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. 2008 Feb 1;138(2):308-320.
doi: 10.1016/j.jspi.2007.06.010.

Development and Validation of Biomarker Classifiers for Treatment Selection

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Free PMC article

Development and Validation of Biomarker Classifiers for Treatment Selection

Richard Simon. J Stat Plan Inference. .
Free PMC article

Abstract

Many syndromes traditionally viewed as individual diseases are heterogeneous in molecular pathogenesis and treatment responsiveness. This often leads to the conduct of large clinical trials to identify small average treatment benefits for heterogeneous groups of patients. Drugs that demonstrate effectiveness in such trials may subsequently be used broadly, resulting in ineffective treatment of many patients. New genomic and proteomic technologies provide powerful tools for the selection of patients likely to benefit from a therapeutic without unacceptable adverse events. In spite of the large literature on developing predictive biomarkers, there is considerable confusion about the development and validation of biomarker based diagnostic classifiers for treatment selection. In this paper we attempt to clarify some of these issues and to provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of pharmacogenomic classifiers.

Figures

Figure 1
Figure 1
Randomized clinical trial for evaluating whether use of a biomarker based classifier for treatment selection results in improved clinical outcome. All patients with conventional diagnosis are randomized between biomarker based treatment (M-rx) or standard of care based treatment (SOC-rx). This design is often very inefficient.
Figure 2
Figure 2
Improved clinical trial design for evaluating whether use of a biomarker based classifier for treatment selection results in improved clinical outcome. The biomarker classifier based treatment (M-rx) and standard of care based treatment (SOC-rx) are determined before randomization and patients for whom the two treatment strategies agree are not randomized. This design is often much more efficient than that shown in Figure 1.
Figure 3
Figure 3
Targeted clinical trial design for evaluating a new experimental therapy. A biomarker classifier is developed for identifying those patients most likely to respond to the new treatment (E). Only those patients are randomized to E versus the control treatment. The patients predicted less likely to respond (marker negative) are off study. The targeted design is most useful in cases where the biomarker classifier has a strong biological rationale for identifying responsive patients and where it may not be ethically advisable to expose marker negative patients to the new treatment.
Figure 4
Figure 4
Stratified analysis design for evaluating a new experimental treatment (E) relative to a control (C). The status of a biomarker based classifier of the likelihood of responding to E is utilized in a prospectively specified analysis plan. The biomarker classifier is not just used for stratifying the randomization. Alternative analysis plans are described in the text.

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