• Their use is recommended when there is compelling evidence that biomarker-positive individuals benefit more from the experimental treatment than the biomarker-negative patients.
• They are appropriate when it is not expected for the novel treatment to be effective in biomarker-negative patients unless it is beneficial for the biomarker-positive patients.

1. As these subgroup-specific designs follow a sequential assessment and thus the designs are composed of two stages, the sample size calculation is also staged.
2. For binary outcome the required number of biomarker-positive patients is the same as for the enrichment designs.
3. For the conduct of these designs, it is important to ensure that there is also an adequate number of biomarker-negative patients for analysis purposes.
4. For time-to-event outcomes, the required number of events for biomarker-positive patients is the same as for the enrichment designs.

• 

   is referred to the required number of biomarker-positive patients (binary outcome),  is the required number of biomarker-positive patients (binary outcome) in the enrichment designs.

• 

   is referred to the required total number of patients (binary outcome),  is the required number of biomarker-positive patients (binary outcome) in the enrichment designs.

• 

   is referred to the required number of biomarker-negative patients (binary outcome),  is the required number of biomarker-positive patients (binary outcome) in the enrichment designs.

• 

   is referred to the required number of events for biomarker-positive patients (time-to-event outcome),  is the required number of events for biomarker-positive patients (time-to-event outcome).

• 

   is referred to the required number of events for biomarker-negative patients (time-to-event outcome),  is the required number of events for biomarker-positive patients (time-to-event outcome), , , are the event rates in biomarker-negative and biomarker-positive control subgroups.

   

Advantages

• Allow for the estimation of treatment effect in biomarker-positive and biomarker-negative subgroups.
• Preserve the overall type I error rates and allow for a smaller sample size than the parallel version.
• Considered as the best direct evidence for clinical decision making as it tests the treatment effectiveness in both the biomarker-positive and biomarker-negative subgroup in a sequential way.
• Do not require larger sample size than the overall/biomarker-positive designs when the prevalence of the biomarker-positive patients is small.

Limitations

• Have less power when there is homogeneity of treatment across the different biomarker defined subgroups as compared to the overall/biomarker-positive designs.
• Need a much larger sample size than the overall/biomarker positive designs if we assume that the treatment effect is relatively homogeneous across the biomarker-defined subgroup.

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