Adaptive Trial Design Research Articles

Chapter 17 - Lessons from immunotherapies in multiple sclerosis

The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research.

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

Quantitative systems pharmacology model of erythropoiesis to simulate therapies targeting anemia due to chronic kidney disease.

Anemia induced by chronic kidney disease (CKD) has multiple underlying mechanistic causes and generally worsens as CKD progresses. Erythropoietin (EPO) is a key endogenous protein which increases the number of erythrocyte progenitors that mature into red blood cells that carry hemoglobin (Hb). Recombinant human erythropoietin (rHuEPO) in its native and re-engineered forms is used as a therapeutic to alleviate CKD-induced anemia by stimulating erythropoiesis. However, due to safety risks associated with erythropoiesis-stimulating agents (ESAs), a new class of drugs, prolyl hydroxylase inhibitors (PHIs), has been developed. Instead of administering exogenous EPO, PHIs facilitate the accumulation of HIF-α, which results in the increased production of endogenous EPO. Clinical trials for ESAs and PHIs generally involve balancing decisions related to safety and efficacy by carefully evaluating the criteria for patient selection and adaptive trial design. To enable such decisions, we developed a quantitative systems pharmacology (QSP) model of erythropoiesis which captures key aspects of physiology and its disruption in CKD. Furthermore, CKD virtual populations of varying severities were developed, calibrated, and validated against public data. Such a model can be used to simulate alternative trial protocols while designing phase 3 clinical trials, as well as an asset for reverse translation in understanding emerging clinical data.

A comparison of clinical development pathways to advance tuberculosis regimen development.

Current tuberculosis (TB) regimen development pathways are slow and in urgent need of innovation. We investigated novel phase IIc and seamless phase II/III trials utilizing multi-arm multi-stage and Bayesian response adaptive randomization trial designs to select promising combination regimens in a platform adaptive trial. Clinical trial simulation tools were built using predictive and validated parametric survival models of time to culture conversion (intermediate endpoint) and time to TB-related unfavorable outcome (final endpoint). This integrative clinical trial simulation tool was used to explore and optimize design parameters for aforementioned trial designs. Both multi-arm multi-stage and Bayesian response adaptive randomization designs were able to reliably graduate desirable regimens in ≥ 95% of trial simulations and reliably stop suboptimal regimens in ≥ 90% of trial simulations. Overall, adaptive phase IIc designs reduced patient enrollment by 17% and 25% with multi-arm multi-stage and Bayesian response adaptive randomization designs respectively compared to the conventional sequential approach, while seamless designs reduced study duration by 2.6 and 3.5years respectively (typically ≥ 8.5years for standard sequential approach). In this study, we demonstrate that adaptive trial designs are suitable for TB regimen development, and we provide plausible design parameters for a platform adaptive trial. Ultimately trial design and specification of design parameters will depend on clinical trial objectives. To support decision-making for clinical trial designs in contemporary TB regimen development, we provide a flexible clinical trial simulation tool that can be used to explore and optimize design features and parameters.

Lung Cancer Clinical Trials with a Seamless Phase II/III Design: Systematic Review

Current lung cancer clinical research focuses on biomarkers and personalized treatment strategies. Adaptive clinical trial designs have gained significant ground due to their increased flexibility, compared to the conventional model of drug development from phase I to phase IV trials. One such adaptive approach is the seamless phase II/III design, which has been used to reduce the total sample size and drug development time. In this context, an algorithmic systematic search was conducted in MEDLINE (PUBMED), SCOPUS, EMBASE and Cochrane Central Register of Controlled Trials until 31 June 2022 in order to identify lung cancer trials of systematic treatments that have employed the seamless phase II/III methodology and to describe their characteristics. The search strategy yielded a total of 1420 records that were screened through their title and abstract; 28 eligible trials were included in the systematic review. Based on the study endpoints, the most common subtype included phase II/III trials with inefficacy/futility analyses (61%; 17/28), followed by dose escalation phase II/III trials (18%; 5/28), one multi-arm multi stage trial and 5 trials with other design (18%). Most eligible trials were open-label (71%; 20/27), included patients with non-small cell lung cancer (82%; 23/28), evaluated targeted therapies and/or immunotherapies (82%; 23/28) and recruited patients with advanced disease (89.3%; 25/28). In conclusion, the seamless phase II/III design is a feasible and suitable approach in lung cancer research, with distinct design subcategories according to study endpoints.

