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

TPS2078 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. 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. 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 new 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. GBM AGILE has screened over 1000 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. While enrollment had an initial dip during the early stages of the pandemic (April-May, 2020), with flexible processes including remote based monitoring, minimizing in person visits, and remote provision of IMP, the enrollment rebounded by June, 2020. Through the use of improved and efficient processes allowed within a master protocol/adaptive platform trial infrastructure, GBM AGILE has been seamlessly operating a global trial during a global pandemic. Clinical trial information: NCT03970447.

This article provides a regulatory view of the design of clinical trials using Bayesian statistics and adaptive methods. The foci are the similarities and differences between Bayesian and adaptive designs for clinical studies for medical devices and the contrast with trials of pharmaceutical drugs. The critical role of the U.S. Food and Drug Administration in such designs is highlighted. The previous device experience with Bayesian trials has provided some important insight into the planning of adaptive trials. This article discusses both frequentist and Bayesian adaptive designs and presents several real examples of designs for medical device studies. The crucial role that simulations play in assessing the operating characteristics of the designs is emphasized. The unique advantage of predictive modeling in the Bayesian adaptive design is explored. For adaptive designs there are challenging problems including operational bias and logistical issues. The reporting of both Bayesian and adaptive trials is discussed.

AimsThis study aimed to identify the barriers and facilitators to implementing adaptive trial designs in paediatric intensive care unit (PICU) trials.MethodsA mixed-methods approach was used, combining an electronic survey and semi-structured interviews. The survey targeted clinicians and researchers involved in PICU trials, collecting information on their knowledge, and experience with adaptive trial designs. Semi-structured interviews were conducted with experienced international PICU trialists to explore themes related to adaptive trial design development and implementation.ResultsEighty-six survey responses were collected from 21 countries, with 62 responses from 20 countries eligible for analysis. Of these, 45% (n = 28) of respondents had experience with adaptive trials, particularly in platform trials (n = 9, 32%) and adaptive randomisation (n = 8, 29%). Barriers to implementation included statistical complexities, infrastructure requirements, and a lack of expertise. Facilitators included increasing understanding of adaptive methodologies and access to statistical expertise. Ten interviews were conducted. Interview themes highlighted optimism about adaptive designs, a lack of formal training, the need for collaboration, the role of high-profile trials, and the potential risks involved.ConclusionsAdaptive trial designs in PICU trials hold promise but are underutilised due to substantial barriers, particularly in statistical and infrastructural areas. Addressing these challenges, promoting formal training, enhancing collaboration, and raising awareness of adaptive trial methodologies are critical to increasing their adoption in paediatric critical care.

With the advancement in biomedicine, many biologically targeted therapies have been developed. These targeted agents, however, may not work for everyone. Biomarker profiles can be used to identify effective targeted therapies. Our goals are to characterize the molecular signature of individual tumors, offer the best-fit targeted therapies to patients in a study, and identify promising agents for future development. We propose an outcome-based adaptive randomization trial design for patients with advanced stage non-small cell lung cancer. All patients have baseline biopsy samples taken for biomarker assessment prior to randomization to treatments. The primary endpoint of this study is the disease control rate at 8 weeks after randomization. The Bayesian probit model is used to characterize the disease control rate. Patients are adaptively randomized to one of four treatments with the randomization rate based on the updated disease control rate from the accumulated data in the trial. For each biomarker profile, high-performing treatments have higher randomization rates, and vice versa. An early stopping rule is implemented to suspend low-performing treatments from randomization. Based on extensive simulation studies, with a total of 200 evaluable patients, our trial has desirable operating characteristics to: (1) identify effective agents with a high probability; (2) suspend ineffective agents; and (3) treat more patients with effective agents that correspond to their biomarker profiles. Our trial design continues to update and refine the estimates as the trial progresses. This biomarker-based trial requires biopsible tumors and a two-week turn around time for biomarker profiling before randomization. Additionally, in order to learn from the interim data and adjust the randomization rate accordingly, the outcome-based adaptive randomization design is applicable only for trials when the endpoint can be assessed in a relative short period of time. Bayesian adaptive randomization trial design is a smart, novel, and ethical design. In conjunction with an early stopping rule, it can be used to efficiently identify effective agents, eliminate ineffective ones, and match effective treatments with patients' biomarker profiles. The proposed design is suitable for the development of targeted therapies and provides a rational design for personalized medicine.

IntroductionAdaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies.MethodsWe describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O’Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials.ResultsThe case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O’Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study.ConclusionsCost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding.HighlightsOpportunities are potentially being missed to incorporate health economic considerations into the design of adaptive clinical trials.Existing expected value of sample information analysis methods can be extended to compare possible group sequential and nonadaptive trial designs when planning a clinical trial.We recommend that adjusted analyses be presented to control for the potential impact of the adaptive designs and to maintain the accuracy of the calculations.This approach can help to justify the choice of design characteristics and ensure the cost-effective use of limited research funding.

Quality improvement in health service programmes often involves introducing small, low-risk programmatic changes, such as modifying workflows, to incrementally improve outcomes, which accumulate over time to have significant overall gains in quality and efficiency. Although these changes are common in health services, they are rarely evaluated using statistically rigorous designs, partly because conventional randomised trials are perceived as inefficient for detecting modest effects. This study was motivated by a vision screening and referral programme and focuses on evaluating the modest but important impacts of low-risk QI interventions. To advance data-driven approaches for achieving quality improvement, we used a simulation study to explore the use of Bayesian adaptive trial designs to compare two variants of programmatic changes that yield small improvements in outcomes. The study examined key adaptive design features, including interim analysis frequency, prior specification, and early stopping rules for efficacy and equivalence. Changes in error rates, sample size, and bias were assessed across scenarios with small effect sizes ranging from 0% to 5%. The findings were used to configure an ideal trial design that prioritises rapid identification of the more effective programmatic change while minimizing the risk of adopting an inferior one. The recommended trial design incorporates a sceptical prior, a stringent stopping rule for efficacy, and a relaxed criteria to stop for equivalence. Under this design, a marginal improvement as small as 1% could be detected with high probability using considerably fewer participants than would be required under conventional, fixed-size randomised controlled trials. Bayesian adaptive trial designs offer a feasible approach for evaluating low-risk, incremental QI interventions in high-throughput service settings. Their use may support more efficient, data-driven decision-making when modest improvements are expected and the consequences of incorrect adoption are limited.

An adaptive clinical trial design for cocaine use disorder: Extended-release amphetamine salts for early behavioral intervention non-responders

Developing a vaccine against the human immunodeficiency virus (HIV) poses an exceptional challenge. There are no documented cases of immune-mediated clearance of HIV from an infected individual, and no known correlates of immune protection. Although nonhuman primate models of lentivirus infection have provided valuable data about HIV pathogenesis, such models do not predict HIV vaccine efficacy in humans. The combined lack of a predictive animal model and undefined biomarkers of immune protection against HIV necessitate that vaccines to this pathogen be tested directly in clinical trials. Adaptive clinical trial designs can accelerate vaccine development by rapidly screening out poor vaccines while extending the evaluation of efficacious ones, improving the characterization of promising vaccine candidates and the identification of correlates of immune protection.

Recent FDA guidance on adaptive clinical trial designs defines bias as “a systematic tendency for the estimate of treatment effect to deviate from its true value”, and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realised trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This paper is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this paper can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.

BackgroundBayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept.MethodsWe discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials.ResultsIn both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power.ConclusionsCurrently, regulators require demonstration of control of type I error for both frequentist and Bayesian adaptive designs, particularly for late-phase trials. To demonstrate control of type I error in Bayesian adaptive designs, adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error. If one instead uses a strict Bayesian approach, which is currently more accepted in the design and analysis of exploratory trials, then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of clinically-relevant values.

BackgroundBayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. The aim of this work was to determine how Bayesian adaptive designs can be constructed for phase III clinical trials in critical care, and to assess the influence that Bayesian designs would have on trial efficiency and study results.MethodsWe re-designed the High Frequency OSCillation in Acute Respiratory distress syndrome (OSCAR) trial using Bayesian adaptive design methods, to allow for the possibility of early stopping for success or futility. We constructed several alternative designs and studied their operating characteristics via simulation. We then performed virtual re-executions by applying the Bayesian adaptive designs using the OSCAR data to demonstrate the practical applicability of the designs.ResultsWe constructed five alternative Bayesian adaptive designs and identified a preferred design based on the simulated operating characteristics, which had similar power to the original design but recruited fewer patients on average. The virtual re-executions showed the Bayesian sequential approach and original OSCAR trial yielded similar trial conclusions. However, using a Bayesian sequential design could have led to a reduced sample size and earlier completion of the trial.ConclusionsUsing the OSCAR trial as an example, this case study found that Bayesian adaptive designs can be constructed for phase III critical care trials. If the OSCAR trial had been run using one of the proposed Bayesian adaptive designs, it would have terminated at a smaller sample size with fewer deaths in the trial, whilst reaching the same conclusions. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.Trial registrationOSCAR Trial registration ISRCTN, ISRCTN10416500. Retrospectively registered 13 June 2007.

Current drug development strategies present many challenges that can impede drug approval by regulatory agencies. Alternative study models, such as adaptive trial designs, have recently sparked interest, as they provide a flexible and more efficient approach in conducting clinical trials. Adaptive trial designs offer several potential benefits over traditional randomized controlled trials, which include decrease in costs, reduced clinical development time and limiting exposure of patients to potentially ineffective treatments allowing completion of studies with fewer patients. This article explores the current use of adaptive trial designs in non-oncologic skin diseases and highlights the most common types of adaptive designs used in the field. We also review the operational challenges and statistical considerations associated with such designs and propose clinical development strategies to successfully implement adaptive designs. The article also proposes instances where adaptive trial designs are particularly beneficial, and other situations where they may not be very useful.

Background In the UK, over 100,000 people have a stroke annually. Over 1.3 million live with the effects of stroke, including problems with mobility, communication, cognition, anxiety, depression and fatigue. Previous research has tested single interventions to improve stroke outcomes in separate, fixed design, parallel-group trials. Evidence generation has been slow and inefficient. Adaptive trial designs are required, to better understand multiple treatments, targeting multiple questions simultaneously. We undertook to develop the first efficient adaptive platform trial protocol, aligned with national research priorities for ‘Life After Stroke’. Methods We embedded PPI activities throughout the platform development and co-developed resources to maximise equality, diversity, and inclusion. We established an inclusive multidisciplinary collaboration to inform design choices and future UK-wide platform delivery. We scrutinised existing research to identify candidate interventions and relevant outcomes; agreeing these with collaborators and PPI. We undertook detailed simulations to inform choice of platform design (adaptive elements, allocation, numbers of interventions, decision criteria for dropping/adding arms, interim analyses timing/frequency; intermediate outcomes choice). We explored barriers to trial and intervention participation through in-person and virtual meetings. To facilitate rapid trial set-up, we engaged with stroke research leaders and data providers; reviewed platform randomisation requirements; and widely disseminated our learning. Results We developed an efficient, adaptive trial protocol, which is feasible, inclusive and acceptable to stroke survivors and services and submitted a funding application for a platform trial testing at least five pre-determined non-pharmacological interventions for post-stroke emotional difficulties, the top research priority for ‘Life After Stroke’. Conclusions The complex and inclusive platform trial design has only been possible through UK-wide multidisciplinary collaboration with stroke researchers, trialists, clinicians, methodologists, third sector, and patient and public contributors. Such a trial would be a step-change in trial design, reducing research waste and accelerating evidence generation to inform improved stroke service provision world-wide.

Vasopressors and the search for the optimal trial design

In this chpater, we review the standards and guidelines for adaptive trial designs and master protocols. Methodological rigour and transparent reporting are required in all clinical trials. Adaptive trial designs and master protocols are no exception. The standard reporting guidelines and risk-of-bias assessments for conventional trials can be applied to adaptive trial designs and master protocols. Adaptive trial designs have pre-planned adaptations and analysis plans that are specified in a formal way that outline how the data will be used to guide the design. Planning of adaptive clinical trials, basket trials, umbrella trials, and platform trials will require early engagement with the stakeholders and methodologists to think through potential hurdles and challenges of the statistical design and its implementation.