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Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma. [ABSTRACT FROM AUTHOR]

Adaptive designs, such as group sequential designs (and the ones with additional adaptive features) or adaptive platform trials, have been quintessential efficient design strategies in trials of unmet medical needs, especially for generating evidence from global regions. Such designs allow interim decision making and making adjustment to study design when necessary, meanwhile maintaining study integrity and operating characteristics. However, driven by the heightened competitive landscape and the desire to bring effective treatment to patients faster, innovation in the already functional designs is still germane to further propel drug development to a more efficient path. One way to achieve this is by leveraging external real-world data (RWD) in the adaptive designs to support interim or final decision making. In this paper, we propose a novel framework of incorporating external RWD in adaptive design to improve interim and/or final analysis decision making. Within this framework, researchers can prespecify the decision process and choose the timing and amount of borrowing while maintaining objectivity and controlling of type I error. Simulation studies in various scenarios are provided to describe power, type I error, and other performance metrics for interim/final decision making. A case study in non-small cell lung cancer is used for illustration on proposed design framework. [ABSTRACT FROM AUTHOR]

Combination therapy, a treatment modality that involves multiple treatment agents, has become imperative for improving treatment effectiveness and addressing resistance in the field of oncology. However, determining the most effective dose for these combinations, particularly when dealing with intricate drug interactions and diverse toxicity patterns, presents a substantial challenge. This paper introduces a novel Bayesian dose-finding design for combination therapies with information borrowing, named the DOD-Combo design. Leveraging historical single-agent trials and the meta-analytic-predictive (MAP) power prior, our approach utilizes a copula-type model to connect individual drug priors with joint toxicity probabilities in combination treatments. The MAP power prior allows the integration of information from multiple historical trials, constructing informative priors for each agent. Extensive simulations confirm our method’s superior performance compared to combination designs with no information borrowing. By adaptively incorporating historical data, our approach reduces sample sizes and enhances efficiency in selecting the maximum tolerated dose (MTD), effectively addressing the intricate challenges presented by combination trials. [ABSTRACT FROM AUTHOR]

The propensity score‐integrated composite likelihood (PSCL) method is one method that can be utilized to design and analyze an application when real‐world data (RWD) are leveraged to augment a prospectively designed clinical study. In the PSCL, strata are formed based on propensity scores (PS) such that similar subjects in terms of the baseline covariates from both the current study and RWD sources are placed in the same stratum, and then composite likelihood method is applied to down‐weight the information from the RWD. While PSCL was originally proposed for a fixed design, it can be extended to be applied under an adaptive design framework with the purpose to either potentially claim an early success or to re‐estimate the sample size. In this paper, a general strategy is proposed due to the feature of PSCL. For the possibility of claiming early success, Fisher's combination test is utilized. When the purpose is to re‐estimate the sample size, the proposed procedure is based on the test proposed by Cui, Hung, and Wang. The implementation of these two procedures is demonstrated via an example. [ABSTRACT FROM AUTHOR]

Adaptive survey designs are increasingly used by survey practitioners to counteract ongoing declines in household survey response rates and manage rising fieldwork costs. This paper reports findings from an evaluation of an early-bird incentive (EBI) experiment targeting high-effort respondents who participate in the 2019 wave of the US Panel Study of Income Dynamics. We identified a subgroup of high-effort respondents at risk of nonresponse based on their prior wave fieldwork effort and randomized them to a treatment offering an extra time-delimited monetary incentive for completing their interview within the first month of data collection (treatment group; N  = 800) or the standard study incentive (control group; N  = 400). In recent waves, we have found that the costs of the protracted fieldwork needed to complete interviews with high-effort cases in the form of interviewer contact attempts plus an increased incentive near the close of data collection are extremely high. By incentivizing early participation and reducing the number of interviewer contact attempts and fieldwork days to complete the interview, our goal was to manage both nonresponse and survey costs. We found that the EBI treatment increased response rates and reduced fieldwork effort and costs compared to a control group. We review several key findings and limitations, discuss their implications, and identify the next steps for future research. [ABSTRACT FROM AUTHOR]

In theory, offering each sample member the mode sequence that maximizes their response propensity should increase the response rate and/or reduce the amount of nonresponse follow-up relative to a design that offers all sample members the same mode sequence. However, for this sort of tailoring to be feasible in a cross-sectional survey, it must be possible to use data available prior to data collection (e.g. on the sampling frame) to predict sample members' "mode-sensitivity"—the effect of the offered mode sequence on response propensity. Using data from randomized experiments incorporated into the 2016 and 2019 cycles of the National Household Education Survey, we evaluate whether data appended to an address-based sampling (ABS) frame can accurately predict the sensitivity of household-level response behavior to the initial offer of a paper questionnaire instead of a web instrument and whether a modeled-mode design that tailors the offered mode sequence (web-push vs. paper-only) based on the resulting predictions improves household-level data collection outcomes relative to a uniform web-push design. We find that several characteristics available on the ABS frame show statistically significant interactions with the offered mode sequence in determining the probability of response to initial survey mailings. Consequently, relative to a uniform web-push design, the modeled-mode design increased the response rate to early mailings and reduced the number of mailings required per response. However, the modeled-mode design did not meaningfully increase the final response rate, nor did it lead to a substantial reduction in indicators of nonresponse bias. [ABSTRACT FROM AUTHOR]

