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A Bayesian adaptive design for biomarker trials with linked treatments.

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Wason, James MS 
Abraham, Jean E 
Baird, Richard D 
Gournaris, Ioannis 
Vallier, Anne-Laure 


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.



Antineoplastic Agents, Bayes Theorem, Biomarkers, Tumor, Clinical Trials, Phase II as Topic, Data Interpretation, Statistical, Humans, Neoplasms, Randomized Controlled Trials as Topic, Treatment Outcome

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Br J Cancer

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Springer Science and Business Media LLC
Cancer Research UK (C14303/A17197)
MRC (unknown)
Medical Research Council (MR/J004979/1)
Cancer Research UK (15601)
This work was supported by the Medical Research Council (grant number G0800860) and the NIHR Cambridge Biomedical Research Centre.