Innovative Adaptive Study Design in Transfusion-Dependent Beta-Thalassemia: Bayesian Design with Concurrent Randomization and Borrowing from Historical Data

Background Clinical development of new therapies in transfusion-dependent beta-thalassemia has several challenges. Patient enrollment in rare diseases requires multi-center multi-country studies, and the lack of reliable surrogate endpoint for dose selection requires powering for clinical endpoints usually used in Phase 3 trials. An acceptable endpoint from a regulatory perspective which is based on responders analysis, such as proportion of patients experiencing ≥50% reduction in Red Blood Cell (RBC) transfusion burden and a reduction of ≥2 units, requires 12 weeks screening period to establish baseline transfusion burden for reliable comparison. Importantly, higher randomization ratio of treatment:placebo can improve patients' motivation to enroll into a trial, but it is less statistically efficient and requires higher sample size. We designed a Phase-2b, double-blind, randomized, placebo controlled, multi-center study with Vamifeport (NCT04938635) to assess the efficacy and safety of multiple doses of a new therapy in adults with transfusion-dependent beta-thalassemia. The proposed design follows the Bayesian framework with borrowing from published historical control data. The historical control data is used to construct an informative prior for the control arm to reduce the burden of patients randomized to a control arm and improve the trial's efficiency in performing dose selection. Study Design and Methods Adults (18 to 65 y.o.) with documented diagnosis of β-thalassemia or hemoglobin E / β-thalassemia will be randomized to three doses of the investigational drug or placebo plus best supportive care. RBC transfusion dependence is defined as at least 6 RBC Units in the 24 weeks prior to randomization and no transfusion-free period for ≥35 days during that period. The primary endpoint is the proportion of patients experiencing ≥33% reduction of RBC units from baseline and a reduction of ≥2 units assessed from week 13 to week 24. The key secondary endpoints include proportion of patients experiencing ≥33% reduction from week 37 to week 48; proportion of patients experiencing ≥50% reduction over any consecutive 12-week interval from week 1 to week 48 and the mean change from baseline in RBC transfusions (units) from week 13 to week 24. The primary and key-secondary analysis will be conducted in a hierarchical fashion to account for multiplicity. We proposed a Bayesian design with the use of noninformative, or weakly informative, priors for the active dose arms while using a robustified informative prior for the control arm. Historical control data will be "borrowed" in an informative prior for the control arm rate from the Phase 3 trial - BELIEVE. The robustification is required in order to control the level of borrowing depending on the level of prior-data conflict. Prior-data conflict can arise from multiple sources like population heterogeneity between the historical and current study. Therefore, the selection of historical data (BELIEVE trial) addresses similarity in inclusion / exclusion criteria, standard of care etc. The robustification of the informative prior does not take into account prior-data conflict in terms of population or study characteristics but directly focuses on the informative prior of the parameter of interest and the corresponding likelihood of the current data. For example, in the BELIEVE study, out of 112 patients randomized to the control arm, 5 patients (4.5%) had a ≥33% reduction in transfusion burden over 24 weeks. A prior-data conflict may arise if the Phase-2b trial of interest here, suggests that the proportion is substantially different that 4.5% and this can inflate the frequentist Type-I or Type-II error rates examined via simulations. We evaluated Type-I error rates of the proposed design with 5000 Monte-Carlo runs for each scenario of the response rates. Using informative prior with no prior-data conflict the type-I error with no robustification is ≈ 2.4%. As the prior-data conflict increases, without robustification, the type-I error cannot be controlled. However, with a robustification weight of 0.5 the type-I errors can be controlled in line with regulatory requirements. Discussion A proposed Bayesian design with robustified informative prior for the control arm helps reduce patients' burden of randomization to control arm and reduce overall sample size for a rare disease trial when recruitment and trial duration are challenging. Disclosures Muehlemann: Vifor Pharma AG: Consultancy. Mukherjee: Vifor Pharma AG: Consultancy. Taher: Bristol Myers Squibb: Consultancy, Research Funding; Vifor Pharma: Consultancy, Research Funding; Agios Pharmaceuticals: Consultancy; Ionis Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Gudmundsdottir: Vifor Pharma AG: Current Employment. Morin: Vifor Pharma AG: Current Employment. Richard: Vifor Pharma AG: Current Employment.

Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer

Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens.

Clinical effectiveness and safety of baricitinib for the treatment of juvenile idiopathic arthritis-associated uveitis or chronic anterior antinuclear antibody-positive uveitis: study protocol for an open-label, adalimumab active-controlled phase 3 clinical trial (JUVE-BRIGHT)

Background Juvenile idiopathic arthritis (JIA) is the most common pediatric rheumatic disease and the most common systemic disorder associated with uveitis in childhood. Uveitis is more common in JIA patients who are antinuclear antibody (ANA)-positive, have an early-onset disease, and have oligoarticular arthritis. JIA-associated uveitis (JIA-uveitis) is typically anterior, chronic, bilateral, nongranulomatous, and asymptomatic. Visual outcomes in JIA-uveitis have improved with current screening and treatment options; however, many patients fail to respond or do not achieve long-lasting remission. Baricitinib, an oral selective Janus kinase (JAK)1 and 2 inhibitor, may impact key cytokines implicated in the pathogenesis of JIA-uveitis or ANA-positive uveitis, representing a potential novel treatment option for disease management. Methods The multicenter, phase 3 trial will be conducted using an open-label Bayesian design. The study will enroll at least 20 and up to 40 patients aged 2 to <18 years with active JIA-uveitis or chronic ANA-positive uveitis without systemic features. At least 20 patients who have had an inadequate response or intolerance to methotrexate (MTX-IR), but not biologic disease-modifying antirheumatic drugs (bDMARDs), will be randomized (1:1) to open-label baricitinib or adalimumab. Approximately 20 additional patients who are MTX-IR or bDMARD inadequate responders will receive baricitinib treatment. Patients will be treated with once daily oral baricitinib at a fixed dose by age group (4 mg for patients aged ≥6 to <18 years and 2 mg for patients <6 years) or adalimumab (20 mg for patients weighing <30 kg and 40 mg for patients ≥30 kg) as a subcutaneous injection every 2 weeks. Treatment with stable background conventional synthetic DMARDs, low-dose corticosteroids, and/or nonsteroidal anti-inflammatory drugs is allowed. The primary endpoint is the proportion of patients with response at week 24. Patients may continue treatment for up to 5 years. Discussion This is the first pediatric clinical trial to assess the clinical effectiveness and safety of a JAK inhibitor in JIA-uveitis or chronic ANA-positive uveitis. A novel Bayesian design is used to assess the efficacy of baricitinib, including an adalimumab reference arm, in this small patient population with unmet medical need. Trial registration EudraCT 2019-000119-10. Registered on January 4, 2019; NCT04088409. Registered on September 12, 2019

The Bayesian Design of Adaptive Clinical Trials

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.