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Despite the concerns of time trend in subject profiles, the use of Bayesian response adaptive randomization (BRAR) in large multicenter phase 3 confirmative trials has been reported in recent years, motivated by the potential benefits in subject ethics and/or trial efficiency. However three issues remain unclear to investigators: 1) among several BRAR algorithms, how to choose one for the specific trial setting; 2) when to start and how frequently to update the allocation ratio; and 3) how to choose the interim analyses stopping boundaries to preserve the type 1 error. In this paper, three commonly used BRAR algorithms are evaluated based on type 1 error, power, sample size, the proportion of subjects assigned to the better performing arm, and the total number of failures, under two specific trial settings and different allocation ratio update timing and frequencies. Simulation studies show that for two-arm superiority trials, none of the three BRAR algorithms has predominant benefits in both patient ethics and trial efficiency when compared to fixed equal allocation design. For a specific trial aiming to identify the best or the worst among three treatments, a properly selected BRAR algorithm and its implementation parameters are able to gain ethical and efficiency benefits simultaneously. Although the simulation results come from a specific trial setting, the methods described in this paper are generally applicable to other trials. [ABSTRACT FROM AUTHOR]

[Display omitted] • Contemporary design and modular construction approach for underground stations. • BIM-based multi-LoD parametric modelling for the prefabricated metro station. • Explicit parametric relationships and constraints for adaptive designs. • Upfront carbon (kgCO 2 e) or the minimum life cycle scope of embodied carbon. • Integrating embodied carbon assessment and prefabricated construction feasibility. With underground increasingly used for accommodating infrastructure and transport networks, construction sustainability throughout the life cycle of projects attracts growing attention. Specifically, to achieve decarbonisation goals, there are two types of strategy in the early design and construction stages: optimising material usage and designing with low-carbon materials to build clever, and utilising low-carbon construction technologies and minimising waste to build efficiency. Integrated solutions that consider environmental impact and construction feasibility become increasingly important. This paper incorporates building information modelling (BIM) to evaluate Greenhouse Gas (GHG) emissions and construction practicality while enabling design adaptation. A solution for adaptive designs of underground stations with variable geometries is achieved, which collectively explores carbon accounting and construction viability. Under the emerging contemporary design and modular construction approach, a multiple level-of-details (multi-LoD) prefabricated station is developed to verify the proposed framework. We focus on calculating embodied carbon of the prefabricated station at the product and construction stages according to EN 15978:2011. Then, automatic quantity take-off of all elements presented in the model is achieved by enabling data/parameter assignments and processing in BIM via visual programming. Five parametric adaptions with a fixed width are made to compare the contribution proportion of components to carbon emission. The difference in carbon footprint contributed by changing the steel reinforcement ratio is the highest. Two use cases were further evaluated for construction feasibility and investigation of the beneficial potential of the integrated solution for large-scale metro network expansion. The first case verifies the interoperability between the multi-LoD BIM model and numerical modelling for a station at the construction stage with known ground conditions. The second case demonstrates the on-site BIM-based construction simulation with selected carbon solution for a station on a planned metro line. This paper explores the implementation of information modelling and enhances interoperability for the planning and construction of sustainable underground infrastructure. [ABSTRACT FROM AUTHOR]

This paper mainly deals with the problem of exponential and adaptive synchronization for a type of inertial complex-valued neural networks via directly constructing Lyapunov functionals without utilizing standard reduced-order transformation for inertial neural systems and common separation approach for complex-valued systems. At first, a complex-valued feedback control scheme is designed and a nontrivial Lyapunov functional, composed of the complex-valued state variables and their derivatives, is proposed to analyze exponential synchronization. Some criteria involving multi-parameters are derived and a feasible method is provided to determine these parameters so as to clearly show how to choose control gains in practice. In addition, an adaptive control strategy in complex domain is developed to adjust control gains and asymptotic synchronization is ensured by applying the method of undeterminated coefficients in the construction of Lyapunov functional and utilizing Barbalat Lemma. Lastly, a numerical example along with simulation results is provided to support the theoretical work. [ABSTRACT FROM AUTHOR]

