifiers -- subgroup selection -- tailored therapy; Biochemical markers -- Periodicals; 610.28
• DOI: 10.2217/bmm-2017-0320
• Languages: English
• ISSNs: 1752-0363

Abstract

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.