Conditional power (CP) is a commonly used tool to inform interim decision‐making in clinical trials, but the conventional approach using only primary endpoint data to calculate CP may not perform well when the primary endpoint requires a long follow‐up period, or the treatment effect size changes during the trial. Several methods have been proposed to use additional short term auxiliary data observed at the interim analysis to improve the CP estimation in these situations, however, they may rely on strong assumptions, have limited applications, or use ad hoc choices of information fraction. In this paper we propose a general framework where the true CP formula is first derived in the presence of auxiliary data, and CP estimation is obtained by substituting the unknown parameters with consistent estimators. We conducted extensive simulations to examine the performance of both proposed and conventional approaches using the true CP as the benchmark. As the proposed approach is based on the true underlying CP, the simulations confirmed its superiority over the conventional approach in terms of efficiency and accuracy, especially if observed auxiliary data reflect the change of treatment effect size. The simulations also indicate that the magnitude of improvement in CP estimation is associated with the correlation between auxiliary and primary endpoints and/or the magnitude of the effect size change during the trial. [ABSTRACT FROM AUTHOR]

Traditional vaccine efficacy trials usually use fixed designs with fairly large sample sizes. Recruiting a large number of subjects requires longer time and higher costs. Furthermore, vaccine developers are more than ever facing the need to accelerate vaccine development to fulfill the public's medical needs. A possible approach to accelerate development is to use the method of dynamic borrowing of historical controls in clinical trials. In this paper, we evaluate the feasibility and the performance of this approach in vaccine development by retrospectively analyzing two real vaccine studies: a relatively small immunological trial (typical early phase study) and a large vaccine efficacy trial (typical Phase 3 study) assessing prophylactic human papillomavirus vaccine. Results are promising, particularly for early development immunological studies, where the adaptive design is feasible, and control of type I error is less relevant. [ABSTRACT FROM AUTHOR]

In modern oncology drug development, adaptive designs have been proposed to identify the recommended phase 2 dose. The conventional dose finding designs focus on the identification of maximum tolerated dose (MTD). However, designs ignoring efficacy could put patients under risk by pushing to the MTD. Especially in immuno‐oncology and cell therapy, the complex dose‐toxicity and dose‐efficacy relationships make such MTD driven designs more questionable. Additionally, it is not uncommon to have data available from other studies that target on similar mechanism of action and patient population. Due to the high variability from phase I trial, it is beneficial to borrow historical study information into the design when available. This will help to increase the model efficiency and accuracy and provide dose specific recommendation rules to avoid toxic dose level and increase the chance of patient allocation at potential efficacious dose levels. In this paper, we propose iBOIN‐ET design that uses prior distribution extracted from historical studies to minimize the probability of decision error. The proposed design utilizes the concept of skeleton from both toxicity and efficacy data, coupled with prior effective sample size to control the amount of historical information to be incorporated. Extensive simulation studies across a variety of realistic settings are reported including a comparison of iBOIN‐ET design to other model based and assisted approaches. The proposed novel design demonstrates the superior performances in percentage of selecting the correct optimal dose (OD), average number of patients allocated to the correct OD, and overdosing control during dose escalation process. [ABSTRACT FROM AUTHOR]

Background: Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power.Results: The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package 'barts'.Conclusion: The proposed methods for trial design provide an attractive alternative to their frequentist counterparts. [ABSTRACT FROM AUTHOR]

Unblinded sample size re‐estimation (SSR) is often planned in a clinical trial when there is large uncertainty about the true treatment effect. For Proof‐of Concept (PoC) in a Phase II dose finding study, contrast test can be adopted to leverage information from all treatment groups. In this article, we propose two‐stage SSR designs using frequentist conditional power (CP) and Bayesian predictive power (PP) for both single and multiple contrast tests. The Bayesian SSR can be implemented under a wide range of prior settings to incorporate different prior knowledge. Taking the adaptivity into account, all type I errors of final analysis in this paper are rigorously protected. Simulation studies are carried out to demonstrate the advantages of unblinded SSR in multi‐arm trials. [ABSTRACT FROM AUTHOR]

Adaptive planning of clinical trials allows modifying the entire trial design at any time point mid‐course. In this paper, we consider the case when a trial‐external update of the planning assumptions during the ongoing trial makes an unforeseen design adaptation necessary. We take up the idea to construct adaptive designs with defined features by solving an optimization problem and apply it to the situation of unplanned design reassessment. By using the conditional error principle, we present an approach on how to optimally modify the trial design at an unplanned interim analysis while at the same time strictly protecting the type I error rate. This linking of optimal design planning and the conditional error principle allows sound reactions to unforeseen events that make a design reassessment necessary. [ABSTRACT FROM AUTHOR]

Background: Current dose-finding designs for phase I clinical trials can correctly select the MTD in a range of 30-80% depending on various conditions based on a sample of 30 subjects. However, there is still an unmet need for efficiency and cost saving.Methods: We propose a novel dose-finding design based on Bayesian stochastic approximation. The design features utilization of dose level information through local adaptive modelling and free assumption of toxicity probabilities and hyper-parameters. It allows a flexible target toxicity rate and varying cohort size. And we extend it to accommodate historical information via prior effective sample size. We compare the proposed design to some commonly used methods in terms of accuracy and safety by simulation.Results: On average, our design can improve the percentage of correct selection to about 60% when the MTD resides at a early or middle position in the search domain and perform comparably to other competitive methods otherwise. A free online software package is provided to facilitate the application, where a simple decision tree for the design can be pre-printed beforehand.Conclusion: The paper proposes a novel dose-finding design for phase I clinical trials. Applying the design to future cancer trials can greatly improve the efficiency, consequently save cost and shorten the development period. [ABSTRACT FROM AUTHOR]