HTA204 HTA Assessment of Adaptive Trials for Oncology Drugs in Europe

Adaptive trials allow modifications to the trial design following initiation of the trial, and are of particular interest for research in oncology, increasing trial flexibility, efficiency, and the speed at which novel therapies can be evaluated. This study aimed to identify the likelihood for oncology therapies, investigated via adaptive trial designs, to obtain regulatory approval and positive HTA recommendations.

An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials

Background and ObjectivesAdaptive features may increase flexibility and efficiency of clinical trials, and improve participants’ chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials. MethodsWe provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials. ResultsWe provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs. ConclusionThis overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.

First-in-Human Phase I Trial of a ROR1 Targeting Bispecific T Cell Engager (NVG-111) in Combination with Ibrutinib or As Monotherapy in Subjects with Relapsed Refractory Chronic Lymphocytic Leukaemia (CLL) and Mantle Cell Lymphoma (MCL)

Background: NVG-111 is a first in class, humanized, tandem scFv, ROR1xCD3 bispecific T cell engager that mediates potent killing of ROR1+ tumor by engaging an epitope on the Frizzled domain of ROR1 and redirecting T cell activity via the CD3 binder that has been engineered for lower cytokine release. Methods: This multi-center, open-label, phase I study is evaluating NVG-111 in relapsed/refractory CLL and MCL subjects who have received ≥2 prior systemic therapies. An adaptive trial design was used for dose escalation, starting with accelerated dose titration (ATD) followed by a Bayesian continual reassessment method with overdose control. Each patient received at least one dose of NVG-111, administered as a continuous intravenous infusion (cIV) over 21 days followed by 7 days off drug (=1 cycle). NVG-111 was administered in combination (cohorts 1-4) with ibrutinib to patients who had achieved a stable or partial response to >1 year of ibrutinib therapy, or as monotherapy in patients who had progressed after BTKi / BCL2 (cohort 4b). Measurable residual disease (MRD) was assessed using a flow cytometric method with sensitivity 1e4 (MRD4). Mesoscale discovery electrochemiluminescence assay (MSD-ECLA) was used for pharmacokinetic analysis of NVG-111. Serum cytokine levels were determined using a human high sensitivity cytokine A premixed magnetic Luminex assay. Primary endpoints consisted of safety, tolerability and determination of maximum tolerated dose (MTD)/Recommended Phase 2 Dose (RP2D). Additional end-points included overall response rates as well as duration of response. Results: As of July 2022, 10 subjects (8 males and 2 females, median age 60 years) had been enrolled to the study; three into single subject ATD cohorts, 3 into 30 µg/day flat dosing cohort (cohort 4) and 4 into cohort 4b that entailed a step-up dosing of NVG-111 in cycle 1 only; (first week cycle 1: 3 µg/day, second week, cycle 1: 10 µg/day and week 3, cycle 1: 30 µg/day dose). Eight subjects had CLL and 2 MCL. cIV was well tolerated. Adverse events (AEs) were largely limited to week 1 of cycle 1 and all were reversible. The most common AEs were Grade 1 or 2 nausea (70%), headaches (60%), and fatigue (50%) and thrombocytopenia (30%). Grade 1/2 cytokine release syndrome (CRS) was observed in (40%) of subjects. Grade 3 dose limiting toxicities occurred in two subjects consisting of immune effector cell-associated neurotoxicity syndrome-like symptoms (ICANS) in one (cohort 4) and ALT and AST elevation (cohort 4b) in the other. Both subjects recovered upon stopping NVG-111. Average steady-state serum concentration (Cavg.ss) of NVG-111 in serum of patients who received a 10µg/day and 30µg/day dose were 168-288pg/mL and 425-610pg/mL respectively. This was in line with the predicted drug levels from the single species allometric scaling. Evidence of T cell activation was observed in all evaluable subjects (9/10) with cytokine levels at the 30µg/day dose level being: TNFα, 21±6pg/ml; IL6, 206±178pg/ml; IFNɣ, 7±4pg/ml; and IL10, 47±21pg/ml. Objective clinical responses were observed in 66% of subjects and included 2 MRD4 negative clinical remissions (CR). These two subjects remained in MRD4 negative CRs at 6 months after completion of treatment. Conclusion: Early data shows that NVG-111 is generally well tolerated with a predictable and manageable safety profile. Promising evidence of efficacy was observed which appears to be durable in two subjects with MRD4 negative CR. Dose escalation is ongoing in combination or as monotherapy to determine the MTD/RP2D.

