Predictive biomarkers for treatment selection: statistical considerations. (November 2015)

• Record Type: Journal Article
• Authors: Chen, James J; Lu, Tzu-Pin; Chen, Yu-Chuan; Lin, Wei-Jiun
• Is Part Of: Biomarkers in medicine. Volume 9:Number 11(2015)
• Page Start: 1121
• Page End: 1135
• Publication Date: 2015-11
• Subjects: biomarker adaptive design -- personalized and precision medicine -- predictive biomarker -- predictive classifier -- subgroup analysis -- subgroup selection; Biochemical markers -- Periodicals; 610.28
• DOI: 10.2217/bmm.15.84
• Languages: English
• ISSNs: 1752-0363

Abstract

Predictive biomarkers are developed for treatment selection to identify patients who are likely to benefit from a particular therapy. This review describes statistical methods and discusses issues in the development of predictive biomarkers to enhance study efficiency for detection of treatment effect on the selected responder patients in clinical studies. The statistical procedure for treatment selection consists of three components: biomarker identification, subgroup selection and clinical utility assessment. Major statistical issues discussed include biomarker designs, procedures to identify predictive biomarkers, classification models for subgroup selection, subgroup analysis and multiple testing for clinical utility assessment and evaluation.