An immunotherapy trial often uses the phase I/II design to identify the optimal biological dose, which monitors the efficacy and toxicity outcomes simultaneously in a single trial. The progression‐free survival rate is often used as the efficacy outcome in phase I/II immunotherapy trials. As a result, patients developing disease progression in phase I/II immunotherapy trials are generally seriously ill and are often treated off the trial for ethical consideration. Consequently, the happening of disease progression will terminate the toxicity event but not vice versa, so the issue of the semi‐competing risks arises. Moreover, this issue can become more intractable with the late‐onset outcomes, which happens when a relatively long follow‐up time is required to ascertain progression‐free survival. This paper proposes a novel Bayesian adaptive phase I/II design accounting for semi‐competing risks outcomes for immunotherapy trials, referred to as the dose‐finding design accounting for semi‐competing risks outcomes for immunotherapy trials (SCI) design. To tackle the issue of the semi‐competing risks in the presence of late‐onset outcomes, we re‐construct the likelihood function based on each patient's actual follow‐up time and develop a data augmentation method to efficiently draw posterior samples from a series of Beta‐binomial distributions. We propose a concise curve‐free dose‐finding algorithm to adaptively identify the optimal biological dose using accumulated data without making any parametric dose–response assumptions. Numerical studies show that the proposed SCI design yields good operating characteristics in dose selection, patient allocation, and trial duration. [ABSTRACT FROM AUTHOR]

Asymptotic distribution theory for maximum likelihood estimators under fixed alternative hypotheses is reported in the literature even though the power of any realistic test converges to one under fixed alternatives. Under fixed alternatives, authors have established that nuisance parameter estimates are inconsistent when sample size re-estimation (SSR) follows blinded randomization. These results have helped to inhibit the use of SSR. In this paper, we argue for local alternatives to be used instead of fixed alternatives. We treat unavailable treatment assignments in blinded experiments as missing data and rely on single imputation from marginal distributions to fill in for missing data. With local alternatives, it is sufficient to proceed only with the first step of the EM algorithm mimicking imputation under the null hypothesis. Then, we show that blinded and unblinded estimates of the nuisance parameter σ θ 2 are consistent, and re-estimated sample sizes converge to their locally asymptotically optimal values. This theoretical finding is confirmed through Monte-Carlo simulation studies. Practical utility is illustrated through a multiple logistic regression example. We conclude that, for hypothesis testing with a predetermined minimally clinically relevant local effect size, both blinded and unblinded SSR procedures lead to similar sample sizes and power. [ABSTRACT FROM AUTHOR]

Adaptive two-stage designs for clinical trials are well understood from a statistical perspective. However, there is still few research on how the stage-two sample size looks like when it is regarded as a function of the first-stage test statistic. In this paper, a formal proof on the concavity of the sample size function is provided if the design's second stage is optimized such that it minimizes the expected sample size under the alternative under constraints on maximal type I error rate and minimal power. [ABSTRACT FROM AUTHOR]

In sequential analysis, hypothesis testing is performed repeatedly in a prospective manner as data accrue over time to quickly arrive at an accurate conclusion or decision. In this tutorial paper, detailed explanations are given for both designing and operating sequential testing. We describe the calculation of exact thresholds for stopping or signaling, statistical power, expected time to signal, and expected sample sizes for sequential analysis with Poisson and binary type data. The calculations are run using the package Sequential, constructed in R language. Real data examples are inspired on clinical trials practice, such as the current efforts to develop treatments to face the COVID‐19 pandemic, and the comparison of treatments of osteoporosis. In addition, we mimic the monitoring of adverse events following influenza vaccination and Pediarix vaccination. [ABSTRACT FROM AUTHOR]

Early‐phase dose‐finding clinical trials are often subject to the issue of late‐onset outcomes. In phase I/II clinical trials, the issue becomes more intractable because toxicity and efficacy can be competing risk outcomes such that the occurrence of the first outcome will terminate the other one. In this paper, we propose a novel Bayesian adaptive phase I/II clinical trial design to address the issue of late‐onset competing risk outcomes. We use the continuation‐ratio model to characterize the trinomial response outcomes and the cause‐specific hazard rate method to model the competing‐risk survival outcomes. We treat the late‐onset outcomes as missing data and develop a Bayesian data augmentation method to impute the missing data from the observations. We also propose an adaptive dose‐finding algorithm to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed design yields desirable operating characteristics. [ABSTRACT FROM AUTHOR]

Covariate‐adaptive randomization (CAR) is widely used in clinical trials to balance treatment allocation over covariates. Over the past decade, significant progress has been made on the theoretical properties of covariate‐adaptive design and associated inference. However, most results are established under the assumption that the covariates are correctly measured. In practice, measurement error is inevitable, resulting in misclassification for discrete covariates. When covariate misclassification is present in a clinical trial conducted using CAR, the impact is twofold: it impairs the intended covariate balance, and raises concerns over the validity of test procedures. In this paper, we consider the impact of misclassification on covariate‐adaptive randomized trials from the perspectives of both design and inference. We derive the asymptotic normality, and thereby the convergence rate, of the imbalance of the true covariates for a general family of covariate‐adaptive randomization methods, and show that a superior covariate balance can still be attained compared to complete randomization. We also show that the two sample t‐test is conservative, with a reduced Type I error, but that this can be corrected using a bootstrap method. Moreover, if the misclassified covariates are adjusted in the model used for analysis, the test maintains its nominal Type I error, with an increased power. Our results support the use of covariate‐adaptive randomization in clinical trials, even when the covariates are subject to misclassification. [ABSTRACT FROM AUTHOR]

