The cross-validated adaptive signature design.
OBJECTIVE
Many anticancer therapies benefit only a subset of treated patients and may be overlooked by the traditional broad eligibility approach to design phase III clinical trials. New biotechnologies such as microarrays can be used to identify the patients that are most likely to benefit from anticancer therapies. However, due to the high-dimensional nature of the genomic data, developing a reliable classifier by the time the definitive phase III trail is designed may not be feasible.


METHODS
Previously, Freidlin and Simon (Clinical Cancer Research, 2005) introduced the adaptive signature design that combines a prospective development of a sensitive patient classifier and a properly powered test for overall effect in a single pivotal trial. In this article, we propose a cross-validation extension of the adaptive signature design that optimizes the efficiency of both the classifier development and the validation components of the design.


RESULTS
The new design is evaluated through simulations and is applied to data from a randomized breast cancer trial.


CONCLUSIONS
The cross-validation approach is shown to considerably improve the performance of the adaptive signature design. We also describe approaches to the estimation of the treatment effect for the identified sensitive subpopulation.
Freidlin
Boris
B
Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland 20892, USA. freidlinb@ctep.nci.nih.gov
Jiang
Wenyu
W
Simon
Richard
R
D000222:Adaptation, Physiological / Q000187:drug effects*; D000970:Antineoplastic Agents / Q000494:pharmacology* / Q000627:therapeutic use; D054316:Biomarkers, Pharmacological / Q000032:analysis; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000235:genetics; D017326:Clinical Trials, Phase III as Topic / Q000379:methods; D003198:Computer Simulation; D003661:Decision Support Techniques; D015195:Drug Design; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D020360:Neoadjuvant Therapy; D018579:Patient Selection; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D054928:Validation Studies as Topic
D000970:Antineoplastic Agents; D054316:Biomarkers, Pharmacological