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Background Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. Methods Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. Results The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. Conclusions We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

Background/aims This article describes the proposed design of a phase I study evaluating the safety of ceramide nanoliposome and vinblastine among an initial set of 19 possible dose combinations in patients with relapsed/refractory acute myeloid leukemia and patients with untreated acute myeloid leukemia who are not candidates for intensive induction chemotherapy. Methods Extensive collaboration between statisticians and clinical investigators revealed the need to incorporate several adaptive features into the design, including the flexibility of adding or eliminating certain dose combinations based on safety criteria applied to multiple dose pairs. During the design stage, additional dose levels of vinblastine were added, increasing the dimension of the drug combination space and thus the complexity of the problem. Increased complexity made application of existing drug combination dose-finding methods unsuitable in their current form. Results Our solution to these challenges was to adapt a method based on isotonic regression to meet the research objectives of the study. Application of this adapted method is described herein, and a simulation study of the design’s operating characteristics is conducted. Conclusion The aim of this article is to bring to light examples of novel design applications as a means of augmenting the implementation of innovative designs in the future and to demonstrate the flexibility of adaptive designs in satisfying changing design conditions.

Background/Aims: Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. Methods: This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. Results: With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. Conclusions: By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.

One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more pre-specified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED., 10 pages, 1 figures

In the era of precision medicine, it is of increasing interest to consider multiple strata (e.g. indications, regions, or subgroups) within a single oncology dose-finding study when identifying the maximum tolerated dose (MTD). We propose two Bayesian semi-parametric designs (BSD) for dose-finding with multiple strata to allow for both adaptively dosing patients based on various toxicity profiles and efficient identification of the MTD for each stratum. We develop non-parametric priors based on the Dirichlet process to allow for a flexible prior distribution and negate the need for a pre-specified exchangeability parameter. The two BSD models are built under different prior beliefs of strata heterogeneity and allow for appropriate borrowing of information across similar strata. Simulation studies are performed to evaluate the BSD model performance by comparing it with existing methods, including the fully stratified, exchangeability, and exchangeability-non-exchangeability models. In general, our BSD models outperform the competing methods in correctly identifying the MTD for different strata and necessitate a smaller sample size to determine the MTD. The BSD models are robust to various heterogeneity assumptions and can be easily extended to other binary and time to event endpoints.

IntroductionAdaptive designs offer a flexible approach, allowing changes to a trial based on examinations of the data as it progresses. Adaptive clinical trials are becoming a popular choice, as the prudent use of finite research budgets and accurate decision-making are priorities for healthcare providers around the world. The methods of health economics, which aim to maximise the health gained for money spent, could be incorporated into the design and analysis of adaptive clinical trials to make them more efficient. We aimed to understand the perspectives of stakeholders in health technology assessments to inform recommendations for the use of health economics in adaptive clinical trials.MethodsA qualitative study explored the attitudes of key stakeholders—including researchers, decision-makers and members of the public—towards the use of health economics in the design and analysis of adaptive clinical trials. Data were collected using interviews and focus groups (29 participants). A framework analysis was used to identify themes in the transcripts.ResultsIt was considered that answering the clinical research question should be the priority in a clinical trial, notwithstanding the importance of cost-effectiveness for decision-making. Concerns raised by participants included handling the volatile nature of cost data at interim analyses; implementing this approach in global trials; resourcing adaptive trials which are designed and adapted based on health economic outcomes; and training stakeholders in these methods so that they can be implemented and appropriately interpreted.ConclusionThe use of health economics in the design and analysis of adaptive clinical trials has the potential to increase the efficiency of health technology assessments worldwide. Recommendations are made concerning the development of methods allowing the use of health economics in adaptive clinical trials, and suggestions are given to facilitate their implementation in practice.

