Abstract
Background:Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups.Methods:We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective.Results:Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials.Conclusions:This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously.
Highlights
Response to treatments is highly heterogeneous in cancer
This is a proposal for two independent multi-arm phase II trials to simultaneously test the effectiveness of new therapies and refine biomarkers that are most likely to be the reliable predictors of treatment outcome
Our approach represents a slight perspective shift compared with studies such as BATTLE-2 and I-SPY 2. Both these studies start with a collection of drugs that are deemed interesting for further development in non-small cell lung cancer (NSCLC) and breast cancer, respectively, and try to identify if one or more from a group of preselected biomarkers can predict for clinical benefit
Summary
We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment’s effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. Data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective
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