The delayed outcome issue is common in early phase dose-finding clinical trials. This problem becomes more intractable in phase I/II clinical trials because both toxicity and efficacy responses are subject to the delayed outcome issue. The existing methods applying for the phase I trials cannot be used directly for the phase I/II trial due to a lack of capability to model the joint toxicity–efficacy distribution. In this paper, we propose a conditional weighted likelihood (CWL) method to circumvent this issue. The key idea of the CWL method is to decompose the joint probability into the product of marginal and conditional probabilities and then weight each probability based on each patient's actual follow-up time. The CWL method makes no parametric model assumption on either the dose–response curve or the toxicity–efficacy correlation and therefore can be applied to any existing phase I/II trial design. Numerical trial applications show that the proposed CWL method yields desirable operating characteristics. [ABSTRACT FROM AUTHOR]

Multi-arm multi-stage clinical trials in which more than two drugs are simultaneously investigated provide gains over separate single- or two-arm trials. In this paper we propose a generic Bayesian adaptive decision-theoretic design for multi-arm multi-stage clinical trials with K (K ≥ 2) arms. The basic idea is that after each stage a decision about continuation of the trial and accrual of patients for an additional stage is made on the basis of the expected reduction in loss. For this purpose, we define a loss function that incorporates the patient accrual costs as well as costs associated with an incorrect decision at the end of the trial. An attractive feature of our loss function is that its estimation is computationally undemanding, also when K > 2. We evaluate the frequentist operating characteristics for settings with a binary outcome and multiple experimental arms. We consider both the situation with and without a control arm. In a simulation study, we show that our design increases the probability of making a correct decision at the end of the trial as compared to nonadaptive designs and adaptive two-stage designs. [ABSTRACT FROM AUTHOR]

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]

The COVID-19 pandemic has triggered explosive activities in searching for cures, including vaccines against the SARS-CoV-2 infection. As of April 30, 2020, there are at least 102 COVID-19 vaccine development programs worldwide, the majority of which are in preclinical development phases, five are in phase I trial, and three are in phase I/II trial. Experts caution against rushing COVID-19 vaccine development, not only because the knowledge about SARS-CoV-2 is lacking (albeit rapidly accumulating), but also because vaccine development is a complex, lengthy process with its own rules and timelines. Clinical trials are critically important in vaccine development, usually starting from small-scale phase I trials and gradually moving to the next phases (II and III) after the primary objectives are met. This paper is intended to provide an overview on design considerations for vaccine clinical trials, with a special focus on COVID-19 vaccine development. Given the current pandemic paradigm and unique features of vaccine development, our recommendations from statistical design perspective for COVID-19 vaccine trials include: (1) novel trial design (e.g., master protocol) to expedite the simultaneous evaluation of multiple candidate vaccines or vaccine doses, (2) human challenge studies to accelerate clinical development, (3) adaptive design strategies (e.g., group sequential designs) for early termination due to futility, efficacy, and/or safety, (4) extensive modeling and simulation to characterize and establish long-term efficacy based on early-phase or short-term follow-up data, (5) safety evaluation as one of the primary focuses throughout all phases of clinical trials, (6) leveraging real-world data and evidence in vaccine trial design and analysis to establish vaccine effectiveness, and (7) global collaboration to form a joint development effort for more efficient use of resource and expertise and data sharing. [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]

Background: For many disease areas, there are often treatments in different stages of the development process. We consider the design of a two-arm parallel group trial where it is planned to add a new experimental treatment arm during the trial. This could potentially save money, patients, time and resources; however, the addition of a treatment arm creates a multiple comparison problem. Current practice in trials when a new treatment arm has been added is to compare the new treatment only to controls randomised concurrently, and this is the setting we consider here. Furthermore, for standard multi-arm trials, optimal allocation randomises a larger number of patients to the control arm than to each experimental treatment arm.Methods: In this paper we propose an adaptive design, the aim of which is to adapt the sample size of the trial when the new treatment arm is added to control the family-wise error rate (FWER) in the strong sense, whilst maintaining the marginal power of each treatment-to-control comparison at the level of the original study. We explore optimal allocation for designs where a treatment arm is added with the aim of increasing the overall power of the study, where we define the overall power to be the probability of detecting all treatments that are better than the control.Results and Conclusions: An increase in sample size is required to maintain the marginal power for each pairwise comparison when adding a treatment arm if control of the FWER is required at the level of the type I error in the original study. When control of the FWER is required in a single trial which adds an additional experimental treatment arm, but control of the FWER is not required in separate trials, depending on the design characteristics, it may be better to run a separate trial for each experimental treatment, in terms of the number of patients required. An increase in overall power can be achieved when optimal allocation is used once a treatment arm has been added to the trial, rather than continuing with equal allocation to all treatment arms. [ABSTRACT FROM AUTHOR]

