Adaptive designs in dermatology clinical trials: Current status and future perspectives.

BackgroundAlthough children have historically been excluded from clinical trials (CTs), many require medicines tested and approved in CTs, forcing health care providers to treat their pediatric patients based on extrapolated data. Unfortunately, traditional randomized CTs can be slow and resource-intensive, and they often require multi-center collaboration. However, an adaptive design (AD) framework for CTs could be used to increase the efficiency of pediatric CTs by incorporating prospectively planned modifications to CT methods without undermining the integrity or validity of the study. There are many possible adaptations, but each will have ethical, logistical, and statistical implications. It remains unclear which adaptations (or combinations thereof) will lead to real-world improvements in pediatric CT efficiency. This study will identify, evaluate, and synthesize the various regulatory, ethical, logistical, and statistical considerations and emerging issues of AD in CTs that could be used to evaluate the use of drugs in children.Methods/designFollowing the development of a peer-reviewed search strategy, a systematic review on AD in CTs will be conducted. Data on regulatory, ethical, logistic, and statistical considerations as well as population and trial design characteristics will be synthesized. A mixed-methods study including surveys and focus groups with regulators, research ethics board members, biostatisticians, clinicians, and scientists, as well as representatives from patient groups and the public will evaluate the opportunities and challenges in applying AD in trials enrolling children and propose recommendations on best practices.DiscussionThis study will deliver practical recommendations on the use of AD in pediatric CTs. Collaboration and consultation with national and global partners will ensure that our results meet the needs of researchers, regulators, and patients, both locally and globally, and that they remain current and relevant by engaging a wide variety of stakeholders. Overall, this research will enrich the knowledge base regarding if, how, and when AD can be used to answer research questions with fewer resources while still meeting the highest ethical standards and regulatory requirements for CTs. In turn, this will result in increased high-quality clinical research needed by health care providers so they have access to appropriate, population-specific evidence regarding the safe and effective use of medicines in children.

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.

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.

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.

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.

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.

Biotechnology Law ReportVol. 29, No. 2 CLINICAL TRIALSFDA Issues Guidance on Adaptive Clinical Trial DesignPublished Online:16 Jun 2010https://doi.org/10.1089/blr.2010.9967AboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail "FDA Issues Guidance on Adaptive Clinical Trial Design." , 29(2), p. 173FiguresReferencesRelatedDetailsCited byInnovative Designs, Methods and Application for Disease Risk Prediction and Drug Development: Frequentist, Bayesian, and BeyondContemporary Clinical Trials, Vol. 63A literature review of applied adaptive design methodology within the field of oncology in randomised controlled trials and a proposed extension to the CONSORT guidelines18 July 2017 | BMC Medical Research Methodology, Vol. 17, No. 1Adaptive designs undertaken in clinical research: a review of registered clinical trials19 March 2016 | Trials, Vol. 17, No. 1 Volume 29Issue 2Apr 2010 Information© 2010 Mary Ann Liebert, Inc.To cite this article:FDA Issues Guidance on Adaptive Clinical Trial Design.Biotechnology Law Report.Apr 2010.173-173.http://doi.org/10.1089/blr.2010.9967Published in Volume: 29 Issue 2: June 16, 2010PDF download

Chapter 19 - Adaptive trial design

Current methods for the design and analysis of phase III clinical trials often results in the approval and use of drugs in broad populations of patients, many of whom do not benefit. This has serious limitations for patients and for health care economics. Current methods are particularly problematic for the development of molecularly targeted drugs which are expected to benefit only a subset of traditionally diagnosed patients. New paradigms for the design and analysis of clinical trials are needed. We will describe recent developments in the prospective use of predictive biomarkers in the design and analysis of phase III clinical trials. The presentation will not be about exploratory analysis of data from clinical trials, but rather on the use of the use of genomic biomarkers in the design and analysis in a sufficiently structured and prospective manner that the conclusions about treatment effects and how they relate to biomarker specified subsets have the degree of reliability normally associated with phase III clinical trials. We will cover the targeted “enrichment” design in which a classifier test result is used as an eligibility criterion. The efficiency of that design and how it depends on the specificity of treatment effect and test performance characteristics will be discussed as well as limitations of that design. We will discuss “stratified designs” in which the test is not used to restrict eligibility but as part of the primary analysis plan of the trial. Specific analysis plans and sample size considerations will be discussed. Both the enrichment design and stratification design require that the classifier be completely specified and analytically validated prior to the start of the pivotal trial. We will discuss more flexible prospective designs for evaluating multiple candidate predictive biomarkers and the recently published cross-validated adaptive signature design which is extremely powerful for combining in one clinical trial the development and internal validation of a predictive classifier utilizing any type of candidate variables. The prospective-retrospective approach using archived tissues will also be described. We will present a new framework for the analysis of clinical trials that incorporates both hypothesis testing and predictive modeling. This framework provides for complementary roles of frequentist and Bayesian methods but requires that models be justified based on predictive accuracy. PDF reprints of the following relevant publications are available at http://brb.nci.nih.gov Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. 2005;23:7332-41. Simon R, Maitnourim A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clinical Cancer Res. 2005;10:6759-63; suppl and correction, 2006;12:3229. Simon R. Using genomics in clinical trial design. Clinical Cancer Res. 2008;14:5984-93. Freidlin B, Simon R. Adaptive signature design: An adaptive clinical trial design for generating and prospectively testing a gene expression signature for sensitive patients. Clinical Cancer Res. 2005;11:7872-8. Jiang W, Freidlin B, Simon R. Biomarker adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect. J Natl Cancer Inst. 2007;99:1036-43. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Nat Cancer Inst. 2009;101:1446-52. Freidlin B, Jiang W, Simon R. The cross-validated adaptive signature design. Clinical Cancer Res. 2010;16(2):691-8. Citation Information: Clin Cancer Res 2010;16(14 Suppl):IA4-2.

