Reverse marker-based strategy is a more efficient strategy as compared to the first and third biomarker-strategy subtype designs for testing the interaction hypothesis of treatment and biomarker.

1. These designs should be used in cases where prior evidence indicates that both experimental and control treatment are effective in treating patients but the optimal strategy has not yet been identified.

• 

  is referred to the required total number of patients per arm (binary outcome), is the expected response rate in the reverse biomarker-based strategy arm and , the expected response rate can be found by calculating the formula , are the assumed response rates of biomarker-positive patients receiving the control treatment and biomarker-negative patients receiving the experimental treatment.

Advantages

• Can estimate directly the marker-strategy response rate.
• Allows the estimation of the effect size of the experimental treatment compared to the control treatment for each biomarker-defined subgroup separately.
• There is no chance that the same treatment will be tailored to biomarker-positive patients who are randomized either to the biomarker-based strategy arm or the reverse marker strategy. Also, there is no possibility of the same treatment assignment to biomarker-negative patients who are randomly assigned to the two biomarker-based strategy arms.
• It has been demonstrated by Eng, 2014 [Google Scholar] that these new types of designs are more than four times more efficient for testing the interaction between treatment and biomarker compared to Biomarker-strategy designs with biomarker assessment in the control arm, Biomarker-strategy designs with randomization in the control arm and the marker stratified designs.

Limitations

• It has been claimed by Baker, 2014 [Google Scholar] that other designs than the reverse marker-based strategy are more appropriate in order to investigate questions which include both treatment effect of biomarker-defined subgroups and the biomarker strategy treatment effect. These designs should allow the estimation of treatment effects within biomarker-defined subgroups as well as the estimation of the global treatment effect.

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