Boris Freidlin1, Edward L Korn, Robert Gray. 1. aBiometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
Abstract
BACKGROUND: New targeted anticancer therapies often benefit only a subset of patients with a given cancer. Definitive evaluation of these agents may require phase III randomized clinical trial designs that integrate evaluation of the new treatment and the predictive ability of the biomarker that putatively determines the sensitive subset. PURPOSE: We propose a new integrated biomarker design, the Marker Sequential Test (MaST) design, that allows sequential testing of the treatment effect in the biomarker subgroups and overall population while controlling the relevant type I error rates. METHODS: After defining the testing and error framework for integrated biomarker designs, we review the commonly used approaches to integrated biomarker testing. We then present a general form of the MaST design and describe how it can be used to provide proper control of false-positive error rates for biomarker-positive and biomarker-negative subgroups. The operating characteristics of the MaST design are compared by analytical methods and simulations to the sequential subgroup-specific design that sequentially assesses the treatment effect in the biomarker subgroups. Practical aspects of MaST design implementation are discussed. RESULTS: The MaST design is shown to have higher power relative to the sequential subgroup-specific design in situations where the treatment effect is homogeneous across biomarker subgroups, while preserving the power for settings where treatment benefit is limited to biomarker-positive subgroup. For example, in the time-to-event setting considered with 30% biomarker-positive prevalence, the MaST design provides up to a 30% increase in power in the biomarker-positive and biomarker-negative subgroups when the treatment benefits all patients equally, while sustaining less than a 2% loss of power against alternatives where the benefit is limited to the biomarker-positive subgroup. LIMITATIONS: The proposed design is appropriate for settings where it is reasonable to assume that the treatment will not be effective in the biomarker-negative patients unless it is effective in the biomarker-positive patients. CONCLUSION: The MaST trial design is a useful alternative to the sequential subgroup-specific design when it is important to consider the treatment effect in the biomarker-positive and biomarker-negative subgroups.
BACKGROUND: New targeted anticancer therapies often benefit only a subset of patients with a given cancer. Definitive evaluation of these agents may require phase III randomized clinical trial designs that integrate evaluation of the new treatment and the predictive ability of the biomarker that putatively determines the sensitive subset. PURPOSE: We propose a new integrated biomarker design, the Marker Sequential Test (MaST) design, that allows sequential testing of the treatment effect in the biomarker subgroups and overall population while controlling the relevant type I error rates. METHODS: After defining the testing and error framework for integrated biomarker designs, we review the commonly used approaches to integrated biomarker testing. We then present a general form of the MaST design and describe how it can be used to provide proper control of false-positive error rates for biomarker-positive and biomarker-negative subgroups. The operating characteristics of the MaST design are compared by analytical methods and simulations to the sequential subgroup-specific design that sequentially assesses the treatment effect in the biomarker subgroups. Practical aspects of MaST design implementation are discussed. RESULTS: The MaST design is shown to have higher power relative to the sequential subgroup-specific design in situations where the treatment effect is homogeneous across biomarker subgroups, while preserving the power for settings where treatment benefit is limited to biomarker-positive subgroup. For example, in the time-to-event setting considered with 30% biomarker-positive prevalence, the MaST design provides up to a 30% increase in power in the biomarker-positive and biomarker-negative subgroups when the treatment benefits all patients equally, while sustaining less than a 2% loss of power against alternatives where the benefit is limited to the biomarker-positive subgroup. LIMITATIONS: The proposed design is appropriate for settings where it is reasonable to assume that the treatment will not be effective in the biomarker-negative patients unless it is effective in the biomarker-positive patients. CONCLUSION: The MaST trial design is a useful alternative to the sequential subgroup-specific design when it is important to consider the treatment effect in the biomarker-positive and biomarker-negative subgroups.