Sample size plays a crucial role in clinical trials. Flexible sample-size designs, as part of the more general category of adaptive designs that utilize interim data, have been a popular topic in recent years. In this paper, we give a comparative review of four related methods for such a design. The likelihood method uses the likelihood ratio test with an adjusted critical value. The weighted method adjusts the test statistic with given weights rather than the critical value. The dual test method requires both the likelihood ratio statistic and the weighted statistic to be greater than the unadjusted critical value. The promising zone approach uses the likelihood ratio statistic with the unadjusted value and other constraints. All four methods preserve the type-I error rate. In this paper we explore their properties and compare their relationships and merits. We show that the sample size rules for the dual test are in conflict with the rules of the promising zone approach. We delineate what is necessary to specify in the study protocol to ensure the validity of the statistical procedure and what can be kept implicit in the protocol so that more flexibility can be attained for confirmatory phase III trials in meeting regulatory requirements. We also prove that under mild conditions, the likelihood ratio test still preserves the type-I error rate when the actual sample size is larger than the re-calculated one. [ABSTRACT FROM AUTHOR]

In drug development, a two-stage winner design (Lan et al. 2005, Shun et al. 2008) can be cost-effective when the best treatment is to be determined from multiple experimental treatments in superiority trials. However, the statistical methods assessing non-inferiority in a two-stage winner design have not yet been studied, for which the complexity arises in determining the critical value when parameter space is not a single point under the null hypothesis. Because the maximum error may not occur at the vertex of the null space, it is unclear if naive use of critical values and distributional results from the test for superiority remains correct. In this paper, we provided rigorous justifications to determine the critical value for testing non-inferiority hypothesis, with a pre-specified non-inferiority margin, in a two-stage winner design with two experimental treatments and an active control. We studied the distribution of the test statistics, critical values, sample size and power calculations using the exact distribution of the test statistics as well as using normal approximations. Theoretical justifications and extensive numerical assessments were conducted to calculate the design parameters and evaluate the performance of our methods. [ABSTRACT FROM AUTHOR]

In this paper, we present the dfcomb R package for the implementation of a single prospective clinical trial or simulation studies of phase I combination trials in oncology. The aim is to present the features of the package and to illustrate how to use it in practice though different examples. The use of combination clinical trials is growing, but the implementation of existing model-based methods is complex, so this package should promote the use of innovative adaptive designs for early phases combination trials. [ABSTRACT FROM AUTHOR]

Time to event is the clinically definitive endpoint in Phase III trials of new treatments of cancer, cardiovascular and many other diseases. Because these trials involve relatively long follow-up, their protocols usually incorporate periodic interim analyses of the data by a Data and Safety Monitoring Board/Committee. This paper gives a review of the major developments in the design of these trials in the 21st century, spurred by the need for better clinical trial designs to cope with the remarkable advances in cancer biology, genomics and imaging that can help predict patients' sensitivity or resistance to certain treatments. In addition to this overview and discussion of related issues and challenges, we also introduce a new approach to address some of these issues. [ABSTRACT FROM AUTHOR]

Background: The field of telehealth and telemedicine is expanding as the need to improve efficiency of health care becomes more pressing. The decision to implement a telehealth system is generally an expensive undertaking that impacts a large number of patients and other stakeholders. It is therefore extremely important that the decision is fully supported by accurate evaluation of telehealth interventions.Objective: Numerous reviews of telehealth have described the evidence base as inconsistent. In response they call for larger, more rigorously controlled trials, and trials which go beyond evaluation of clinical effectiveness alone. The aim of this paper is to discuss various ways in which evaluation of telehealth could be improved by the use of adaptive trial designs.Results: We discuss various adaptive design options, such as sample size reviews and changing the study hypothesis to address uncertain parameters, group sequential trials and multi-arm multi-stage trials to improve efficiency, and enrichment designs to maximise the chances of obtaining clear evidence about the telehealth intervention.Conclusion: There is potential to address the flaws discussed in the telehealth literature through the adoption of adaptive approaches to trial design. Such designs could lead to improvements in efficiency, allow the evaluation of multiple telehealth interventions in a cost-effective way, or accurately assess a range of endpoints that are important in the overall success of a telehealth programme. [ABSTRACT FROM AUTHOR]

Background The field of telehealth and telemedicine is expanding as the need to improve efficiency of health care becomes more pressing. The decision to implement a telehealth system is generally an expensive undertaking that impacts a large number of patients and other stakeholders. It is therefore extremely important that the decision is fully supported by accurate evaluation of telehealth interventions. Objective Numerous reviews of telehealth have described the evidence base as inconsistent. In response they call for larger, more rigorously controlled trials, and trials which go beyond evaluation of clinical effectiveness alone. The aim of this paper is to discuss various ways in which evaluation of telehealth could be improved by the use of adaptive trial designs. Results We discuss various adaptive design options, such as sample size reviews and changing the study hypothesis to address uncertain parameters, group sequential trials and multi-arm multi-stage trials to improve efficiency, and enrichment designs to maximise the chances of obtaining clear evidence about the telehealth intervention. Conclusion There is potential to address the flaws discussed in the telehealth literature through the adoption of adaptive approaches to trial design. Such designs could lead to improvements in efficiency, allow the evaluation of multiple telehealth interventions in a cost-effective way, or accurately assess a range of endpoints that are important in the overall success of a telehealth programme. [ABSTRACT FROM AUTHOR]

