It is a two-stage Phase III clinical trial design proposed by Wang et al. (2007).
Alternative names: : Adaptive accrual, Adaptive accrual based on interim analysis design, Adaptive Enrichment, Adaptive Modification of Target Population. Adaptive Population Enrichment, Two-stage Adaptive Design, Two-stage adaptive accrual
Adaptations: Information obtained from interim stage is used to broaden the targeted patient population.
Details
Methodology
- A pre-planned total sample size with futility stopping is considered for this two-stage adaptive design. The trial assesses the treatment effect both in the entire population and in the biomarker-positive population.
- Wang et al. (2007) performed a simulation study testing a composite hypothesis; the hypothesis of the global treatment effect and a hypothesis of treatment effect in the biomarker-positive subgroup. A bivariate normal model which incorporates the correlation between the two test statistics for each hypothesis was used. Furthermore, two multiplicity adjustment methods which have a strong control of experimentwise false-positive rate (α = 0.025) were considered in order to test the composite hypotheses of no treatment effect; the first method was the equal split-alpha method which allocate α1 = α2 = 0.0125 and the second method was the Hochberg’s method for multiple testing; a special case of partitioning α which starts with the least significant p-value and investigate the other p-values in a sequential manner until it reaches the most significant one (unequal alpha split).
Statistical/Practical considerations
Advantages
- Can detect a particular biomarker defined subgroup most likely to respond to the novel treatment, thus the efficiency of study design can be increased.
- Can gain more power than a fixed study design under the scenario that the genomic biomarker is predictive of treatment effect (i.e., the value of effect size indicates that there is treatment effect in the biomarker-defined subgroup, e.g. the value of 0.4) than in the case where the genomic biomarker is prognostic (i.e., the scenario where we assume that the value of effect size is zero).
Limitations
- Can result in biased treatment effect estimates.
- Criticised as a design without satisfactory operating characteristics in real practice with a lack of generalizability and information in subgroups which are excluded.
- May augment the duration of the trial depending on the prevalence of the biomarker for the biomarker— defined subgroup which continues to full accrual due to recruitment of many more biomarker-positive patients.
- Requirement of an appropriate futility boundary and rapid and reliable clinical endpoint.
- Conservativeness of futility boundaries as the futility boundary is set to be in the region in which the observed efficacy of the standard of care is greater than that for the experimental treatment.
- Assumes complete confidence in the biomarker.
- Early termination of the entire trial is not permitted.
Key references
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Variations:
Modified Bayesian version of the two-stage design of Wang et al. (2007)
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Description: Karuri and Simon (2007) use a Bayesian framework in order to allow further flexibility for expressing the degree of prior information regarding the utility of a biomarker.
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Alternative names: No alternative names found for this trial design
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Methodology More precisely, posterior distribution of treatment effects within the biomarker-positive and biomarker-negative subgroups based on an interim analysis of first-stage is used in order to come to a decision regarding the recruitment and the continuation of the trial.
- Adavantages and limitations:
Advantages | Limitations |
Can incorporate prior belief regarding the strength of biomarker into the Phase III setting using a Bayesian framework and simultaneously protecting the study population and minimizing the Type I error in the biomarker-positive and biomarker-negative subgroups |
No information found |
Can terminate the study early according to whether the treatment is effective or not in the biomarker-positive subgroup at the interim stage whereas the main design by Wang et al. (2007) does not allow for early termination of the trial. |
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Satisfactory power for testing the biomarker-positive subgroup |
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Enables the reduction of number of biomarker-negative patients for whom a particular treatment tailored to them seems to be ineffective according to biological evidence. |
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The utilized Bayesian formulation sheds light on the nature of inference at the end of the study. | |
Can result in reduction of costs of clinical development. |
- References:
Karuri SW, Simon R. A two-stage Bayesian design for co-development of new drugs and companion diagnostics. Statistics in medicine. 2012;31(10):901–14. pmid:22238151 View Article PubMed/NCBI Google Scholar
Freidlin B, Korn EL, Gray R. Marker Sequential Test (MaST) design. Clinical trials (London, England). 2014;11(1):19–27. View Article PubMed/NCBI Google Scholar