In adaptive two-group clinical trials, a current method is to perform a one-shot unblinded sample size reassessment. Whereas the interim unblinded look of the data is adjusted for inference by using the weighted Cui-Hung-Wang statistics, some questions remain: how and when to reassess the sample size?We define the Power Derivative Principle as follows: a sample size is optimal when the derivative of the power with respect to the sample size has reached some implicit value. Applied to two-group clinical trials, this Power Derivative Principle determines a new one-shot unblinded sample size reassessment rule (including the determination of futility bounds). A full Power Derivative Strategy induces furthermore an optimal information fraction for the interim analysis. The Power Derivative Strategy is then compared to adaptive design methods proposed in the literature and to group sequential strategies. For this comparison, we used, on the one hand, the very common information fraction f = 0.5 and, on the other hand, the information fraction found as being optimal with the Power Derivative Principle.The optimal information fraction depends only on α-and β-risks. For usual values of these risks, the optimal information fraction value is very close to 0.9. Moreover, with this unexpected optimal value, reassessment methods become roughly comparable (it is definitely not the case when f=0.5).Our results suggest that a sample size reassessment is more beneficial when considered close to the planned end of a trial, allowing a study with borderline interim results to be saved.

Funder: RCUK | MRC | Medical Research Foundation; doi: https://doi.org/10.13039/501100009187, Funder: NIHR Biomedical Research Centre BRC-1215-20014, BACKGROUND: Efficient trial designs are required to prioritise promising drugs within Phase II trials. Adaptive designs are examples of such designs, but their efficiency is reduced if there is a delay in assessing patient responses to treatment. METHODS: Motivated by the WIRE trial in renal cell carcinoma (NCT03741426), we compare three trial approaches to testing multiple treatment arms: (1) single-arm trials in sequence with interim analyses; (2) a parallel multi-arm multi-stage trial and (3) the design used in WIRE, which we call the Multi-Arm Sequential Trial with Efficient Recruitment (MASTER) design. The MASTER design recruits patients to one arm at a time, pausing recruitment to an arm when it has recruited the required number for an interim analysis. We conduct a simulation study to compare how long the three different trial designs take to evaluate a number of new treatment arms. RESULTS: The parallel multi-arm multi-stage and the MASTER design are much more efficient than separate trials. The MASTER design provides extra efficiency when there is endpoint delay, or recruitment is very quick. CONCLUSIONS: We recommend the MASTER design as an efficient way of testing multiple promising cancer treatments in non-comparative Phase II trials.

One of the primary purposes of an oncology dose-finding trial is to identify an optimal dose (OD) that is both tolerable and has an indication of therapeutic benefit for subjects in subsequent clinical trials. In addition, it is quite important to accelerate early stage trials to shorten the entire period of drug development. However, it is often challenging to make adaptive decisions of dose escalation and de-escalation in a timely manner because of the fast accrual rate, the difference of outcome evaluation periods for efficacy and toxicity and the late-onset outcomes. To solve these issues, we propose the time-to-event Bayesian optimal interval design to accelerate dose-finding based on cumulative and pending data of both efficacy and toxicity. The new design, named "TITE-BOIN-ET" design, is nonparametric and a model-assisted design. Thus, it is robust, much simpler, and easier to implement in actual oncology dose-finding trials compared with the model-based approaches. These characteristics are quite useful from a practical point of view. A simulation study shows that the TITE-BOIN-ET design has advantages compared with the model-based approaches in both the percentage of correct OD selection and the average number of patients allocated to the ODs across a variety of realistic settings. In addition, the TITE-BOIN-ET design significantly shortens the trial duration compared with the designs without sequential enrollment and therefore has the potential to accelerate early stage dose-finding trials.

Background Prolonged treatment with analgesic and sedative drugs in the pediatric intensive care unit (PICU) may lead to undesirable effects such as dependence and tolerance. Moreover, during analgosedation weaning, patients may develop clinical signs of withdrawal, known as withdrawal syndrome (WS). Some studies indicate that dexmedetomidine, a selective α2-adrenoceptor agonist, may be useful to prevent WS, but no clear evidence supports these data. The aims of the present study are to evaluate the efficacy of dexmedetomidine in reducing the occurrence of WS during analgosedation weaning, and to clearly assess its safety. Methods We will perform an adaptive, multicenter, randomized, double-blind, placebo-controlled trial. Patients aged N = 154 patients). The study was approved by the Ethics Committee of the University-Hospital S.Orsola-Malpighi of Bologna on 22 March 2017. Discussion The present trial will allow us to clearly assess the efficacy of dexmedetomidine in reducing the occurrence of WS during weaning from analgosedation drugs. In addition, the study will provide a unique insight into the safety profile of dexmedetomidine. Trial registration ClinicalTrials.gov, NCT03645603. Registered on 24 August 2018. EudraCT, 2015–002114-80. Retrospectively registered on 2 January 2019.