In the past decades, the world has experienced several major virus outbreaks, e.g. West African Ebola outbreak, Zika virus in South America and most recently global coronavirus (COVID-19) pandemic. Many vaccines have been developed to prevent a variety of infectious diseases successfully. However, several infections have not been preventable so far, like COVID-19, which induces an immediate urgent need for effective vaccines. These emerging infectious diseases often pose unprecedent challenges for the global heath community as well as the conventional vaccine development paradigm. With a long and costly traditional vaccine development process, there are extensive needs in innovative vaccine trial designs and analyses, which aim to design more efficient vaccines trials. Featured with reduced development timeline, less resource consuming or improved estimate for the endpoints of interests, these more efficient trials bring effective medicine to target population in a faster and less costly way. In this paper, we will review a few vaccine trials equipped with adaptive design features, Bayesian designs that accommodate historical data borrowing, the master protocol strategy emerging during COVID-19 vaccine development, Real-World-Data (RWD) embedded trials and the correlate of protection framework and relevant research works. We will also discuss some statistical methodologies that improve the vaccine efficacy, safety and immunogenicity analyses. Innovative clinical trial designs and analyses, together with advanced research technologies and deeper understanding of the human immune system, are paving the way for the efficient development of new vaccines in the future. [ABSTRACT FROM AUTHOR]

The rapidly changing drug development landscapes have brought unique challenges to sponsors in designing clinical trials in a faster and more efficient way. With the ability to accelerate development timeline, reduce redundant sample size, and select the right dose and patient population during the clinical trial, adaptive designs help to increase the probability of success of clinical trials and eventually contribute to bringing the promising drugs to patients earlier and fulfilling their unmet medical needs. Although extensive adaptive design methods have been proposed in recent years, a comprehensive review of how to implement adaptive design in the practical confirmatory trials is still lacking. In this paper, we will review the evolving history of adaptive designs, updates of newly released regulatory guidance and emerging practical adaptive designs, including but not limited to sample size re-estimation, seamless design and surrogate endpoint used in the interim analysis. Furthermore, we will discuss the current practice of adaptive design implementation by demonstrating a complex oncology seamless phase 2/3 adaptive design case study. Through this example, we will introduce the critical roles of each cross disciplinary function, communication process and important documents when adaptive designs are implemented in real-world setting. [ABSTRACT FROM AUTHOR]

Adaptive group-sequential trials incorporating mid-term design modifications, such as selection of hypotheses or treatment groups, or sample size reestimation during the interim analysis, provide efficient tools to utilize the data from enrolled patients. A graphical multiple test procedure using weighted Bonferroni test has been proposed in adaptive group-sequential designs based on a marginal p -value combination approach. Multiple test procedures using weighted Bonferroni test may result in potential power loss when the test statistics are positively correlated. This paper proposes a graphical approach for adaptive group-sequential trials using weighted parametric test which utilizes the correlation structure between test statistics. The proposed parametric approach is more powerful than the Bonferroni-based approach and preserves the familywise Type I error. The proposed approach is appealing in adaptive designs with the option of treatment selection during the interim analysis and seamless combination of the dose finding and confirmatory stage, because it can utilize the natural correlations between the test statistics resulting from comparing a few treatment groups to a common control group. Simulations are conducted to evaluate the power and Type I error. The proposed parametric approach is illustrated with a confirmatory two-stage adaptive design with a dose selection stage and confirmatory stage with the selected treatment groups. [ABSTRACT FROM AUTHOR]

Once scientific questions are determined, other design features of clinical trials including increasing the power while controlling the type I error rate, planning interim analysis, and achieving treatment balance among subgroups will be either required or preferred. We propose to sequentially monitor the covariate adaptive randomization (CAR) procedures with sample size re-estimation (SSR) to satisfy a variety of design objectives of clinical trials. However, each of the three adaptive designs (sequential monitoring, CAR, and SSR) poses a challenge to the control of the type I error rate. In this paper, we investigated how to utilize the advantages of the three adaptive methods and control the type I error rate. We proved that the asymptotic joint distribution of the sequential statistics follows the asymptotic canonical joint distribution defined in Jennison and Turnbull [14]. Besides, numerical studies demonstrated that our methods could control the type I error rate, increase the power, and lead to much-improved treatment balance across subgroups. [ABSTRACT FROM AUTHOR]