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. 2015 Sep 1;113(5):699-705.
doi: 10.1038/bjc.2015.278. Epub 2015 Aug 11.

A Bayesian adaptive design for biomarker trials with linked treatments

James M S Wason  1 , Jean E Abraham  2   3   4 , Richard D Baird  2   3   4 , Ioannis Gournaris  5 , Anne-Laure Vallier  3 , James D Brenton  2   4   5 , Helena M Earl  2   3   4 , Adrian P Mander  1
Affiliations

A Bayesian adaptive design for biomarker trials with linked treatments

James M S Wason et al. Br J Cancer. .

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.

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Figures

Figure 1
Figure 1
Mean allocation probability for: (A) linked-BAR design and (B) non-linked BAR design as trial progresses for a B1-positive patient when T1 provides benefit in B1-positive patients and T2 is detrimental in B1-positive patients. Lines represent the average over 2500 replicates.
Figure 2
Figure 2
Mean allocation probability for: (A) linked-BAR design and (B) non-linked BAR design as trial progresses for a B1-positive patient when T2 provides benefit in B1-positive patients and T1 is detrimental in B1-positive patients. Lines represent the average over 2500 replicates.
Figure 3
Figure 3
Power of the four designs to recommend T1 in B1-positive patients as prevalence of B1 changes under scenario 2 in Table 1.
See this image and copyright information in PMC

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