TY - JOUR
T1 - Clinical trial designs for predictive biomarker validation
T2 - One size does not fit all
AU - Mandrekar, Sumithra J.
AU - Sargent, Daniel J.
N1 - Funding Information:
Supported in part by the National Cancer Institute Grants: Mayo Clinic Cancer Center (CA-15083), and the North Central Cancer Treatment Group (CA-25224).
PY - 2009/5
Y1 - 2009/5
N2 - Traditionally, anatomic staging systems have been used to provide predictions of individual patient outcome and, to a lesser extent, guide the choice of treatment in cancer patients. With targeted therapies, biomarkers have the potential for providing added value through an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient for treatment selection and patient management. Specifically, biomarkers can aid in patient stratification (risk assessment), treatment response identification (surrogate markers), or differential diagnosis (identifying individuals who are likely to respond to specific drugs). In this study, we explore two major topics in relation to the design of clinical trials for predictive marker validation. First, we discuss the appropriateness of an enrichment (i.e., targeted) vs. an unselected design through case studies focusing on the clinical question(s) at hand, the strength of the preliminary evidence, and assay reproducibility. Second, we evaluate the efficiency (total number of events and sample size) of two unselected predictive marker designs for validation of a marker under a wide range of clinically relevant scenarios, exploring the impact of the prevalence of the marker and the hazard ratios for the treatment comparisons. The review and evaluation of these designs represents an essential step toward the goal of personalized medicine because we explicitly seek to explore and evaluate the methodology for the clinical validation of biomarker guided therapy.
AB - Traditionally, anatomic staging systems have been used to provide predictions of individual patient outcome and, to a lesser extent, guide the choice of treatment in cancer patients. With targeted therapies, biomarkers have the potential for providing added value through an integrated approach to prediction using the genetic makeup of the tumor and the genotype of the patient for treatment selection and patient management. Specifically, biomarkers can aid in patient stratification (risk assessment), treatment response identification (surrogate markers), or differential diagnosis (identifying individuals who are likely to respond to specific drugs). In this study, we explore two major topics in relation to the design of clinical trials for predictive marker validation. First, we discuss the appropriateness of an enrichment (i.e., targeted) vs. an unselected design through case studies focusing on the clinical question(s) at hand, the strength of the preliminary evidence, and assay reproducibility. Second, we evaluate the efficiency (total number of events and sample size) of two unselected predictive marker designs for validation of a marker under a wide range of clinically relevant scenarios, exploring the impact of the prevalence of the marker and the hazard ratios for the treatment comparisons. The review and evaluation of these designs represents an essential step toward the goal of personalized medicine because we explicitly seek to explore and evaluate the methodology for the clinical validation of biomarker guided therapy.
KW - Biomarker
KW - Enrichments designs
KW - Predictive marker designs
KW - Unselected designs
UR - http://www.scopus.com/inward/record.url?scp=67651030311&partnerID=8YFLogxK
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U2 - 10.1080/10543400902802458
DO - 10.1080/10543400902802458
M3 - Review article
C2 - 19384694
AN - SCOPUS:67651030311
SN - 1054-3406
VL - 19
SP - 530
EP - 542
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 3
ER -