Background: There is a growing interest in the use of Bayesian adaptive designs in late-phase clinical trials. This includes the use of stopping rules based on Bayesian analyses in which the frequentist type I error rate is controlled as in frequentist group-sequential designs.Methods: This paper presents a practical comparison of Bayesian and frequentist group-sequential tests. Focussing on the setting in which data can be summarised by normally distributed test statistics, we evaluate and compare boundary values and operating characteristics.Results: Although Bayesian and frequentist group-sequential approaches are based on fundamentally different paradigms, in a single arm trial or two-arm comparative trial with a prior distribution specified for the treatment difference, Bayesian and frequentist group-sequential tests can have identical stopping rules if particular critical values with which the posterior probability is compared or particular spending function values are chosen. If the Bayesian critical values at different looks are restricted to be equal, O'Brien and Fleming's design corresponds to a Bayesian design with an exceptionally informative negative prior, Pocock's design to a Bayesian design with a non-informative prior and frequentist designs with a linear alpha spending function are very similar to Bayesian designs with slightly informative priors.This contrasts with the setting of a comparative trial with independent prior distributions specified for treatment effects in different groups. In this case Bayesian and frequentist group-sequential tests cannot have the same stopping rule as the Bayesian stopping rule depends on the observed means in the two groups and not just on their difference. In this setting the Bayesian test can only be guaranteed to control the type I error for a specified range of values of the control group treatment effect.Conclusions: Comparison of frequentist and Bayesian designs can encourage careful thought about design parameters and help to ensure appropriate design choices are made. [ABSTRACT FROM AUTHOR]

The need for efficient clinical trial designs has led to the development of flexible multi-arm designs. In this paper, we investigated sample size re-estimation (SSR) methods for fully flexible designs that allow for any unplanned treatment selection, early stopping for efficacy or futility. Even though SSR methods for two-arm trials are well developed, they are not directly applicable to certain fully flexible multi-arm trials due to the application of the closed test procedure. We derive SSR procedures based on the conditional power approach in the context of confirmatory multi-arm trials for three designs: inverse normal combination test, Fisher's combination test and flexible group sequential design. We conduct extensive simulation studies to evaluate the performance of the three designs with and without SSR. Results show that the relative performance of the three designs depends on many factors. In practice, simulation studies are usually necessary to help determine the most appropriate design. [ABSTRACT FROM AUTHOR]

Introduction: In contemporary oncology drug development, implementation of novel early-phase designs with the ability to address multiple research objectives is needed to better refine regimens. This paper describes an adaptive design strategy for identifying a range of optimal regimens based on two endpoints within multiple cohorts. The proposed design was developed to address objectives in an early-phase trial of cancer vaccines in combination with agonistic antibodies to CD40 and CD27. Materials and methods: We describe a model-based design strategy that was developed for a trial evaluating the safety and immunogenicity of vaccination with (1) peptides plus CD40 antibody and TLR3 ligand, (2) systemic administration of an agonistic CD27 antibody, and (3) to assess immune response from (1) and (2) compared to optimal controls in participants with stage IIB-IV melanoma. Results and conclusions: The proposed design is a practical adaptive method for use with combined immunotherapy regimens with multiple objectives within multiple cohorts of interest. Further advances in the effectiveness of cancer immunotherapies will require new approaches that include redefining optimal strategies to take multiple regimens forward into later phases, incorporating additional endpoints in the dose selection process and testing drug combination therapies to improve efficacy and reduce toxicity. Our goal is to facilitate the acceptance and application of more novel designs in contemporary early development trials. [ABSTRACT FROM AUTHOR]

This paper aims to investigate whether any bridge is possible between so-called best intention and D-optimum designs. It introduces combined criteria for dose optimisation in seamless phase I/II adaptive clinical trials. Each of the optimality criteria considers efficacy and toxicity as endpoints and is based on the probability of a successful outcome and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. In addition, one of the criteria incorporates penalties for choosing a toxic or inefficacious dose. Starting with the lowest dose, the adaptive design selects the dose for each subsequent cohort that maximises the respective defined criterion. The methodology is illustrated with a dose-response model that assumes trinomial responses. Simulation studies show that the method is capable of identifying the optimal dose accurately without exposing many patients to toxic doses. [ABSTRACT FROM AUTHOR]

Background: The application of adaptive design methodology within a clinical trial setting is becoming increasingly popular. However the application of these methods within trials is not being reported as adaptive designs hence making it more difficult to capture the emerging use of these designs. Within this review, we aim to understand how adaptive design methodology is being reported, whether these methods are explicitly stated as an 'adaptive design' or if it has to be inferred and to identify whether these methods are applied prospectively or concurrently.Methods: Three databases; Embase, Ovid and PubMed were chosen to conduct the literature search. The inclusion criteria for the review were phase II, phase III and phase II/III randomised controlled trials within the field of Oncology that published trial results in 2015. A variety of search terms related to adaptive designs were used.Results: A total of 734 results were identified, after screening 54 were eligible. Adaptive designs were more commonly applied in phase III confirmatory trials. The majority of the papers performed an interim analysis, which included some sort of stopping criteria. Additionally only two papers explicitly stated the term 'adaptive design' and therefore for most of the papers, it had to be inferred that adaptive methods was applied. Sixty-five applications of adaptive design methods were applied, from which the most common method was an adaptation using group sequential methods.Conclusions: This review indicated that the reporting of adaptive design methodology within clinical trials needs improving. The proposed extension to the current CONSORT 2010 guidelines could help capture adaptive design methods. Furthermore provide an essential aid to those involved with clinical trials. [ABSTRACT FROM AUTHOR]

The Australian Government selected France’s Direction des Constructions Navales Services to design Australia’s future submarine, the Shortfin Barracuda. This paper evaluates different system developmental approaches that can be applied, such as the U.S. acquisition life cycle and a research, engineer, design, adapt, reflect (RADER) model. The evaluation showed a need for deliberate regard for the significant philosophical, political, military, cultural and economic differences of the French practices compared to U.S., U.K. and Australian. The paper reviews major lessons learned in Australian acquisitions regarding early preview test and evaluation (T&E), including the Collins Class Submarine, and proposes 11 T&E goals, especially early commitment to an estimated seven test sites needed to ensure the early design is proven and low risk. To save substantial overall time and rework, greater investment is needed in the social spaces for critical thinking and T&E to guide the submarine towards a continuously evolving design and build. [ABSTRACT FROM AUTHOR]

