It evaluates both the treatment effect in the overall study population and in the biomarker-positive subgroup sequentially.
Alternative names: Biomarker-stratified designs with fall-back analysis, fall-back designs, prospective subgroup designs, sequential designs, other analysis plan designs, Fallback designs
Details
Utility
- This approach is recommended when there is insufficient confidence in the predictive value of the biomarker and that the novel treatment is believed to be effective in all individuals (i.e., the rationale for the biomarker is weak).
- These designs can be used in order to avoid the possibility of missing an important treatment effect in the biomarker-positive patients (with insufficient benefit in the biomarker-negative subgroup).
Methodology
- The sample size should be set in such a way so as to yield adequate power for the overall test at the reduced significance level α1 and for the potential biomarker positive subgroup analysis at significance level α-a1.
- The fall-back version is identical to the parallel version of biomarker-positive and overall strategies in terms of sample sizes and study outcomes, however the difference between these approaches is that the fall-back strategy is useful in settings where a biomarker will be assessed only if the overall population benefit is not promising.
- This strategy can test the treatment effectiveness in biomarker-positive patients even if there is no detected benefit of the novel treatment in the overall population. However, it does not evaluate clearly the treatment benefit in the biomarker-negative subpopulation.
Sample size Formula
- At the first stage, the sample size formula proposed for marker stratified designs aiming to yield appropriate power for the entire population could be considered by using the reduced significance level α1=α-α2.
- At the second stage, the formula proposed for enrichment designs could be applied for the biomarker-positive subgroup by using the reduced significance level α2=α-α1.
Statistical/Practical considerations
Advantages
- Can assess the treatment effect in the biomarker-positive patients, if no benefit is detected in the overall population.
- Can control the overall type I error α.
- Can require smaller sample size as compared to the subgroup-specific designs, especially when we assume that the novel treatment equally benefits both biomarker-defined subgroups.
Limitations
- Cannot control the probability of rejecting the null hypothesis of no treatment effect in the biomarker-negative subgroup when the treatment benefit is restricted to biomarker-positive patients. Consequently, there is a high risk of inappropriately recommending the novel treatment for biomarker-negative patients due to the large treatment effect in biomarker-positive subgroup.
- Can be problematic for determining whether the treatment is beneficial in the biomarker-negative subgroup.
Key references
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