Patients are again randomized between testing strategies (i.e., biomarker-based strategy and non-biomarker-based strategy) but it differs in terms of the timing of biomarker evaluation.
Alternative names: Biomarker-strategy designs with standard control, Direct-predictive biomarker-based, RCT of testing, Test-treatment, Parallel controlled pharmacogenetic diagnostic study, Marker strategy, Marker-based with no randomization in the non-marker-based arm, Classical, Marker-based strategy, Marker strategy designs for prognostic biomarkers
These designs are useful in situations where it is either not feasible or ethical to test the biomarker in the entire population due to several logistical (e.g., specimens not submitted), technical (e.g., assay failure) or clinical reasons (e.g., tumor inaccessible); thus the biomarker status is obtained only in patients who are tailored to the biomarker-based strategy arm.
- The same mathematical formula for sample size calculation assuming a continuous clinical outcome proposed by Young et al. (2010) [Google Scholar] and the formula assuming binary outcome proposed by Eng, 2014 [Google Scholar] for the biomarker-strategy designs with biomarker assessment in the control arm could be applied.
- In terms of survival outcome, the same formula provided for the required number of events in the first version of biomarker-strategy designs (i.e., biomarker-strategy designs with biomarker assessment in the control arm) could be considered.
Sample size Formula
Same formulae as for the ‘Biomarker-strategy designs with biomarker assessment in the control arm’ can be considered.
- Galanis et al., 2011 [Google Scholar] stated that these designs can be attractive when evaluating multiple biomarkers or the predictive value of molecular profiling between several treatment options is to be assessed. Also, Freidlin and Korn, 2010 [Google Scholar] claimed that these biomarker-strategy designs should be used only if researchers want to evaluate a complex biomarker-guided strategy with a variety of treatment options or biomarker categories.
- Biomarker can be validated without including all possible biomarker–treatment combinations (in the non-biomarker-based arm all patients receive only the control treatment instead of receiving both the experimental and control treatment).
- Have the option of testing the biomarker status of patients in the non-biomarker-strategy arm which can aid secondary analyses.
- Able to inform us whether the biomarker is prognostic.
- Criticized for their potential cost increase due to the fact that patients without predicted responsive biomarker are double enrolled in the trial (biomarker-negative patients receive control treatment in both strategy arms).
- Biomarker-positive and biomarker-negative subpopulations might be more imbalanced as compared with the first type of biomarker-strategy designs due to the fact that the randomization to different treatment strategies is performed before the evaluation of the biomarker status (balancing the randomization is useful to ensure that all randomized patients have tissue available). This can happen especially when the number of patients is very small.
- Unable to inform us whether the biomarker is predictive as these designs are able to answer the question about whether the biomarker-based strategy is more effective than standard treatment, irrespective of the biomarker status of the study population.
- The evaluation of the true biomarker by treatment effect is not possible as the biomarker-positive patients receive only the experimental treatment and not the alternative treatment (control treatment). Consequently, these designs cannot detect the case in which the control treatment might be more beneficial for the entire population.
- In case that the number of biomarker-positive patients is very small, then the treatment received will be similar in biomarker-strategy arm and non-biomarker strategy arm. Consequently, the trial might give little information regarding the efficacy of the experimental treatment or it might not be able to detect it. As a result, these types of designs should be used when there is an adequate number of biomarker-positive and biomarker-negative patients.
- Unable to compare directly experimental treatment to control treatment as the aim is to compare not the treatments but the biomarker-strategies.
- Less efficient designs than biomarker-stratified designs and a poor substitute for clinical trials which aim to compare the experimental treatment to control treatment, since it is possible for some patients in both the biomarker-based strategy arm and non-biomarker-based strategy arm to be assigned to the same treatment (due to the existence of biomarker-negative patients in both strategy arms the treatment effect can be diluted). Consequently, as a large overlap of patients receiving the same treatment might have occurred, the comparison of the two biomarker-strategy arms results in a hazard ratio which is forced towards unity, i.e., no treatment effect exists as the effect of experimental versus control treatment is diluted by the biomarker-based treatment selection. For this reason, a large sample size is needed to detect at least a small overall difference in outcomes between the two biomarker-strategy arms.
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