Search

Background: Pulmonary arterial hypertension is a life-threatening progressive disorder characterised by high blood pressure (hypertension) in the arteries of the lungs (pulmonary artery). Although treatable, there is no known cure for this rare disorder, and its exact cause is unknown. Mutations in the bone morphogenetic protein receptor type-2 (BMPR2) are the most common genetic cause of familial pulmonary arterial hypertension. This study represents the first-ever trial of treatments aimed at directly rescuing the BMPR2 pathway, repurposing two drugs that have shown promise at restoring levels of BMPR2 signalling: hydroxychloroquine and phenylbutyrate., Methods: This three-armed phase II precision medicine study will investigate BMPR2 target engagement and explore the efficacy of two repurposed therapies in pulmonary arterial hypertension patients with BMPR2 mutations. Patients will be stratified based on two BMPR2 mutation classes: missense and haploinsufficient mutations. Eligible subjects will be randomised to one of the three arms (two active therapy arms and a placebo arm, all plus standard of care) following a Bayesian response-adaptive design implemented independently in each stratum and updated in response to a novel panel of primary biomarkers designed to assess biological modification of the disease., Discussion: The results of this trial will provide the first randomised evidence of the efficacy of these therapies to rescue BMPR2 function and will efficiently explore the potential for a differential response of these therapies per mutation class to address causes rather than consequences of this rare disease., Trial Registration: The study has been registered with ISRCTN (ISRCTN10304915, 22/09/2023)., (© 2024. The Author(s).)

The use of digital health technologies to measure outcomes in clinical trials opens new opportunities as well as methodological challenges. Digital outcome measures may provide more sensitive and higher-frequency measurements but pose vital statistical challenges around how such outcomes should be defined and validated and how trials incorporating digital outcome measures should be designed and analysed. This article presents eight methodological questions, exploring issues such as the length of measurement period, choice of summary statistic and definition and handling of missing data as well as the potential for new estimands and new analyses to leverage the time series data from digital devices. The impact of key issues highlighted by the eight questions on a primary analysis of a trial are illustrated through a simulation study based on the 2019 Bellerophon INOPulse trial which had time spent in MVPA as a digital outcome measure. These eight questions present broad areas where methodological guidance is needed to enable wider uptake of digital outcome measures in trials., (© 2024. The Author(s).)

Background: Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people's chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person's health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem., Methods: A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day's discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination., Results: Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event., Conclusions: The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software., (© 2024. The Author(s).)

Traditional approaches in dose-finding trials, such as the continual reassessment method, focus on identifying the maximum tolerated dose. In contemporary early-phase dose-finding trials, especially in oncology with targeted agents or immunotherapy, a more relevant aim is to identify the lowest dose level that maximises efficacy whilst remaining tolerable. Backfilling, defined as the practice of assigning patients to dose levels lower than the current highest tolerated dose, has been proposed to gather additional pharmacokinetic, pharmacodynamic and biomarker data to recommend the most appropriate dose to carry forward for subsequent studies. The first formal framework [5] for backfilling proposed randomising backfill patients with equal probability among those doses below the dose level where the study is currently at. Here, we propose to use Bayesian response-adaptive randomisation to backfill patients. This patient-oriented approach to backfilling aims to allocate more patients to dose levels in the backfill set with higher expected efficacy based on emerging data. The backfill set constitutes of the doses below the dose the dose-finding algorithm is at. At study completion, collective patient data inform the dose-response curve, suggesting an optimal dose level balancing toxicity and efficacy. Our simulation study across diverse clinical trial settings demonstrates that a backfilling strategy using Bayesian response-adaptive randomisation can result in a patient-oriented patient assignment procedure which simultaneously enhances the likelihood of correctly identifying the most appropriate dose level. This contribution offers a methodological framework and practical implementation for patient-oriented backfilling, encompassing design and analysis considerations in early-phase trials., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: SSV declares being part of the advisory board of PhaseV, which should not be a conflict of interest for our work but is reported for transparency purposes. The authors have no other conflict of interest to report., (Copyright © 2024. Published by Elsevier Inc.)

A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID-19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimizing cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analyzing this with a single diagnostic test) has been suggested. In this article, we present results from an extensive and realistic simulation study comparing testing policies based on individually testing subjects with symptoms (a policy resembling the UK strategy at the start of the COVID-19 pandemic), individually testing subjects at random or pools of subjects randomly combined and tested. To compare these testing methods, a dynamic model compromised of a relationship network and an extended SEIR model is used. In contrast to most existing literature, testing capacity is considered as fixed and limited rather than unbounded. This article then explores the impact of the proportion of symptomatic infections on the expected performance of testing policies. Symptomatic testing performs better than pooled testing unless a low proportion of infections are symptomatic. Additionally, we include the novel feature for testing of non-compliance and perform a sensitivity analysis for different compliance assumptions. Our results suggest for the pooled testing scheme to be superior to testing symptomatic people individually, only a small proportion of the population ( > 10 % $$ >10\% $$ ) needs to not comply with the testing procedure., (© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Background: There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing)., Methods: Two different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a 'typical' English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to 'normal life' without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the 'ward' testing scheme only healthcare workers who work on a single ward are eligible and in the 'full' testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers., Results: Including a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2[Formula: see text] in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests., Conclusions: Whether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown., (© 2023. The Author(s).)