CTNI-38. UPDATE ON GBM AGILE: A GLOBAL, PHASE 2/3 ADAPTIVE PLATFORM TRIAL TO EVALUATE MULTIPLE REGIMENS IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA

Abstract BACKGROUND GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a biomarker based, multi-arm, international, seamless Phase 2/3 response adaptive randomization platform trial designed to rapidly identify experimental therapies that improve overall survival and confirm efficacious experimental therapies and associated biomarker signatures to support new drug approvals and registration. GBM AGILE is a collaboration between academic investigators, patient organizations and industry to support new drug applications for newly diagnosed and recurrent GBM. METHODS The primary objective of GBM AGILE is to identify therapies that effectively improve the overall survival in patients with ND or recurrent GBM. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. Operating under a Master Protocol, GBM AGILE allows multiple drugs from different pharmaceutical/biotech companies to be evaluated simultaneously and/or over time against a common control. New experimental therapies are added as information about promising new drugs is identified while therapies are removed as they complete their evaluation. The master protocol/ trial infrastructure includes efficiencies through an adaptive trial design, shared control arm and operational processes to serve the goal of helping patients receive optimal care in a fast and efficient manner. GBM AGILE has screened over 1200 patients and enrollment rates are 3 to 4 times greater than traditional GBM trials, with active sites averaging 0.75 to 1 patients/sites/month. There are 41 active sites in the US, 4 active sites in Canada and 3 active sites in Europe with more sites anticipated to open across 5 countries in Europe. Expansion to China and Australia are under progress. Through the use of improved and flexible processes, GBM AGILE serves as a global trial that supports the efficient and rapid incorporation and evaluation of new experimental therapies for patients with GBM.Clinical trial information: NCT03970447.

Demonstration of a 'leapfrog' randomized controlled trial as a method to accelerate the development and optimization of psychological interventions.

The scale of the global mental health burden indicates the inadequacy not only of current treatment options, but also the pace of the standard treatment development process. The 'leapfrog' trial design is a newly-developed simple Bayesian adaptive trial design with potential to accelerate treatment development. A first leapfrog trial was conducted to provide a demonstration and test feasibility, applying the method to a low-intensity internet-delivered intervention targeting anhedonia. At the start of this online, single-blind leapfrog trial, participants self-reporting depression were randomized to an initial control arm comprising four weeks of weekly questionnaires, or one of two versions of a four-week cognitive training intervention, imagery cognitive bias modification (imagery CBM). Intervention arms were compared to control on an ongoing basis via sequential Bayesian analyses, based on a primary outcome of anhedonia at post-intervention. Results were used to eliminate and replace arms, or to promote them to become the control condition based on pre-specified Bayes factor and sample size thresholds. Two further intervention arms (variants of imagery CBM) were added into the trial as it progressed. N = 188 participants were randomized across the five trial arms. The leapfrog methodology was successfully implemented to identify a 'winning' version of the imagery CBM, i.e. the version most successful in reducing anhedonia, following sequential elimination of the other arms. The study demonstrates feasibility of the leapfrog design and provides a foundation for its adoption as a method to accelerate treatment development in mental health. Registration: clinicaltrials.gov, NCT04791137.

The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design.

Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the 'Correlated Beta' or 'CoBe' dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives.

A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning

Reinforcement learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacting with an environment rather than from supervised data. We contrast and compare RL with traditional sequential design, focusing on simulation-based Bayesian sequential design (BSD). Recently, there has been an increasing interest in RL techniques for healthcare applications. We introduce two related applications as motivating examples. In both applications, the sequential nature of the decisions is restricted to sequential stopping. Rather than a comprehensive survey, the focus of the discussion is on solutions using standard tools for these two relatively simple sequential stopping problems. Both problems are inspired by adaptive clinical trial design. We use examples to explain the terminology and mathematical background that underlie each framework and map one to the other. The implementations and results illustrate the many similarities between RL and BSD. The results motivate the discussion of the potential strengths and limitations of each approach.

