Multi-stage adaptive enrichment trial design with subgroup estimation

Abstract

We consider the problem of estimating the best subgroup and testing for treatment effect in a clinical trial. We define the best subgroup as the subgroup that maximizes a utility function that reflects the trade-off between the subgroup size and the treatment effect. For moderate effect sizes and sample sizes, simpler methods for subgroup estimation worked better than more complex treebased regression approaches. We propose a three-stage design with a weighted inverse normal combination test to test the hypothesis of no treatment effect across the three stages.

The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations

“…The final efficacy analysis will be performed as soon as data on 150 participants from a best subgroup are available, including 60 participants from the a priori best subgroup, 45 participants from the best subgroup estimated in the first precision medicine analysis, and 45 participants from the best subgroup estimated in the second precision medicine analysis. This approach of prospective enrichment increases our chances to establish efficacy of the interventions we investigate (Joshi et al 2020). As discussed in Section 6, conducting the final efficacy analysis on the union of the a priori and updated subgroups ensures control of the Type I error probability for the analysis.…”

Finding the best subgroup with differential treatment effect with multiple outcomes

“…Joshi et al adopt this definition and consider subgroup prevalence raised to the power of 0.75 for the design purposes and 0.5 for estimation. 8,9 Some definitions include the sample size. Chen et al 10 define the best subgroup as the one comprising all individuals with greater CATE and stronger statistical significance of testing the CATE than its complement.…”

Challenges and opportunities in biomarker-driven trials: adaptive randomization

“…Optimal individualized treatment rules built on biomarkers identify a subgroup of patients who benefit from the experimental treatment and aid to determine personalized treatment decisions (4)(5)(6)(7)(8)(9). Fixed or adaptive enrichment designs use the biomarkers in order to restrict enrollment to the patients who are expected to get more benefit from experimental treatment than the control, which magnifies the signal and improves the power to detect the treatment effect (10)(11)(12)(13)(14)(15)(16). Basket trials (e.g., NCI MATCH, NCI MPACT) or umbrella trials (e.g., BATTLE, I-SPY 2, Lung MAP) are implemented under the master protocol based on multiple tumor types for certain genetic mutations or different genetic mutations for a single type of tumor, respectively (17)(18)(19)(20)(21)(22).…”