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Cancer Therapy: Clinical |
Authors' Affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland
Requests for reprints: Boris Freidlin, Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, 6130 Executive Boulevard, EPN 8122, MSC 7434, Bethesda, MD 20892-7434. Phone: 301-402-0640; Fax: 301-402-0560; E-mail: freidlinb{at}ctep.nci.nih.gov.
Purpose: A new generation of molecularly targeted agents is entering the definitive stage of clinical evaluation. Many of these drugs benefit only a subset of treated patients and may be overlooked by the traditional, broad-eligibility approach to randomized clinical trials. Thus, there is a need for development of novel statistical methodology for rapid evaluation of these agents.
Experimental Design: We propose a new adaptive design for randomized clinical trials of targeted agents in settings where an assay or signature that identifies sensitive patients is not available at the outset of the study. The design combines prospective development of a gene expression–based classifier to select sensitive patients with a properly powered test for overall effect.
Results: Performance of the adaptive design, relative to the more traditional design, is evaluated in a simulation study. It is shown that when the proportion of patients sensitive to the new drug is low, the adaptive design substantially reduces the chance of false rejection of effective new treatments. When the new treatment is broadly effective, the adaptive design has power to detect the overall effect similar to the traditional design. Formulas are provided to determine the situations in which the new design is advantageous.
Conclusion: Development of a gene expression–based classifier to identify the subset of sensitive patients can be prospectively incorporated into a randomized phase III design without compromising the ability to detect an overall effect.
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