Exploring the perspectives of people with stroke, caregivers and healthcare professionals on the design and delivery of a mHealth adaptive physical activity intervention: a qualitative study protocol

Background: Despite recent advances in acute stroke intervention, secondary prevention strategies are lacking. Physical activity (PA) is the second-largest predictor of stroke and a cornerstone of secondary prevention therapies. Interventions to promote PA post-stroke include components aimed at reducing sedentary behaviour and increasing participation in lifestyle PA and structured exercise. Despite guidelines to adapt PA to individuals’ needs, there is no evidence on the empirical development of adaptive PA interventions post-stroke. This study will explore patient, caregiver and multidisciplinary healthcare professional perspectives on the design and delivery of adaptive, personalised PA interventions, delivered using a smartphone application, following mild-to-moderate stroke. Findings will directly inform the protocol of an experimental trial, using a novel adaptive trial design. Methods: A descriptive qualitative study will be undertaken to inform the design, delivery and subsequent acceptability of a smartphone application to reduce sedentary behaviour and promote PA post-stroke. Data will be collected via one-to-one interviews and focus groups and analysed according to a six-step thematic analysis. Findings will be reported in accordance with the consolidated criteria for reporting qualitative research (COREQ) checklist. One-to-one interviews and focus group interviews will be conducted with three stakeholder groups: 1) People post-stroke, who are independently mobile, without communication and cognitive deficits, living in the community, and without other diagnosed neurological conditions. 2) Caregivers (formal and informal) involved in post-stroke care. 3) Healthcare professionals who are members of multidisciplinary stroke teams. Ethics and dissemination: Ethical approval has been granted by the Faculty of Education and Health Sciences Research Ethics Committee at the University of Limerick [Ref: 2019_10_03_ EHS]. Findings will be shared locally with all stakeholder groups, submitted for publication, and will inform the protocol and conduct for a novel and flexible experimental trial, examining the effectiveness of an adaptive PA intervention post-stroke.

Stepped implementation-to-target: a study protocol of an adaptive trial to expand access to addiction medications

BackgroundIn response to the US opioid epidemic, significant national campaigns have been launched to expand access to `opioid use disorder (MOUD). While adoption has increased in general medical care settings, specialty addiction programs have lagged in both reach and adoption. Elevating the quality of implementation strategy, research requires more precise methods in tailoring strategies rather than a one-size-fits-all-approach, documenting participant engagement and fidelity to the delivery of the strategy, and conducting an economic analysis to inform decision making and policy. Research has yet to incorporate all three of these recommendations to address the challenges of implementing and sustaining MOUD in specialty addiction programs.MethodsThis project seeks to recruit 72 specialty addiction programs in partnership with the Washington State Health Care Authority and employs a measurement-based stepped implementation-to-target approach within an adaptive trial design. Programs will be exposed to a sequence of implementation strategies of increasing intensity and cost: (1) enhanced monitoring and feedback (EMF), (2) 2-day workshop, and then, if outcome targets are not achieved, randomization to either internal facilitation or external facilitation. The study has three aims: (1) evaluate the sequential impact of implementation strategies on target outcomes, (2) examine contextual moderators and mediators of outcomes in response to the strategies, and (3) document and model costs per implementation strategy. Target outcomes are organized by the RE-AIM framework and the Addiction Care Cascade.DiscussionThis implementation project includes elements of a sequential multiple assignment randomized trial (SMART) design and a criterion-based design. An innovative and efficient approach, participating programs only receive the implementation strategies they need to achieve target outcomes. Findings have the potential to inform implementation research and provide key decision-makers with evidence on how to address the opioid epidemic at a systems level.Trial registrationThis trial was registered at ClinicalTrials.gov (NCT05343793) on April 25, 2022.

Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment.