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker-stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta-binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a "customized" equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs. [ABSTRACT FROM AUTHOR]

Maximum entropy sampling (MES) criteria provide a useful framework for studying sequential designs for computer experiments in a Bayesian framework. However, there is some technical difficulty in making the procedure fully adaptive in the sense of making proper use of previous output as well as input data. In the simple Gaussian set-up only previous input values need to be used. The approach discussed uses a full hierarchical model for the Gaussian process. The idea is to take advantage of the Karhumen-Loéve (K-L) expansion to approximate the process covariance function using an orthogonal function basis. It is argued that this may make it easier to use Bayesian hierarchical models, rather than estimating the covariance parameters directly, using the traditional approach. The article paper shows how to reduce the full MES method to a simple one by using a special empirical Bayes approximation, rather than using time-consuming integration. A simple simulator example is presented to show that full adaptation is beneficial. [ABSTRACT FROM AUTHOR]

In this paper, we develop the fixed-borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject-level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior-data conflict. If there is too much conflict between the new trial data and the historical control data, the prior information is discarded and the study proceeds to the final analysis opportunity at which time a noninformative prior is used for analysis. Otherwise, the trial is stopped early and the informative power prior is used for analysis. Simulation studies are used to calibrate the early stopping rule. The proposed design methodology seamlessly accommodates covariates in the statistical model, which the authors argue is necessary to justify borrowing information from historical controls. Implementation of the proposed methodology is straightforward for many common data models, including linear regression models, generalized linear regression models, and proportional hazards models. We demonstrate the methodology to design a cardiovascular outcomes trial for a hypothetical new therapy for treatment of type 2 diabetes mellitus and borrow information from the SAVOR trial, one of the earliest cardiovascular outcomes trials designed to assess cardiovascular risk in antidiabetic therapies. [ABSTRACT FROM AUTHOR]

Background: Adequate reporting of adaptive designs (ADs) maximises their potential benefits in the conduct of clinical trials. Transparent reporting can help address some obstacles and concerns relating to the use of ADs. Currently, there are deficiencies in the reporting of AD trials. To overcome this, we have developed a consensus-driven extension to the CONSORT statement for randomised trials using an AD. This paper describes the processes and methods used to develop this extension rather than detailed explanation of the guideline.Methods: We developed the guideline in seven overlapping stages: 1) Building on prior research to inform the need for a guideline; 2) A scoping literature review to inform future stages; 3) Drafting the first checklist version involving an External Expert Panel; 4) A two-round Delphi process involving international, multidisciplinary, and cross-sector key stakeholders; 5) A consensus meeting to advise which reporting items to retain through voting, and to discuss the structure of what to include in the supporting explanation and elaboration (E&E) document; 6) Refining and finalising the checklist; and 7) Writing-up and dissemination of the E&E document. The CONSORT Executive Group oversaw the entire development process.Results: Delphi survey response rates were 94/143 (66%), 114/156 (73%), and 79/143 (55%) in rounds 1, 2, and across both rounds, respectively. Twenty-seven delegates from Europe, the USA, and Asia attended the consensus meeting. The main checklist has seven new and nine modified items and six unchanged items with expanded E&E text to clarify further considerations for ADs. The abstract checklist has one new and one modified item together with an unchanged item with expanded E&E text. The E&E document will describe the scope of the guideline, the definition of an AD, and some types of ADs and trial adaptations and explain each reporting item in detail including case studies.Conclusions: We hope that making the development processes, methods, and all supporting information that aided decision-making transparent will enhance the acceptability and quick uptake of the guideline. This will also help other groups when developing similar CONSORT extensions. The guideline is applicable to all randomised trials with an AD and contains minimum reporting requirements. [ABSTRACT FROM AUTHOR]

This article discusses the critical and complex design decisions associated with transitioning an interviewer-administered survey to a self-administered, postal, web/paper survey. Our approach embeds adaptive, responsive, and tailored (ART) design principles and data visualization during a multi-phased data collection operation to project the outcomes of each phase in preparation for subsequent phases. This requires rapid decision making based upon experimental results using a data visualization system to monitor critical-to-quality (CTQ) metrics and facilitate projections of outcomes from the current phase of data collection to inform the design of the subsequent phase. We describe the objectives of the overall design, the features designed to address these objectives, components of the visual adaptive total design (ATD) system for monitoring quality components and relative costs in real time, and examples of the visualization elements and functionalities that were used in one case study. We also discuss subsequent initiatives to develop an interactive version of the monitoring tool and applications for other studies, including those employing adaptive, responsive, and tailored (ART) designs. Our case study is a series of pilot studies conducted for the Residential Energy Consumption Survey (RECS), sponsored by the U.S. Energy Information Administration (EIA). [ABSTRACT FROM AUTHOR]

We discuss an evidence-based approach to guiding real-time design decisions during the course of survey data collection. We call it responsive and adaptive design (RAD), a scientific framework driven by cost-quality tradeoff analysis and optimization that enables the most efficient production of high-quality data. The notion of RAD is not new; nor is it a silver bullet to resolve all the difficulties of complex survey design and challenges. RAD embraces precedents and variants of responsive design and adaptive design that survey designers and researchers have practiced over decades. In this paper, we present the four pillars of RAD: survey process data and auxiliary information, design features and interventions, explicit quality and cost metrics, and a quality-cost optimization tailored to survey strata. We discuss how these building blocks of RAD are addressed by articles published in the 2017 JOS special issue and this special section. It is a tale of the three perspectives filling in each other. We carry over each of these three perspectives to articulate the remaining challenges and opportunities for the advancement of RAD. We recommend several RAD ideas for future research, including survey-assisted population modeling, rigorous optimization strategies, and total survey cost modeling. [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]

Background: Scaling up complex health interventions to large populations is not a straightforward task. Without intentional, guided efforts to scale up, it can take many years for a new evidence-based intervention to be broadly implemented. For the past decade, researchers and implementers have developed models of scale-up that move beyond earlier paradigms that assumed ideas and practices would successfully spread through a combination of publication, policy, training, and example. Drawing from the previously reported frameworks for scaling up health interventions and our experience in the USA and abroad, we describe a framework for taking health interventions to full scale, and we use two large-scale improvement initiatives in Africa to illustrate the framework in action. We first identified other scale-up approaches for comparison and analysis of common constructs by searching for systematic reviews of scale-up in health care, reviewing those bibliographies, speaking with experts, and reviewing common research databases (PubMed, Google Scholar) for papers in English from peer-reviewed and "gray" sources that discussed models, frameworks, or theories for scale-up from 2000 to 2014. We then analyzed the results of this external review in the context of the models and frameworks developed over the past 20 years by Associates in Process Improvement (API) and the Institute for Healthcare improvement (IHI). Finally, we reflected on two national-scale improvement initiatives that IHI had undertaken in Ghana and South Africa that were testing grounds for early iterations of the framework presented in this paper.Results: The framework describes three core components: a sequence of activities that are required to get a program of work to full scale, the mechanisms that are required to facilitate the adoption of interventions, and the underlying factors and support systems required for successful scale-up. The four steps in the sequence include (1) Set-up, which prepares the ground for introduction and testing of the intervention that will be taken to full scale; (2) Develop the Scalable Unit, which is an early testing phase; (3) Test of Scale-up, which then tests the intervention in a variety of settings that are likely to represent different contexts that will be encountered at full scale; and (4) Go to Full Scale, which unfolds rapidly to enable a larger number of sites or divisions to adopt and/or replicate the intervention.Conclusions: Our framework echoes, amplifies, and systematizes the three dominant themes that occur to varying extents in a number of existing scale-up frameworks. We call out the crucial importance of defining a scalable unit of organization. If a scalable unit can be defined, and successful results achieved by implementing an intervention in this unit without major addition of resources, it is more likely that the intervention can be fully and rapidly scaled. When tying this framework to quality improvement (QI) methods, we describe a range of methodological options that can be applied to each of the four steps in the framework's sequence. [ABSTRACT FROM AUTHOR]

The paper shows the results of the first phase of the research project "METROPOLIS - Methodologies and Technologies for integrated and sustainable adaptation and security of urban systems" developed by STRESS Scarl - High Technology District for Sustainable Building of the Campania Region. The project is aimed at the development of innovative strategies for a resilient urban system and design guidelines for appropriate choices of urban regeneration based on the assessment and mitigation of natural and man-made hazards. The paper describes the results concerning the definition of innovative methodologies for the knowledge and mapping of urban vulnerability to climate risks in the East Naples area. The cross-disciplinary and multi-scale approach integrates knowledge and technology from university and industrial partners to develop a decision support tool in the field of urban regeneration. The study of the impacts of extreme weather events, based on the simulation of climate change scenarios in the area of East Naples, includes the data management in a GIS environment from satellite remote sensing, direct surveys and simulation software, focusing on the environmental and technological performance of urban spaces and elements. The research results report risk scenarios for pluvial flood and heat waves hazards according to both climatic variables, both aggravating phenomena arising from the characteristics of urban settlements. The complex reading of the buildings-open spaces system and its response to climate change conditions has allowed to define the vulnerability of elements at risk, as well as adaptation and mitigation solutions to be implemented within urban regeneration interventions, identifying critical issues in relation to comfort and environmental risk conditions, consumption and efficient use of resources, compliance of the technological choices to specific requirements. [ABSTRACT FROM AUTHOR]

This paper deals with the design of the likelihood continual reassessment method, which is an increasingly widely used model-based method for dose-finding studies. It is common to implement the method in a two-stage approach, whereby the model-based stage is activated after an initial sequence of patients has been treated. While this two-stage approach is practically appealing, it lacks a theoretical framework, and it is often unclear how the design components should be specified. This paper develops a general framework based on the coherence principle, from which we derive a design calibration process. A real clinical-trial example is used to demonstrate that the proposed process can be implemented in a timely and reproducible manner, while offering competitive operating characteristics. We explore the operating characteristics of different models within this framework and show the performance to be insensitive to the choice of dose-toxicity model. [ABSTRACT FROM PUBLISHER]

Key design characteristics (KDCs) are important information related to the product and part designs, which significantly influence on the product's functions, performances, and quality. Identifying KDCs for a complex product will help designers to focus on key design parameters during the design process and rapidly obtain design schemes based on their close relationships to the product's functions, performances, and quality. Although there are some researches on key characteristic (KC) identification, most of them are focused on key process characteristics (KPCs) and few on KDCs. There also lacks a KDC identification framework to support KDC identification with better completeness and diverse usages. Adaptive design is the most important pattern of complex product design. Therefore, this paper presents a systematic method to identify KDCs for complex product adaptive design, in which KDCs can be determined by two related phases. Firstly, a product design specification (PDS)-KDC Candidates Network (PKCN) is constructed by using existing product instance data, cluster analysis, KC flow-down, and network analysis approaches. Then, the result from the first phase is used as a basis to identify KDCs for adaptive design. Three KDC identification techniques: similarity reasoning technique, breadth-first search (BFS), and the gray relational analysis approach are applied to find out KDCs from the PKCN, which are the most sensitive to the variation of a PDS. These identified KDCs can help designers to understand the relationships between KDCs and PDS and rapidly develop a design scheme. The effectiveness and the feasibility of the proposed method are verified by a case study via the development of an electric multiple unit (EMU)'s bogie. [ABSTRACT FROM AUTHOR]

