Data Integration in Multi-Stage Adaptive Design

Chair: Freda Cooner (Eli Lilly)
Vice Chair: Qing Xu (FDA)

Speaker: Cyrus Mehta (Cytel)
Title: Adaptive Multi-arm Multi-stage Group Sequential Designs
Abstract: Multi-arm Multi-stage (MAMS) Group Sequential designs are generalizations of Two-arm Multi-stage Group Sequential designs for comparing more than one treatment arm to a common control arm with possible early stopping for efficacy or futility. Adaptive MAMS group sequential designs permit, in addition to early stopping, treatment selection and sample size re-estimation at one or more interim analysis time points. In this presentation I will describe how the group sequential stopping boundaries may be constructed initially, how they may be modified in the presence of adaptive changes, and how strong control of the family wise error rate is achieved. Comparisons will be made to an alternative approach in which independent multiplicity adjusted p-values are combined across the different stages within a closed-testing framework.


Speaker: Heng Xu (Nektar Therapeutics)
Title: Adaptive Endpoints Selection with Application in Rare Disease
Abstract: In rare diseases, there are many unanswered questions that are critical to clinical development. Among them, one important question is how to choose primary endpoints that translate into meaningful improvement of health outcomes for patients while maximizing trial probability of success at the same time. A natural history study is often recommended by regulatory agencies, following this, traditional approach has dampened enthusiasm for many drug developers because it entails much higher cost and longer timeline. We propose to use an innovative design that allows adaptation on primary endpoint(s) so that the learning stage of the disease can be done within the pivotal trial itself through a subset of patients (i.e. informational cohort). The overall family wise error rate will be controlled through the use of combination test following partition test principle. A case example in patients with Pompe disease is used to show that the proposed innovative design maintains robust power across treatment effect scenarios while traditional fixed design bears the high risk of failure due to incorrect endpoint selection. Even if multiple endpoints can be included as primary, the proposed innovative design can still improve power over traditional designs by optimizing alpha allocations in cases with differential treatment effects.


Speaker: Linchen He (Novartis)
Title: Utilizing Real-World Data (RWD) to Inform a Confirmatory Basket Trial (CBT) Design: Studying Use of Rituximab in Autoimmune Diseases
Abstract: We have previously developed a randomized confirmatory basket trial design that controls type I error by indication, yet improves development efficiency, and simulation methods to optimize design parameters based on projected outcomes. In this study, we determine how the inclusion of real-world data (RWD), collected from the electronic health record (EHR), can influence the selection of indications, trial endpoints, and design parameters for a randomized confirmatory basket trial aimed at simultaneously assessing rituximab use in combination with control corticosteroid therapy in multiple autoimmune indications sharing a common pathogenesis. METHODS: Our basket trial was informed by information about off-label use. We performed two types of simulations. First, systematic literature review alone was used for indication selection and effect size estimation. Second, for the simulations including both RWD and literature data, the EHR from Georgetown/Medstar Health was retrospectively analyzed to identify rituximab off-label use. Patient inclusion/exclusion criteria for each disease were created, clinical data extracted, and RWD data sets constructed. Simulations evaluated study metrics, such as power, bias, type I error and benefit-cost ratio. RESULTS: We first selected four indications without RWD. From RWD, we found 657 patients treated with rituximab off-label. Three indications with sufficient RWD and high success likelihood were pursued. Clinical endpoints derived from structured data, such as laboratory values, were easily accessible, but the most relevant information was often found in unstructured physician notes. We will present a comparison of results from simulations informed by both RWD and literature, compared to literature alone. SUMMARY: In this study, RWD informed indication selection and optimization of design parameters for a randomized confirmatory basket trial of rituximab therapy in rare autoimmune diseases.