For randomized clinical trials where a single, primary, binary endpoint would require unfeasibly large sample sizes, composite endpoints (CEs) are widely chosen as the primary endpoint. Despite being commonly used, CEs entail challenges in designing and interpreting results. Given that the components may be of different relevance and have different effect sizes, the choice of components must be made carefully. Especially, sample size calculations for composite binary endpoints depend not only on the anticipated effect sizes and event probabilities of the composite components but also on the correlation between them. However, information on the correlation between endpoints is usually not reported in the literature which can be an obstacle for designing future sound trials. We consider two-arm randomized controlled trials with a primary composite binary endpoint and an endpoint that consists only of the clinically more important component of the CE. We propose a trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. Especially, we consider a decision rule to select between a CE and its most relevant component as primary endpoint. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation, and the expected effect sizes of the composite components. We investigate the statistical power and significance level under the proposed design through simulations. We show that the adaptive design is equally or more powerful than designs without adaptive modification on the primary endpoint. Besides, the targeted power is achieved even if the correlation is misspecified at the planning stage while maintaining the type 1 error. All the computations are implemented in R and illustrated by means of a peritoneal dialysis trial.

Acipimox in Mitochondrial Myopathy (AIMM): study protocol for a randomised, double-blinded, placebo-controlled, adaptive design trial of the efficacy of acipimox in adult patients with mitochondrial myopathy

BackgroundMitochondrial disease is a heterogenous group of rare, complex neurometabolic disorders. Despite their individual rarity, collectively mitochondrial diseases represent the most common cause of inherited metabolic disorders in the UK; they affect 1 in every 4300 individuals, up to 15,000 adults (and a similar number of children) in the UK. Mitochondrial disease manifests multisystem and isolated organ involvement, commonly affecting those tissues with high energy demands, such as skeletal muscle. Myopathy manifesting as fatigue, muscle weakness and exercise intolerance is common and debilitating in patients with mitochondrial disease. Currently, there are no effective licensed treatments and consequently, there is an urgent clinical need to find an effective drug therapy.AimTo investigate the efficacy of 12-week treatment with acipimox on the adenosine triphosphate (ATP) content of skeletal muscle in patients with mitochondrial disease and myopathy.MethodsAIMM is a single-centre, double blind, placebo-controlled, adaptive designed trial, evaluating the efficacy of 12 weeks’ administration of acipimox on skeletal muscle ATP content in patients with mitochondrial myopathy. Eligible patients will receive the trial investigational medicinal product (IMP), either acipimox or matched placebo. Participants will also be prescribed low dose aspirin as a non-investigational medical product (nIMP) in order to protect the blinding of the treatment assignment.Eighty to 120 participants will be recruited as required, with an interim analysis for sample size re-estimation and futility assessment being undertaken once the primary outcome for 50 participants has been obtained. Randomisation will be on a 1:1 basis, stratified by Fatigue Impact Scale (FIS) (dichotomised as < 40, ≥ 40). Participants will take part in the trial for up to 20 weeks, from screening visits through to follow-up at 16 weeks post randomisation.The primary outcome of change in ATP content in skeletal muscle and secondary outcomes relating to quality of life, perceived fatigue, disease burden, limb function, balance and walking, skeletal muscle analysis and symptom-limited cardiopulmonary fitness (optional) will be assessed between baseline and 12 weeks.DiscussionThe AIMM trial will investigate the effect of acipimox on modulating muscle ATP content and whether it can be repurposed as a new treatment for mitochondrial disease with myopathy.Trial registrationEudraCT2018-002721-29. Registered on 24 December 2018,ISRCTN 12895613. Registered on 03 January 2019, https://www.isrctn.com/search?q=aimm

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

BackgroundPlatform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.MethodsTo address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics.Results and conclusionsOur proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

P11.65.B GBM AGILE: A global, phase 2/3 adaptive platform trial to evaluate multiple treatment regimens in newly diagnosed and recurrent glioblastoma