A two-stage enrichment design is a type of adaptive design, which extends a stratified design with a futility analysis on the marker negative cohort at the first stage, and the second stage can be either a targeted design with only the marker positive stratum, or still the stratified design with both marker strata, depending on the result of the interim futility analysis. In this paper we consider the situation where the marker assay and the classification rule are possibly subject to error. We derive the sequential tests for the global hypothesis as well as the component tests for the overall cohort and the marker-positive cohort. We discuss the power analysis with the control of the type-I error rate and show the adverse impact of the misclassification on the powers. We also show the enhanced power of the two-stage enrichment over the one-stage design, and illustrate with examples of the recent successful development of immunotherapy in non-small-cell lung cancer.

This paper proposes a two-stage phase I-II clinical trial design to optimize dose-schedule regimes of an experimental agent within ordered disease subgroups in terms of the toxicity-efficacy trade-off. The design is motivated by settings where prior biological information indicates it is certain that efficacy will improve with ordinal subgroup level. We formulate a flexible Bayesian hierarchical model to account for associations among subgroups and regimes, and to characterize ordered subgroup effects. Sequentially adaptive decision-making is complicated by the problem, arising from the motivating application, that efficacy is scored on day 90 and toxicity is evaluated within 30 days from the start of therapy, while the patient accrual rate is fast relative to these outcome evaluation intervals. To deal with this in a practical manner, we take a likelihood-based approach that treats unobserved toxicity and efficacy outcomes as missing values, and use elicited utilities that quantify the efficacy-toxicity trade-off as a decision criterion. Adaptive randomization is used to assign patients to regimes while accounting for subgroups, with randomization probabilities depending on the posterior predictive distributions of utilities. A simulation study is presented to evaluate the design's performance under a variety of scenarios, and to assess its sensitivity to the amount of missing data, the prior, and model misspecification.

Bayesian methods allow borrowing of historical information through prior distributions. The concept of prior effective sample size (prior ESS) facilitates quantification and communication of such prior information by equating it to a sample size. Prior information can arise from historical observations; thus, the traditional approach identifies the ESS with such a historical sample size. However, this measure is independent of newly observed data, and thus would not capture an actual "loss of information" induced by the prior in case of prior-data conflict. We build on a recent work to relate prior impact to the number of (virtual) samples from the current data model and introduce the effective current sample size (ECSS) of a prior, tailored to the application in Bayesian clinical trial designs. Special emphasis is put on robust mixture, power, and commensurate priors. We apply the approach to an adaptive design in which the number of recruited patients is adjusted depending on the effective sample size at an interim analysis. We argue that the ECSS is the appropriate measure in this case, as the aim is to save current (as opposed to historical) patients from recruitment. Furthermore, the ECSS can help overcome lack of consensus in the ESS assessment of mixture priors and can, more broadly, provide further insights into the impact of priors. An R package accompanies the paper.

We propose a novel response‐adaptive randomization procedure for multi‐armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non‐myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response‐adaptive algorithm based on the Gittins index for the multi‐armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969‐978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi‐armed setting, there are efficiency and patient benefit gains of using a response‐adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response‐adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi‐armed trial context.

It is desirable to work efficiently and cost effectively to evaluate new therapies in a time-sensitive and ethical manner without compromising the integrity and validity of the development process. The seamless phase II/III clinical trial has been proposed to meet this need, and its efficient, ethical and economic advantages can be strengthened by its combination with innovative response adaptive randomization (RAR) procedures. In particular, well-designed frequentist RAR procedures can target theoretically optimal allocation proportions, and there are explicit asymptotic results. However, there has been little research into seamless phase II/III clinical trials with frequentist RAR because of the difficulty in performing valid statistical inference and controlling the type I error rate. In this paper, we propose the framework for a family of frequentist RAR designs for seamless phase II/III trials, derive the asymptotic distribution of the parameter estimators using martingale processes and offer solutions to control the type I error rate. The numerical studies demonstrate our theoretical findings and the advantages of the proposed methods.

Determination of the optimal dose is a critical objective in the drug developmental process. An optimal dose prevents over- and under-exposure to the treatment drug thereby facilitating superior patient experience and reduced costs to the healthcare system. In this paper, we present a method for model-based dose optimisation with simultaneous pharmacokinetic estimation of the model parameters. Multiple doses of the drug are considered and the objective is to maintain the blood concentration of the drug around a pre-decided target concentration. We consider an adaptive setting wherein the model parameters are estimated from the blood samples collected at D-optimal time points from all subjects enrolled so far in the trial. The estimated parameters are then used to determine the optimal dose regimen for the next cohort. This procedure continues until the condition of a pre-decided stopping rule is met. Simulation studies and sensitivity analysis are undertaken to validate the methodology. We also evaluate the performance of the methodology when carried out in a non-adaptive setting. A two-stage design is then presented which combines the advantages of the adaptive as well as the non-adaptive approach. We demonstrate that our methodology enables pharmacokinetic estimation and dose regimen optimisation simultaneously in an ethical and cost-effective manner protecting the subjects from the ill-effects of suboptimal dose regimens and economising the number of subjects required in the trial.

We propose an adaptive enrichment approach to test an active factor, which is a factor whose effect is non-zero in at least one subpopulation. We implement a two-stage play-the-winner design where all subjects in the second stage are enrolled from the subpopulation that has the highest observed effect in the first stage. We recommend a weighted Fisher's combination of the most powerful test for each stage, respectively: the first stage Hotelling's test and the second stage noncentral chi-square test. The test is further extended to cover binary outcomes and time-to-event outcomes.

Single-arm trials with binary endpoint are firmly established in e.g., early clinical oncology. Here, two-stage designs are often employed to allow early termination of the trial in the case of an unexpectedly large or small response rate to the new treatment. Various designs have been proposed over the last few years which usually require strong assumptions about the true response rate during planning. Often, these designs are not robust to deviations from the planning assumptions. In this paper, we define a Bayesian framework for scoring two-stage designs under uncertainty and investigate the characteristics of designs optimizing a commonly employed performance score of Liu et al. The resulting optimal designs are compared with an alternative, utility-based approach incorporating expected power and sample size. We provide insights in the underlying implicit assumptions of using expected power for scoring adaptive designs and relate the global score function to the practice of sample size recalculation based on conditional power. An in-depth comparison of the features of the different performance scores and their respective optimizing designs provides the guidance for practitioners who face the problem of choosing between the various options. A software implementation of the proposed methods is publicly available online.

Response-adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data, so that a greater proportion of patients can be allocated to the better performing treatments. A major concern over the use of response-adaptive designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. In particular, we show that the naive z-test can have an inflated type I error rate even after applying a Bonferroni correction. Simulation studies have often been used to demonstrate error control, but do not provide a guarantee. In this paper, we present adaptive testing procedures for normally distributed outcomes that ensure strong familywise error control, by iteratively applying the conditional invariance principle. Our approach can be used for fully sequential and block randomized trials, and for a large class of adaptive randomization rules found in the literature. We show there is a high price to pay in terms of power to guarantee familywise error control for randomization schemes with extreme allocation probabilities. However, for proposed Bayesian adaptive randomization schemes in the literature, our adaptive tests maintain or increase the power of the trial compared to the z-test. We illustrate our method using a three-armed trial in primary hypercholesterolemia., 55 pages, 3 figures

Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception-participants' tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations-and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

Emerging infectious diseases such as Ebola Virus Disease (EVD), Nipah Virus Encephalitis and Lassa fever pose significant epidemic threats. Responses to emerging infectious disease outbreaks frequently occur in resource-constrained regions and under high pressure to quickly contain the outbreak prior to potential spread. As seen in the 2020 EVD outbreaks in the Democratic Republic of Congo and the current COVID-19 pandemic, there is a continued need to evaluate and address the ethical challenges that arise in the high stakes environment of an emerging infectious disease outbreak response. The research presented here provides analysis of the ethical challenges with regard to allocation of limited resources, particularly experimental therapeutics, using the 2013–2016 EVD outbreak in West Africa as a case study. In-depth semi-structured interviews were conducted with senior healthcare personnel (n = 16) from international humanitarian aid organizations intimately engaged in the 2013–2016 EVD outbreak response in West Africa. Interviews were recorded in private setting, transcribed, and iteratively coded using grounded theory methodology. A majority of respondents indicated a clear propensity to adopt an ethical framework of guiding principles for international responses to emerging infectious disease outbreaks. Respondents agreed that prioritization of frontline workers’ access to experimental therapeutics was warranted based on a principle of reciprocity. There was widespread acceptance of adaptive trial designs and greater trial transparency in providing access to experimental therapeutics. Many respondents also emphasized the importance of community engagement in limited resource allocation scheme design and culturally appropriate informed consent procedures. The study results inform a potential ethical framework of guiding principles based on the interview participants’ insights to be adopted by international response organizations and their healthcare workers in the face of allocating limited resources such as experimental therapeutics in future emerging infectious disease outbreaks to ease the moral burden of individual healthcare providers.

Global health pandemics, such as coronavirus disease 2019 (COVID-19), require efficient and well-conducted trials to determine effective interventions, such as treatments and vaccinations. Early work focused on rapid sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), subsequent in-vitro and in-silico work, along with greater understanding of the different clinical phases of the infection, have helped identify a catalogue of potential therapeutic agents requiring assessment. In a pandemic, there is a need to quickly identify efficacious treatments, and reject those that are non-beneficial or even harmful, using randomised clinical trials. Whilst each potential treatment could be investigated across multiple, separate, competing two-arm trials, this is a very inefficient process. Despite the very large numbers of interventional trials for COVID-19, the vast majority have not used efficient trial designs. Well conducted, adaptive platform trials utilising a multi-arm multi-stage (MAMS) approach provide a solution to overcome limitations of traditional designs. The multi-arm element allows multiple different treatments to be investigated simultaneously against a shared, standard-of-care control arm. The multi-stage element uses interim analyses to assess accumulating data from the trial and ensure that only treatments showing promise continue to recruitment during the next stage of the trial. The ability to test many treatments at once and drop insufficiently active interventions significantly speeds up the rate at which answers can be achieved. This article provides an overview of the benefits of MAMS designs and successes of trials, which have used this approach to COVID-19. We also discuss international collaboration between trial teams, including prospective agreement to synthesise trial results, and identify the most effective interventions. We believe that international collaboration will help provide faster answers for patients, clinicians, and health care systems around the world, including for each further wave of COVID-19, and enable preparedness for future global health pandemics.

Background Early reports indicate that COVID-19 may require intensive care unit (ICU) admission in 5–26% and overall mortality can rise to 11% of the recognised cases, particularly affecting the elderly. There is a lack of evidence-based targeted pharmacological therapy for its prevention and treatment. We aim to compare the effects of a World Health Organization recommendation-based education and a personalised complex preventive lifestyle intervention package (based on the same WHO recommendation) on the outcomes of the COVID-19. Methods PROACTIVE-19 is a pragmatic, randomised controlled clinical trial with adaptive “sample size re-estimation” design. Hungarian population over the age of 60 years without confirmed COVID-19 will be approached to participate in a telephone health assessment and lifestyle counselling voluntarily. Volunteers will be randomised into two groups: (A) general health education and (B) personalised health education. Participants will go through questioning and recommendation in 5 fields: (1) mental health, (2) smoking habits, (3) physical activity, (4) dietary habits, and (5) alcohol consumption. Both groups A and B will receive the same line of questioning to assess habits concerning these topics. Assessment will be done weekly during the first month, every second week in the second month, then monthly. The composite primary endpoint will include the rate of ICU admission, hospital admission (longer than 48 h), and mortality in COVID-19-positive cases. The estimated sample size is 3788 subjects per study arm. The planned duration of the follow-up is a minimum of 1 year. Discussion These interventions may boost the body’s cardiovascular and pulmonary reserve capacities, leading to improved resistance against the damage caused by COVID-19. Consequently, lifestyle changes can reduce the incidence of life-threatening conditions and attenuate the detrimental effects of the pandemic seriously affecting the older population. Trial registration The study has been approved by the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (IV/2428- 2 /2020/EKU) and has been registered at clinicaltrials.gov (NCT04321928) on 25 March 2020.

International audience; Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.

Goldilocks Design (GD) utilizes predictive probability to adaptively select a trial's sample size based on accumulating data. In order to control type I error at a desired level for a subset of the null space, extensive simulations at the study design stage are required to choose critical values, which is a challenge for this type of Bayesian adaptive design to be used for confirmatory trials. In this article, we propose a Modified Goldilocks Design (MGD) where type I error is analytically controlled over the entire null space. We do so by applying the conditional invariance principle and a combination test approach on [Formula: see text]-values that are obtained from independent cohorts of subjects. Simulation studies show that despite analytic control of type I error rate, the proposed MGD has similar power when compared with the original GD. We further apply it to an example trial with time-to-event endpoint in oncology.

We propose a novel response-adaptive randomization procedure for multi-armed trials with continuous outcomes that are assumed to be normally distributed. Our proposed rule is non-myopic, and oriented toward a patient benefit objective, yet maintains computational feasibility. We derive our response-adaptive algorithm based on the Gittins index for the multi-armed bandit problem, as a modification of the method first introduced in Villar et al. (Biometrics, 71, pp. 969-978). The resulting procedure can be implemented under the assumption of both known or unknown variance. We illustrate the proposed procedure by simulations in the context of phase II cancer trials. Our results show that, in a multi-armed setting, there are efficiency and patient benefit gains of using a response-adaptive allocation procedure with a continuous endpoint instead of a binary one. These gains persist even if an anticipated low rate of missing data due to deaths, dropouts, or complete responses is imputed online through a procedure first introduced in this paper. Additionally, we discuss how there are response-adaptive designs that outperform the traditional equal randomized design both in terms of efficiency and patient benefit measures in the multi-armed trial context.

Objective: We investigate the impact of biomarker assay’s accuracy on the operating characteristics of a Bayesian biomarker-driven outcome-adaptive randomization design. Methods: In a simulation study, we assume a trial with two treatments, two biomarker-based strata, and a binary clinical outcome (response). P bt denotes the probability of response for treatment t ( t = 0 or 1) in biomarker stratum ( b = 0 or 1). Four different scenarios in terms of true underlying response probabilities are considered: a null ( P00 = P01 = 0.25, P10 = P11= 0.25) and consistent ( P00 = P10 = 0.25, P01 = 0.5) treatment effect scenario, as well as a quantitative ( P00 = P01 = P10 = 0.25, P11 = 0.5) and a qualitative ( P00 = P11 = 0.5, P01 = P10 = 0.25) stratum-treatment interaction. For each scenario, we compare the case of a perfect with the case of an imperfect biomarker assay with sensitivity and specificity of 0.8 and 0.7, respectively. In addition, biomarker-positive prevalence values P( B = 1) = 0.2 and 0.5 are investigated. Results: Results show that the use of an imperfect assay affects the operational characteristics of the Bayesian biomarker-based outcome-adaptive randomization design. In particular, the misclassification causes a substantial reduction in power accompanied by a considerable increase in the type-I error probability. The magnitude of these effects depends on the sensitivity and specificity of the assay, as well as on the distribution of the biomarker in the patient population. Conclusion: With an imperfect biomarker assay, the decision to apply a biomarker-based outcome-adaptive randomization design may require careful reflection.

A series of recent US Food and Drug Administration (FDA) approvals (such as Sarepta's Exondys 51, Merck's Keytruda, and Portola's Bevyxxa) has generated significant interest within the drug development ecosystem. Facilitated regulatory pathways aimed toward expediting medicines to patients suffering from serious and life-threatening conditions are a good thing, even if it raises curiosity and introduces some degree of uncertainty. Over the last 20 years, two key words in drug development have been speed and innovation. Going forward, the patient voice, data quality, and evidence generation must be added to that list. There is a raging debate over the level of evidence expected to first introduce a treatment to patients. Some argue for less data followed by postapproval follow-up, others for more adaptive clinical trial designs and end-point modification driven by patient-focused drug development and use of real-world evidence. The transition in the regulatory framework is happening in front of our eyes. How are these shifts in regulatory science interpreted within the context of 21st-century drug development-and how can these learnings help advance patient care while placing into context the expected uncertainty we find in benefit-risk data?

Background/aims: The increasing cost of the drug development process has seen interest in the use of adaptive trial designs grow substantially. Accordingly, much research has been conducted to identify barriers to increasing the use of adaptive designs in practice. Several articles have argued that the availability of user-friendly software will be an important step in making adaptive designs easier to implement. Therefore, we present a review of the current state of software availability for adaptive trial design. Methods: We review articles from 31 journals published in 2013–2017 that relate to methodology for adaptive trials to assess how often code and software for implementing novel adaptive designs is made available at the time of publication. We contrast our findings against these journals’ policies on code distribution. We also search popular code repositories, such as Comprehensive R Archive Network and GitHub, to identify further existing user-contributed software for adaptive designs. From this, we are able to direct interested parties toward solutions for their problem of interest. Results: Only 30% of included articles made their code available in some form. In many instances, articles published in journals that had mandatory requirements on code provision still did not make code available. There are several areas in which available software is currently limited or saturated. In particular, many packages are available to address group sequential design, but comparatively little code is present in the public domain to determine biomarker-guided adaptive designs. Conclusions: There is much room for improvement in the provision of software alongside adaptive design publications. In addition, while progress has been made, well-established software for various types of trial adaptation remains sparsely available.

With ten years of follow-up since the advent of the modern paradigm of combination induction therapy, consolidative autologous stem-cell transplant, and lenalidomide maintenance, median survival for multiple myeloma has increased to almost 50% at 10 years. Given this outlook, the overarching goal of maintenance therapy is to spare patients from the toxicities of aggressive or otherwise intrusive therapies while ideally extending survival or, at the least, extending progression-free survival or time until next treatment. This review will focus on the current landscape of maintenance therapies for multiple myeloma. The historical context and evidence for choice of agent, duration of treatment, and current strategies and ongoing trials will be discussed - as well as a focus on unmet needs. The case for studies investigating cessation of therapy and risk and response-adapted approaches will be underscored given that the current paradigm likely results in overtreatment for some patients.

The US Food and Drug Administration has several regulatory programs and pathways to expedite the development and approval of therapeutic agents aimed at treating serious or life-debilitating conditions. A common feature of these programs is the regulatory flexibility, which allows for a customized approval approach that enables market authorization on the basis of less rigorous evidence, in exchange for requiring postmarket evidence generation. An increasing share of therapeutic agents approved by the Food and Drug Administration in recent years are associated with expedited programs. In this article, we provide an overview of the evidentiary standards required by the Food and Drug Administration’s expedited development and review programs, summarize the findings of the recent academic literature demonstrating some of the limitations of these programs, and outline potential opportunities to address these limitations. Recent evidence suggests that therapeutic agents in the Food and Drug Administration’s expedited programs are approved on the basis of fewer and smaller studies that may lack comparator groups and random allocation, and rather than focusing on clinical outcomes for study endpoints, rely instead on surrogate markers of disease. Once on the market, agents receiving expedited approvals are often quickly incorporated into clinical practice, and evidence generated in the postmarket period may not necessarily address the evidentiary limitations at the time of market entry. Furthermore, not all pathways require additional postmarket studies. Evidence suggests that drugs in expedited approval programs are associated with a greater likelihood that the Food and Drug Administration will take a safety action following market entry. There are several opportunities to improve the timeliness, information value, and validity of the pre- and postmarket studies of therapeutic agents receiving expedited approvals. When use of nonrandomized and uncontrolled studies cannot be avoided prior to market entry, randomized trials should be mandatory in the postmarket period, unless there are strong justifications for not carrying out such studies. In the premarket period, validity of the surrogate markers can be improved by more rigorously evaluating their correlation with patient-relevant clinical outcomes. Opportunities to reduce the duration, complexity, and cost of postmarket randomized trials should not compromise their validity and instead incorporate pragmatic “real-world” design elements. Despite recent enthusiasm for widely using real-world evidence, adaptive designs, and pragmatic trials in the regulatory setting, caution is warranted until large-scale empirical evaluations demonstrate their validity compared to more traditional trial designs.

A two treatment response adaptive design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.