Abstract IA4-2: Design of phase III clinical trials for predictive oncology

Current methods for the design and analysis of phase III clinical trials often results in the approval and use of drugs in broad populations of patients, many of whom do not benefit. This has serious limitations for patients and for health care economics. Current methods are particularly problematic for the development of molecularly targeted drugs which are expected to benefit only a subset of traditionally diagnosed patients. New paradigms for the design and analysis of clinical trials are needed. We will describe recent developments in the prospective use of predictive biomarkers in the design and analysis of phase III clinical trials. The presentation will not be about exploratory analysis of data from clinical trials, but rather on the use of the use of genomic biomarkers in the design and analysis in a sufficiently structured and prospective manner that the conclusions about treatment effects and how they relate to biomarker specified subsets have the degree of reliability normally associated with phase III clinical trials. We will cover the targeted “enrichment” design in which a classifier test result is used as an eligibility criterion. The efficiency of that design and how it depends on the specificity of treatment effect and test performance characteristics will be discussed as well as limitations of that design. We will discuss “stratified designs” in which the test is not used to restrict eligibility but as part of the primary analysis plan of the trial. Specific analysis plans and sample size considerations will be discussed. Both the enrichment design and stratification design require that the classifier be completely specified and analytically validated prior to the start of the pivotal trial. We will discuss more flexible prospective designs for evaluating multiple candidate predictive biomarkers and the recently published cross-validated adaptive signature design which is extremely powerful for combining in one clinical trial the development and internal validation of a predictive classifier utilizing any type of candidate variables. The prospective-retrospective approach using archived tissues will also be described. We will present a new framework for the analysis of clinical trials that incorporates both hypothesis testing and predictive modeling. This framework provides for complementary roles of frequentist and Bayesian methods but requires that models be justified based on predictive accuracy. PDF reprints of the following relevant publications are available at http://brb.nci.nih.gov Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005;23:7332-41. Simon R, Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Res. 2005;10:6759-63; suppl and correction, 2006;12:3229. Simon R. Using genomics in clinical trial design. Clinical Cancer Res. 2008;14:5984-93. Freidlin B, Simon R. Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clinical Cancer Res. 2005;11:7872-8. Jiang W, Freidlin B, Simon R. Biomarker adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect. J Natl Cancer Inst. 2007;99:1036-43. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Nat Cancer Inst. 2009;101:1446-52. Freidlin B, Jiang W, Simon R. The cross-validated adaptive signature design. Clinical Cancer Res. 2010;16(2):691-8. Citation Information: Clin Cancer Res 2010;16(14 Suppl):IA4-2.