Abstract Background GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a biomarker based, multi-arm, international, seamless Phase 2/3 Response Adaptive Randomization platform trial designed to rapidly identify experimental therapies that improve overall survival and confirm efficacious experimental therapies and associated biomarker signatures to support new drug approvals and registration. It is a collaboration between academic investigators, patient organizations and industry, under the sponsorship of the non-profit organization, Global Coalition for Adaptive Research, to support new drug applications for newly diagnosed and recurrent GBM. Material and Methods The primary objective of GBM AGILE is to identify therapies that effectively improve overall survival in patients with newly diagnosed or recurrent GBM. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. Operating under a master protocol, GBM AGILE allows multiple drugs from different pharmaceutical companies to be evaluated simultaneously and/or over time against a common control arm. Based on performance, a drug may graduate and move to a Stage 2 (Phase 3) within the trial, and the totality of the data can be used for a new drug application and registration process. New experimental therapies are added as information about promising new drugs is identified while other therapies are removed as they complete their evaluation. The master protocol/ trial infrastructure includes efficiencies through an adaptive trial design, shared control arm and operational processes such as risk-based monitoring and enhanced remote activities. With its adaptable structure, GBM AGILE has continued trial activation, inclusion of new investigational therapies, and enrollment globally through the challenges of a global pandemic.GBM AGILE provides an efficient mechanism to screen and develop robust information regarding the efficacy of proposed novel therapeutics and associated biomarkers for GBM and to quickly move therapies and biomarkers into clinic. GBM AGILE received initial approval from the United States FDA in April 2019, and in Europe through the Voluntary Harmonization Procedure (VHP) in April, 2021. As of 2022, AGILE has screened over 1000 patients studying multiple investigational treatments. Enrollment rates are 3 to 4 times greater than traditional GBM trials, with active sites averaging 0.75 to 1 patients/site/month. Currently, there are 41 sites activated in the US, 4 in Canada and 2 in Switzerland and an estimated 24 sites yet to open in Germany, France, Switzerland, Italy and Austria. In addition to the continued expansion in Europe, effort is undergoing to extend the trial to China and Australia as well. Clinical trial information: NCT03970447

Adaptive interventions to optimise the mobile phone-based smoking cessation support: study protocol for a sequential, multiple assignment, randomised trial (SMART)

BackgroundMobile health (mHealth) is promising in developing personalised smoking cessation interventions. By using an adaptive trial design, we aim to evaluate the effectiveness of personalised mHealth intervention in increasing smoking cessation.MethodsThis study is a two-arm, parallel, accessor-blinded Sequential Multiple-Assignment Randomised Trial (SMART) that randomises 1200 daily cigarette smokers from 70 community sites at two timepoints. In the first phase, participants receive brief cessation advice plus referral assistance to smoking cessation services and are randomly allocated to receive personalised instant messaging (PIM) or regular instant messaging (RIM). In the second phase, PIM participants who are non-responders (i.e. still smoking at 1 month) are randomised to receive either optional combined interventions (multi-media messages, nicotine replacement therapy sampling, financial incentive for active referral, phone counselling, and family/peer support group chat) or continued-PIM. Non-responders in the RIM group are randomised to receive PIM or continued-RIM. Participants who self-report quitting smoking for 7 days or longer at 1 month (responders) in both groups continue to receive the intervention assigned in phase 1. The primary outcomes are biochemical abstinence validated by exhaled carbon monoxide (< 4 ppm) and salivary cotinine (< 10 ng/ml) at 3 and 6 months from treatment initiation. Intention-to-treat analysis will be adopted.DiscussionThis is the first study using a SMART design to evaluate the effect of adaptive mHealth intervention on abstinence in community-recruited daily smokers. If found effective, the proposed intervention will inform the development of adaptive smoking cessation treatment and benefits smokers non-responding to low-intensity mHealth support.Trial registrationClinicalTrials.govNCT03992742. Registered on 20 June 2019.

Protocol for an automated, pragmatic, embedded, adaptive randomised controlled trial: behavioural economics-informed mobile phone-based reminder messages to improve clinic attendance in a Botswanan school-based vision screening programme

BackgroundClinic non-attendance rates are high across the African continent. Emerging evidence suggests that phone-based reminder messages could make a small but important contribution to reducing non-attendance. We will use behavioural economics principles to develop an SMS and voice reminder message to improve attendance rates in a school-based eye screening programme in Botswana.MethodsWe will test a new theory-informed SMS and voice reminder message in a national school-based eye screening programme in Botswana. The control will be the standard SMS message used to remind parents/guardians to bring their child for ophthalmic assessment. All messages will be sent twice. The primary outcome is attendance for ophthalmic assessment. We will use an automated adaptive approach, starting with a 1:1 allocation ratio.DiscussionAs far as we are aware, only one other study has used behavioural economics to inform the development of reminder messages to be deployed in an African healthcare setting. Our study will use an adaptive trial design, embedded in a national screening programme. Our approach can be used to trial other forms of reminder message in the future.Trial registrationISRCTN https://doi.org/10.1186/ISRCTN96528723. Registered on 5 January 2022.