A Bayesian adaptive design for biomarker trials with linked treatments.
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.
MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge, UK.
James M S
JM
Wason
Department of Oncology, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.|Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK.|Cambridge Experimental Cancer Medicine Centre, Cambridge, UK.
Jean E
JE
Abraham
Department of Oncology, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.|Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK.|Cambridge Experimental Cancer Medicine Centre, Cambridge, UK.
Richard D
RD
Baird
Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.
Ioannis
I
Gournaris
Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK.
Anne-Laure
AL
Vallier
Department of Oncology, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.|Cambridge Experimental Cancer Medicine Centre, Cambridge, UK.|Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.
James D
JD
Brenton
Department of Oncology, University of Cambridge and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.|Cambridge Breast Unit, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK.|Cambridge Experimental Cancer Medicine Centre, Cambridge, UK.
Helena M
HM
Earl
MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge, UK.
Adrian P
AP
Mander
D000970:Antineoplastic Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000378:metabolism; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy / Q000378:metabolism; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016896:Treatment Outcome
D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor