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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]

The U.S. Census Bureau is researching and testing new methods to reduce the cost of the 2020 Census while maintaining data quality. One of the most costly components of the 2010 Census was Nonresponse Followup. In this operation, enumerators conducted in-person interviews at housing units that did not return a census questionnaire by mail. For the 2010 Census, enumerators were instructed to visit each of these units up to three times until the case was resolved. Additionally, enumerators were to make up to three contact attempts by telephone. In this paper, we present an overview of current research on determining the number of contact attempts that should be made to nonresponding units with an emphasis on cost containment and improved overall productivity. Rather than the fixed contact strategy employed in the 2010 Census, we consider adaptive approaches that maintain the quality of the data. We present initial results of possible approaches using data from the 2010 Census and discuss the implications of the methods. We also discuss modeling contact probabilities for each hour of the day to support the case management system. [ABSTRACT FROM AUTHOR]

Simon's optimal two-stage design has been widely used in early phase clinical trials for Oncology and AIDS studies with binary endpoints. With this approach, the second-stage sample size is fixed when the trial passes the first stage with sufficient activity. Adaptive designs, such as those due to Banerjee and Tsiatis (2006) and Englert and Kieser (2013), are flexible in the sense that the second-stage sample size depends on the response from the first stage, and these designs are often seen to reduce the expected sample size under the null hypothesis as compared with Simon's approach. An unappealing trait of the existing designs is that they are not associated with a second-stage sample size, which is a non-increasing function of the first-stage response rate. In this paper, an efficient intelligent process, the branch-and-bound algorithm, is used in extensively searching for the optimal adaptive design with the smallest expected sample size under the null, while the type I and II error rates are maintained and the aforementioned monotonicity characteristic is respected. The proposed optimal design is observed to have smaller expected sample sizes compared to Simon's optimal design, and the maximum total sample size of the proposed adaptive design is very close to that from Simon's method. The proposed optimal adaptive two-stage design is recommended for use in practice to improve the flexibility and efficiency of early phase therapeutic development. [ABSTRACT FROM AUTHOR]

This paper is motivated by a phase I-II clinical trial of a targeted agent for advanced solid tumors. We study a stylized version of this trial with the goal to determine optimal actions in each of two cycles of therapy. A design is presented that generalizes the decision-theoretic two-cycle design of Lee and others (2015. Bayesian dose-finding in two treatment cycles based on the joint utility of efficacy and toxicity. Journal of the American Statistical Association, to appear) to accommodate ordinal outcomes. Backward induction is used to jointly optimize the actions taken for each patient in each of the two cycles, with the second action accounting for the patient's cycle 1 dose and outcomes. A simulation study shows that simpler designs obtained by dichotomizing the ordinal outcomes either perform very similarly to the proposed design, or have much worse performance in some scenarios. We also compare the proposed design with the simpler approaches of optimizing the doses in each cycle separately, or ignoring the distinction between cycles 1 and 2. [ABSTRACT FROM AUTHOR]

Adaptive designs have the potential to improve efficiency in the evaluation of new medical treatments in comparison to traditional fixed sample size designs. However, they are still not widely used in practice in clinical research. Little research has been conducted to investigate what adaptive designs are being undertaken. This review highlights the current state of registered adaptive designs and their characteristics. The review looked at phase II, II/III and III trials registered on ClinicalTrials.gov from 29 February 2000 to 1 June 2014, supplemented with trials from the National Institute for Health Research register and known adaptive trials. A range of adaptive design search terms were applied to the trials extracted from each database. Characteristics of the adaptive designs were then recorded including funder, therapeutic area and type of adaptation. The results in the paper suggest that the use of adaptive designs has increased. They seem to be most often used in phase II trials and in oncology. In phase III trials, the most popular form of adaptation is the group sequential design. The review failed to capture all trials with adaptive designs, which suggests that the reporting of adaptive designs, such as in clinical trials registers, needs much improving. We recommend that clinical trial registers should contain sections dedicated to the type and scope of the adaptation and that the term 'adaptive design' should be included in the trial title or at least in the brief summary or design sections. [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]

In prior works, this group demonstrated the feasibility of valid adaptive sequential designs for crossover bioequivalence studies. In this paper, we extend the prior work to optimize adaptive sequential designs over a range of geometric mean test/reference ratios (GMRs) of 70-143% within each of two ranges of intra-subject coefficient of variation (10-30% and 30-55%). These designs also introduce a futility decision for stopping the study after the first stage if there is sufficiently low likelihood of meeting bioequivalence criteria if the second stage were completed, as well as an upper limit on total study size. The optimized designs exhibited substantially improved performance characteristics over our previous adaptive sequential designs. Even though the optimized designs avoided undue inflation of type I error and maintained power at [ABSTRACT FROM AUTHOR]

The covariate-adjusted response adaptive (CARA) design has been shown to be better than traditional designs in terms of both ethics and efficiency. However, its mechanism for allocating subjects makes certain stochastic processes such as allocated response sequences very complicated. Consequently, the validation of statistical inference is usually challenging, and few theoretical results have been obtained. In this paper we systematically solve some fundamental problems for statistical inference with CARA designs. First, we obtain the conditional independence and distribution of allocated response sequences, which is the basis for further theoretical investigation. Second, we propose a new family of CARA designs, which is extensively applicable. We more importantly provide a framework for new CARA designs with unified asymptotic results for statistical inference. The numerical results demonstrate the advantages of the proposed CARA designs. Our findings are crucial in understanding the CARA design as well as its development and application. [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]

Seamless phase II/III clinical trials in which an experimental treatment is selected at an interim analysis have been the focus of much recent research interest. Many of the methods proposed are based on the group sequential approach. This paper considers designs of this type in which the treatment selection can be based on short-term endpoint information for more patients than have primary endpoint data available. We show that in such a case, the familywise type I error rate may be inflated if previously proposed group sequential methods are used and the treatment selection rule is not specified in advance. A method is proposed to avoid this inflation by considering the treatment selection that maximises the conditional error given the data available at the interim analysis. A simulation study is reported that illustrates the type I error rate inflation and compares the power of the new approach with two other methods: a combination testing approach and a group sequential method that does not use the short-term endpoint data, both of which also strongly control the type I error rate. The new method is also illustrated through application to a study in Alzheimer's disease. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Most exploratory clinical trials in cancer are designed as single-arm trials using a binary efficacy outcome with or without interim monitoring. In this context, we have proposed a Bayesian adaptive design denoted as predictive sample size selection design (PSSD), which considered a binary efficacy outcome associated with early futility stopping ( Statistics in Medicine 2012; 31: 4243-4254). As a matter of course, it would be more ethical and informative to evaluate safety as well as efficacy during the course of a trial. However, in most of the trials, only major adverse events are taken into account for early termination of the trial, and safety itself is used as a secondary endpoint. In this paper, we propose an extension of the PSSD to monitor efficacy, take into consideration the sample size adaptation during the trial and add continuous monitoring of safety to the trial design. This method is developed in the Bayesian framework, in which a decision to stop for reasons of safety can be made based on the posterior probability or predictive probability, not necessarily at the time of pre-specified monitoring for efficacy. We investigate the operating characteristics of the proposed method through simulation studies and show that the posterior probability-based method with less informative prior to monitor safety has more reasonable performance. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In China, rapid city expansion and the consequent loss and fragmentation of green spaces presents a major challenge for urban planners wanting to maintain urban sustainability. The aim of this paper is to formulate a workable framework to plan and manage urban green infrastructure for urban sustainability through a case study of Longgang, a typical developed area in China. Urban green infrastructure is associated with various landscape types, which have different functional niches in a UGI network. By integrating morphological spatial pattern analysis (MSPA) into landscape ecological principles based on a geographic information system, the functional niches of landscapes were defined as core areas, connectors, islets, and three levels of potential connectors. Then a future urban green infrastructure scenario with hierarchical hubs, links, and sites was delineated. Finally, by contrasting the plan scenario and the basic ecological control line (BECL) of Longgang, some focus areas for management strategies were proposed. With this approach, green infrastructure elements are preserved first in a given region, and new greening element locations and adaptive measures are emphasized. It is hoped that this framework could be used to promote the spatial navigation of urban green-space system planning and urban sustainability in China. [ABSTRACT FROM AUTHOR]

Simon's two-stage designs are widely used in cancer phase II clinical trials for assessing the efficacy of a new treatment. However in practice, the actual sample size for the second stage is often different from the planned sample size, and the original inference procedure is no longer valid. Previous work on this problem has certain limitations in computation. In this paper, we attempt to maximize the unconditional power while controlling for the type I error for the modified second stage sample size. A normal approximation is used for computing the power, and the numerical results show that the approximation is accurate even under small sample sizes. The corresponding confidence intervals for the response rate are constructed by inverting the hypothesis test, and they have reasonable coverage while preserving the type I error. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The topic of applying two-stage designs in the field of bioequivalence studies has recently gained attention in the literature and in regulatory guidelines. While there exists some methodological research on the application of group sequential designs in bioequivalence studies, implementation of adaptive approaches has focused up to now on superiority and non-inferiority trials. Especially, no comparison of the features and performance characteristics of these designs has been performed, and therefore, the question of which design to employ in this setting remains open. In this paper, we discuss and compare 'classical' group sequential designs and three types of adaptive designs that offer the option of mid-course sample size recalculation. A comprehensive simulation study demonstrates that group sequential designs can be identified, which show power characteristics that are similar to those of the adaptive designs but require a lower average sample size. The methods are illustrated with a real bioequivalence study example. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Adaptive population enrichment designs enable the data-driven selection of one or more pre-specified subpopulations in an interim analysis, and the confirmatory proof of efficacy in the selected subset at the end of the trial. Sample size reassessment and other adaptive design changes can be performed as well. Strong control of the experimentwise Type I error rate is guaranteed by use of the combination testing principle together with the closed testing argument. In this paper the general methodology and designing issues when planning such a design are reviewed. It is shown how to derive overall confidence intervals andp-values. Criteria for assessing the operating characteristics of these designs are given, and the application is illustrated by examples. [ABSTRACT FROM AUTHOR]

Incorporating an emerging therapy as a new randomisation arm in a clinical trial that is open to recruitment would be desirable to researchers, regulators and patients to ensure that the trial remains current, new treatments are evaluated as quickly as possible, and the time and cost for determining optimal therapies is minimised. It may take many years to run a clinical trial from concept to reporting within a rapidly changing drug development environment; hence, in order for trials to be most useful to inform policy and practice, it is advantageous for them to be able to adapt to emerging therapeutic developments. This paper reports a comprehensive literature review on methodologies for, and practical examples of, amending an ongoing clinical trial by adding a new treatment arm. Relevant methodological literature describing statistical considerations required when making this specific type of amendment is identified, and the key statistical concepts when planning the addition of a new treatment arm are extracted, assessed and summarised. For completeness, this includes an assessment of statistical recommendations within general adaptive design guidance documents. Examples of confirmatory ongoing trials designed within the frequentist framework that have added an arm in practice are reported; and the details of the amendment are reviewed. An assessment is made as to how well the relevant statistical considerations were addressed in practice, and the related implications. The literature review confirmed that there is currently no clear methodological guidance on this topic, but that guidance would be advantageous to help this efficient design amendment to be used more frequently and appropriately in practice. Eight confirmatory trials were identified to have added a treatment arm, suggesting that trials can benefit from this amendment and that it can be practically feasible; however, the trials were not always able to address the key statistical considerations, often leading to uninterpretable or invalid outcomes. If the statistical concepts identified within this review are considered and addressed during the design of a trial amendment, it is possible to effectively assess a new treatment arm within an ongoing trial without compromising the original trial outcomes. [ABSTRACT FROM AUTHOR]

Dual-agent trials are now increasingly common in oncology research, and many proposed dose-escalation designs are available in the statistical literature. Despite this, the translation from statistical design to practical application is slow, as has been highlighted in single-agent phase I trials, where a 3 + 3 rule-based design is often still used. To expedite this process, new dose-escalation designs need to be not only scientifically beneficial but also easy to understand and implement by clinicians. In this paper, we propose a curve-free (nonparametric) design for a dual-agent trial in which the model parameters are the probabilities of toxicity at each of the dose combinations. We show that it is relatively trivial for a clinician's prior beliefs or historical information to be incorporated in the model and updating is fast and computationally simple through the use of conjugate Bayesian inference. Monotonicity is ensured by considering only a set of monotonic contours for the distribution of the maximum tolerated contour, which defines the dose-escalation decision process. Varied experimentation around the contour is achievable, and multiple dose combinations can be recommended to take forward to phase II. Code for R, Stata and Excel are available for implementation. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. [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]

The development of molecularly targeted therapies for certain types of cancers has led to the consideration of population enrichment designs that explicitly factor in the possibility that the experimental compound might differentially benefit different biomarker subgroups. In such designs, enrollment would initially be open to a broad patient population with the option to restrict future enrollment, following an interim analysis, to only those biomarker subgroups that appeared to be benefiting from the experimental therapy. While this strategy could greatly improve the chances of success for the trial, it poses several statistical and logistical design challenges. Because late-stage oncology trials are typically event driven, one faces a complex trade-off between power, sample size, number of events, and study duration. This trade-off is further compounded by the importance of maintaining statistical independence of the data before and after the interim analysis and of optimizing the timing of the interim analysis. This paper presents statistical methodology that ensures strong control of type 1 error for such population enrichment designs, based on generalizations of the conditional error rate approach. The special difficulties encountered with time-to-event endpoints are addressed by our methods. The crucial role of simulation for guiding the choice of design parameters is emphasized. Althoughmotivated by oncology, the methods are applicable as well to population enrichment designs in other therapeutic areas. [ABSTRACT FROM AUTHOR]

Background: Adaptive designs are a wide class of methods focused on improving the power, efficiency and participant benefit of clinical trials. They do this through allowing information gathered during the trial to be used to make changes in a statistically robust manner - the changes could include which treatment arms patients are enrolled to (e.g. dropping non-promising treatment arms), the allocation ratios, the target sample size or the enrolment criteria of the trial. Generally, we are enthusiastic about adaptive designs and advocate their use in many clinical situations. However, they are not always advantageous. In some situations, they provide little efficiency advantage or are even detrimental to the quality of information provided by the trial. In our experience, factors that reduce the efficiency of adaptive designs are routinely downplayed or ignored in methodological papers, which may lead researchers into believing they are more beneficial than they actually are.Main Text: In this paper, we discuss situations where adaptive designs may not be as useful, including situations when the outcomes take a long time to observe, when dropping arms early may cause issues and when increased practical complexity eliminates theoretical efficiency gains.Conclusion: Adaptive designs often provide notable efficiency benefits. However, it is important for investigators to be aware that they do not always provide an advantage. There should always be careful consideration of the potential benefits and disadvantages of an adaptive design. [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]

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice. [ABSTRACT FROM AUTHOR]