Predictive biomarkers for treatment selection: statistical considerations.
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.
Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR 72079, USA.|Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan.
James J
JJ
Chen
Department of Public Health, Institute of Epidemiology & Preventive Medicine, National Taiwan University, Taipei, Taiwan.
Tzu-Pin
TP
Lu
Division of Bioinformatics & Biostatistics, National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR 72079, USA.
Yu-Chuan
YC
Chen
Department of Applied Mathematics, Feng Chia University, Taichung, Taiwan.
Wei-Jiun
WJ
Lin
D015415:Biomarkers / Q000032:analysis; D056808:Biostatistics / Q000379:methods*; D003657:Decision Making; D006801:Humans; D012449:Safety; D013812:Therapeutics
D015415:Biomarkers
biomarker adaptive design; personalized and precision medicine; predictive biomarker; predictive classifier; subgroup analysis; subgroup selection