Jones and Holmgren (2007) proposed a Phase II adaptive design by extending the Simon two-stage design. This strategy does not include a control arm yet, consequently it can be considered a single-arm approach exactly like the Simon two-stage approach. The design aims to test a novel treatment which possibly has a different treatment effect in the biomarker-positive versus the biomarker-negative subgroups. This approach requires a pre-defined biomarker with well-established prevalence and permits preliminary determination of efficacy that may be restricted to a particular subset of patients.
Alternative names: Biomarker-adaptive parallel two-stage, Adaptive parallel, Two-parallel Simon, Two-stage design
Adaptations: The design starts with two parallel studies and according to the results of the initial stage we enroll selected or unselected patients during the second stage.
In the first stage of the design, biomarker-negative individuals and biomarker-positive individuals are recruited. An interim analysis is undertaken with its results determining how the design proceeds.
If the number of responses to the novel treatment in the biomarker-negative group, in the first stage , meets or exceeds a cutoff of , then Nun additional unselected individuals are accrued during the second stage (including biomarker-negative responders and biomarker-positive responders). If is less than but the number of responses in the biomarker-positive group in the first stage, , meets or exceeds a cutoff of , then the design enrolls , additional biomarker-positive individuals during the second stage (including responders). If is less than and is less than then the trial stops. Consequently, when the second stage is terminated, a total of N+ and N− biomarker-positive and biomarker-negative individuals, respectively, will have been enrolled, whilst a total of (biomarker-positive group) and (biomarker-negative group) responders will have been observed.
In the case where unselected individuals continued to be accrued during the second stage, the total number of responders in the biomarker-negative subgroup, , is compared to a cutoff, k− whilst the total number of responders in the biomarker-positive subgroup, , is compared to a cutoff, k+. If , then we conclude that the experimental treatment is beneficial in the unselected population; if and then we conclude that the treatment is beneficial only in the biomarker-positive population; if and , then we conclude no treatment benefit. In the case where only biomarker-positive patients continued to be accrued during the second stage, , is compared to a cutoff, . If then we conclude treatment is beneficial in the biomarker-positive subgroup; otherwise we conclude no treatment benefit. The trial stage- and subgroup-specific sample sizes , , Nun, and cutoffs , , k−, k+, are determined so that they control the probability of correct conclusions in the biomarker-positive and unselected patient groups.
Jones and Holmgren (2007) have used the values 34, 34, 32 and 36 for ,, Nun, and respectively and the values 2, 1, 4, 4 and 5 for , , k−, k+ and respectively. As stated by Jones and Holmgren (2007) values for the cutoffs and (equal to 2 and 1 respectively) are obtained from the first stage of the optimal Simon two-stage design. Additionally, in the case where there is preliminary efficacy of the experimental treatment in the unselected population during the first stage of the trial, the study enters the second stage where the values of k− and k+ for decision making need to be defined. Assuming the total number of biomarker-positive subjects (N+) enrolled by the end of the second stage is fixed at its expected value given a known prevalence, the aforementioned values (k− and k+) can be acquired as the minimum values needed for exclusion of the null value from the (1 − α ) × 100% exact Blythe-Still-Casella confidence interval where α ≤0.05; these values can be found using the StatXact software package. However, if the observed total number of biomarker-positive subjects is much different from the expected value, then the cut-offs (k− and k+) can be changed using the confidence interval approach aiming to preserve the desired operating features of the design. Moreover, the value of needed also during the second stage of the trial for decision making can be acquired using either the confidence interval approach or through the calculation of exact binomial probabilities.
- May augment the efficiency of the trial as it allows for early understanding that a particular experimental treatment is beneficial in a specific biomarker defined subgroup.
- Straightforward and simple strategy with reasonable operating characteristics.
- Requires the pre-specification of appropriate response rates in both biomarker-positive and biomarker-negative subgroups which may be difficult.
- Does not allow early termination of the trial for efficacy in biomarker defined subgroups during the first stage of the trial.
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No variations found for this trial design