Clinical trial designs for predictive marker validation in cancer treatment trials.
Current staging and risk-stratification methods in oncology, while helpful, fail to adequately predict malignancy aggressiveness and/or response to specific treatment. Increased knowledge of cancer biology is generating promising marker candidates for more accurate diagnosis, prognosis assessment, and therapeutic targeting. To apply these exciting results to maximize patient benefit, a disciplined application of well-designed clinical trials for assessing the utility of markers should be used. In this article, we first review the major issues to consider when designing a clinical trial assessing the usefulness of a predictive marker. We then present two classes of clinical trial designs: the Marker by Treatment Interaction Design and the Marker-Based Strategy Design. In the first design, we assume that the marker splits the population into groups in which the efficacy of a particular treatment will differ. This design can be viewed as a classical randomized clinical trial with upfront stratification for the marker. In the second design, after the marker status is known, each patient is randomly assigned either to have therapy determined by their marker status or to receive therapy independent of marker status. The predictive value of the marker is assessed by comparing the outcome of all patients in the marker-based arm to that of all of the patients in the non-marker-based arm. We present detailed sample size calculations for a specific clinical scenario. We discuss the advantages and disadvantages of the two trial designs and their appropriateness to specific clinical situations to assist investigators seeking to design rigorous, marker-based clinical trials.
Sargent
Daniel J
DJ
Mayo Clinic, Kahler 1A, 200 First St, SW, Rochester, MN 55905, USA. sargent.daniel@mayo.edu
Conley
Barbara A
BA
Allegra
Carmen
C
Collette
Laurence
L
D014408:Biomarkers, Tumor; D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000401:mortality / Q000628:therapy*; D011237:Predictive Value of Tests; D011379:Prognosis; D012107:Research Design
D014408:Biomarkers, Tumor