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A Bayesian adaptive design for biomarker trials with linked treatments.

Wason JM, Abraham JE, Baird RD, Gournaris I, Vallier AL, Brenton JD, Earl HM, Mander AP - Br. J. Cancer (2015)

Bottom Line: Response to treatments is highly heterogeneous in cancer.At interim analyses, data from treated patients are used to update the allocation probabilities.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.

View Article: PubMed Central - PubMed

Affiliation: MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge, UK.

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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Related in: MedlinePlus

Power of the four designs to recommend T1 in B1-positive patients as prevalence of B1 changes under scenario 2 in Table 1.
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fig3: Power of the four designs to recommend T1 in B1-positive patients as prevalence of B1 changes under scenario 2 in Table 1.

Mentions: We next examined the power of the four approaches to recommend T1 in B1-positive patients under scenario 2 as the prevalence of B1 changes between 0.1 and 0.5 in increments of 0.025. The results of this are shown in Figure 3. The power of all designs depends strongly on prevalence of B1. The linked-BAR design has below 50% power when the prevalence is 0.1, increasing to 90% power when the prevalence is 0.5.


A Bayesian adaptive design for biomarker trials with linked treatments.

Wason JM, Abraham JE, Baird RD, Gournaris I, Vallier AL, Brenton JD, Earl HM, Mander AP - Br. J. Cancer (2015)

Power of the four designs to recommend T1 in B1-positive patients as prevalence of B1 changes under scenario 2 in Table 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4559835&req=5

fig3: Power of the four designs to recommend T1 in B1-positive patients as prevalence of B1 changes under scenario 2 in Table 1.
Mentions: We next examined the power of the four approaches to recommend T1 in B1-positive patients under scenario 2 as the prevalence of B1 changes between 0.1 and 0.5 in increments of 0.025. The results of this are shown in Figure 3. The power of all designs depends strongly on prevalence of B1. The linked-BAR design has below 50% power when the prevalence is 0.1, increasing to 90% power when the prevalence is 0.5.

Bottom Line: Response to treatments is highly heterogeneous in cancer.At interim analyses, data from treated patients are used to update the allocation probabilities.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.

View Article: PubMed Central - PubMed

Affiliation: MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge, UK.

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.

Show MeSH
Related in: MedlinePlus