Seamless phase II/III clinical trials have attracted increasing attention recently. They mainly use Bayesian response adaptive randomization (RAR) designs. There has been little research into seamless clinical trials using frequentist RAR designs because of the difficulty in performing valid statistical inference following this procedure. The well-designed frequentist RAR designs can target theoretically optimal allocation proportions, and they have explicit asymptotic results. In this paper, we study the asymptotic properties of frequentist RAR designs with adjusted target allocation proportions, and investigate statistical inference for this procedure. The properties of the proposed design provide an important theoretical foundation for advanced seamless clinical trials. Our numerical studies demonstrate that the design is ethical and efficient. [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]

This paper deals with the optimization of industrial asset management strategies, whose profitability is characterized by the Net Present Value (NPV) indicator which is assessed by a Monte Carlo simulator. The developed method consists in building a metamodel of this stochastic simulator, allowing to obtain, for a given model input, the NPV probability distribution without running the simulator. The present work is concentrated on the emulation of the quantile function of the stochastic simulator by interpolating well chosen basis functions and metamodeling their coefficients (using the Gaussian process metamodel). This quantile function metamodel is then used to treat a problem of strategy maintenance optimization (four systems installed on different plants), in order to optimize an NPV quantile. Using the Gaussian process framework, an adaptive design method (called quantile function expected improvement) is defined by extending in our case the well-known efficient global optimization algorithm. This allows to obtain an 'optimal' solution using a small number of simulator runs. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Non-stochastic simulation models, such as finite element or computational fluid dynamics, often support real experiments in industrial research. It has become a common practice to provide a meta-model as computer experiments can be highly complex and time-consuming, and the design space is often broad. The meta-model is an approximation of the computer experiments response adapted both globally and locally on the design space, in order to capture local minima/maxima. The Kriging model, first proposed in Geostatistics, is doubtlessly the most popular meta-model because of its recognized ability to provide high-quality predictions. The underlying correlation structure can be evaluated either by estimating the parameters of correlation or by means of a variogram. In this paper, the performance of the Kriging model is compared with an Artificial Neural Network meta-model in order to determine which model guarantees higher accuracy in predicting the result of four-dimensional computational fluid dynamics experiments for low pressure turbines where energy loss values are provided. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the development of molecular targeted drugs, predictive biomarkers have played an increasingly important role in identifying patients who are likely to receive clinically meaningful benefits from experimental drugs (i.e., sensitive subpopulation) even in early clinical trials. For continuous biomarkers, such as mRNA levels, it is challenging to determine cutoff value for the sensitive subpopulation, and widely accepted study designs and statistical approaches are not currently available. In this paper, we propose the Bayesian adaptive patient enrollment restriction (BAPER) approach to identify the sensitive subpopulation while restricting enrollment of patients from the insensitive subpopulation based on the results of interim analyses, in a randomized phase 2 trial with time-to-endpoint outcome and a single biomarker. Applying a four-parameter change-point model to the relationship between the biomarker and hazard ratio, we calculate the posterior distribution of the cutoff value that exhibits the target hazard ratio and use it for the restriction of the enrollment and the identification of the sensitive subpopulation. We also consider interim monitoring rules for termination because of futility or efficacy. Extensive simulations demonstrated that our proposed approach reduced the number of enrolled patients from the insensitive subpopulation, relative to an approach with no enrollment restriction, without reducing the likelihood of a correct decision for next trial (no-go, go with entire population, or go with sensitive subpopulation) or correct identification of the sensitive subpopulation. Additionally, the four-parameter change-point model had a better performance over a wide range of simulation scenarios than a commonly used dichotomization approach. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The discovery of numerous molecular subtypes of common cancers leads to the investigation of biomarkers potentially predictive of treatment effect of an experimental treatment in multiple histologies. However, the prevalence of a putative predictive biomarker within a histology is often low, which makes it challenging to enroll adequate number of patients in a conventional histology-based confirmatory trial. An alternative approach is to study patients with a common biomarker signature in a “basket” trial across multiple histologies. This study design has previously been used to explore experimental therapies with potentially transformative effects. We present a general design concept of a Phase 3 basket trial broadly applicable to any effective therapy. The trial is designed with scientific and statistical rigor to enable the approval of an experimental treatment in multiple tumor indications based on the outcome from a single study. Given the difficulty in indication selection, the basic idea is to prune the inactive indications at an interim analysis and pool the active indications in the final analysis. A critical statistical issue of the basket design is Type I error control for the pooled analysis after pruning. While pruning may be seen as cherry-picking which tends to inflate the Type I error, it also shares similarity with a binding futility analysis which tends to deflate the Type I error if all indications are pruned. The net impact of pruning is complicated. The use of different endpoints for pruning and pooling further complicates the issue. This paper will provide statistical details on Type I error control for the general basket design concept under three sample size adjustment strategies after pruning. Power and sample size calculations are also provided. Comparisons are made to a straightforward design without pruning. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]