Adaptive trial designs that allow enrichment of the study population through subgroup selection can increase the chance of a positive trial when there is a differential treatment effect among patient subgroups. The goal of this study is to illustrate the potential benefit of adaptive subgroup selection in endovascular stroke studies. We simulated the performance of a trial design with adaptive subgroup selection and compared it with that of a traditional design. Outcome data were based on 90-day modified Rankin Scale scores, observed in IMS III (Interventional Management of Stroke III), among patients with a vessel occlusion on baseline computed tomographic angiography (n=382). Patients were categorized based on 2 methods: (1) according to location of the arterial occlusive lesion and onset-to-randomization time and (2) according to onset-to-randomization time alone. The power to demonstrate a treatment benefit was based on 10 000 trial simulations for each design. The treatment effect was relatively homogeneous across categories when patients were categorized based on arterial occlusive lesion and time. Consequently, the adaptive design had similar power (47%) compared with the fixed trial design (45%). There was a differential treatment effect when patients were categorized based on time alone, resulting in greater power with the adaptive design (82%) than with the fixed design (57%). These simulations, based on real-world patient data, indicate that adaptive subgroup selection has merit in endovascular stroke trials as it substantially increases power when the treatment effect differs among subgroups in a predicted pattern.

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

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.

In the November 2014 issue of the TIRS journal Stephen Spielberg [1] wrote an editorial motivating the future direction of the journal. He remarked: ‘We are making progress in drug development, with faster, smarter and more effective ways of evaluating the efficacy and safety of new products, all of which is beginning to transform into better regulatory practices with alignment in high-quality, validated science.’ Also, ‘We need to share together what works and what does not, all in the interest of patients in need and for all who strive to develop important new products.’ The aim of this piece is to begin a discussion to focus on sharing what works and what we could be doing to work more effectively when the objective is to consider the implementation of the Novel Adaptive Design (NAD) clinical trial as a pivotal Phase III trial in a regulatory setting. The NAD is continuing to gain favor as an alternative strategy for implementation of a pivotal trial in a regulatory setting. By ‘novel,’ we mean those adaptive designs that provide for an unblinded interim analysis in order to implement a predefined adaptive design decision. Since the issuance of the draft FDA adaptive design guidance [2] in 2010 we have come to understand that methods that provide for an unblinded interim analysis to estimate the treatment effect distinguishes the less-well understood Novel Adaptive Design (NAD) from the well-understood Adaptive Design [2,3] (AD). To clarify further, ‘less-well understood’ designs tend to entail using the unblinded treatment effect to potentially modify some aspect of the design while a trial continues. We can distinguish this from a conventional Group Sequential Design (GSD), which is characterized as ‘well understood,’ where the actions are built into the study design and typically only involve whether the study terminates or continues unchanged. NAD is being advocated in R&D programs as tools for optimizing clinical trial portfolio management [4]. The potential for increasing the probability of success, accelerated timing for product approval and reduced trial costs represent the optimistic advocacy on behalf of such designs. Kenneth Getz writes: ‘Adaptive trial designs hold promise in optimizing study design. Early study terminations due to futility and sample size reestimation could save up to a hundred million dollars (USD) in direct and indirect costs annually per pharmaceutical company depending on clinical trial scope, when the trial is actually terminated, and on the sponsor’s overall implementation of this adaptive approach across the development portfolio [5].’ If NAD for pivotal clinical trials hold the promise that is being advertised why are we not seeing more of them being introduced as the implementation method of choice?

This chapter discusses the property and principles of adaptive trial designs. Adaptive trial designs refer to trial designs that offer pre-planned opportunities to modify the design of an ongoing trial based on accumulating trial data. Decisions for potential adaptations are made during the trial based on interim data, but flexibilities in adaptive trial designs are established and outlined in the study documents before any patient is recruited. For statistical planning, a simulation-guided approach is often used to evaluate the statistical properties of the design. It is generally required to demonstrate control of false positive rates for the regulatory, ethics, and funding bodies. Measures to mitigate and plan for operational bias and complexity in adaptive trial designs are needed.

Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?