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

The most common objective for response-adaptive clinical trials is to seek to ensure that patients within a trial have a high chance of receiving the best treatment available by altering the chance of allocation on the basis of accumulating data. Approaches that yield good patient benefit properties suffer from low power from a frequentist perspective when testing for a treatment difference at the end of the study due to the high imbalance in treatment allocations. In this work we develop an alternative pairwise test for treatment difference on the basis of allocation probabilities of the covariate-adjusted response-adaptive randomization with forward-looking Gittins Index (CARA-FLGI) Rule for binary responses. The performance of the novel test is evaluated in simulations for two-armed studies and then its applications to multiarmed studies are illustrated. The proposed test has markedly improved power over the traditional Fisher exact test when this class of nonmyopic response adaptation is used. We also find that the test's power is close to the power of a Fisher exact test under equal randomization., (© 2021 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.)

When comparing the performance of multi-armed bandit algorithms, the potential impact of missing data is often overlooked. In practice, it also affects their implementation where the simplest approach to overcome this is to continue to sample according to the original bandit algorithm, ignoring missing outcomes. We investigate the impact on performance of this approach to deal with missing data for several bandit algorithms through an extensive simulation study assuming the rewards are missing at random. We focus on two-armed bandit algorithms with binary outcomes in the context of patient allocation for clinical trials with relatively small sample sizes. However, our results apply to other applications of bandit algorithms where missing data is expected to occur. We assess the resulting operating characteristics, including the expected reward. Different probabilities of missingness in both arms are considered. The key finding of our work is that when using the simplest strategy of ignoring missing data, the impact on the expected performance of multi-armed bandit strategies varies according to the way these strategies balance the exploration-exploitation trade-off. Algorithms that are geared towards exploration continue to assign samples to the arm with more missing responses (which being perceived as the arm with less observed information is deemed more appealing by the algorithm than it would otherwise be). In contrast, algorithms that are geared towards exploitation would rapidly assign a high value to samples from the arms with a current high mean irrespective of the level observations per arm. Furthermore, for algorithms focusing more on exploration, we illustrate that the problem of missing responses can be alleviated using a simple mean imputation approach., Competing Interests: The authors have declared that no competing interests exist.

Background: The NOTACS trial will assess the efficacy, safety and cost-effectiveness of high-flow nasal therapy (HFNT) compared to standard oxygen therapy (SOT) on the outcomes of patients after cardiac surgery., Methods/design: NOTACS is an adaptive, international, multicentre, parallel-group, randomised controlled trial, with a pre-planned interim sample size re-estimation (SSR). A minimum of 850 patients will be randomised 1:1 to receive either HFNT or SOT. The primary outcome is days alive and at home in the first 90 days after the planned surgery (DAH90), with a number of secondary analyses and cost-effectiveness analyses also planned. The interim SSR will take place after a minimum of 300 patients have been followed up for 90 days and will allow for the sample size to increase up to a maximum of 1152 patients., Results: This manuscript provides detailed descriptions of the design of the NOTACS trial, and the analyses to be undertaken at the interim and final analyses. The main purpose of the interim analysis is to assess safety and to perform a sample size re-estimation. The main purpose of the final analysis is to examine the safety, efficacy and cost-effectiveness of HFNT compared to SOT on the outcomes of patients after cardiac surgery., Discussion: This manuscript outlines the key features of the NOTACS statistical analysis plan and was submitted to the journal before the interim analysis in order to preserve scientific integrity under an adaptive design framework. The NOTACS SAP closely follows published guidelines for the content of SAPs in clinical trials., Trial Registration: ISRCTN14092678 . Registered on 13 May 2020., (© 2022. The Author(s).)

Adaptive designs are a class of methods for improving efficiency and patient benefit of clinical trials. Although their use has increased in recent years, research suggests they are not used in many situations where they have potential to bring benefit. One barrier to their more widespread use is a lack of understanding about how the choice to use an adaptive design, rather than a traditional design, affects resources (staff and non-staff) required to set-up, conduct and report a trial. The Costing Adaptive Trials project investigated this issue using quantitative and qualitative research amongst UK Clinical Trials Units. Here, we present guidance that is informed by our research, on considering the appropriate resourcing of adaptive trials. We outline a five-step process to estimate the resources required and provide an accompanying costing tool. The process involves understanding the tasks required to undertake a trial, and how the adaptive design affects them. We identify barriers in the publicly funded landscape and provide recommendations to trial funders that would address them. Although our guidance and recommendations are most relevant to UK non-commercial trials, many aspects are relevant more widely., (© 2022. The Author(s).)

Background: Stepped-wedge cluster randomized trial (SW-CRT) designs are often used when there is a desire to provide an intervention to all enrolled clusters, because of a belief that it will be effective. However, given there should be equipoise at trial commencement, there has been discussion around whether a pre-trial decision to provide the intervention to all clusters is appropriate. In pharmaceutical drug development, a solution to a similar desire to provide more patients with an effective treatment is to use a response adaptive (RA) design., Methods: We introduce a way in which RA design could be incorporated in an SW-CRT, permitting modification of the intervention allocation during the trial. The proposed framework explicitly permits a balance to be sought between power and patient benefit considerations. A simulation study evaluates the methodology., Results: In one scenario, for one particular RA design, the proportion of cluster-periods spent in the intervention condition was observed to increase from 32.2% to 67.9% as the intervention effect was increased. A cost of this was a 6.2% power drop compared to a design that maximized power by fixing the proportion of time in the intervention condition at 45.0%, regardless of the intervention effect., Conclusions: An RA approach may be most applicable to settings for which the intervention has substantial individual or societal benefit considerations, potentially in combination with notable safety concerns. In such a setting, the proposed methodology may routinely provide the desired adaptability of the roll-out speed, with only a small cost to the study's power., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

Clinical trials of a vaccine during an epidemic face particular challenges, such as the pressure to identify an effective vaccine quickly to control the epidemic, and the effect that time-space-varying infection incidence has on the power of a trial. We illustrate how the operating characteristics of different trial design elements maybe evaluated using a network epidemic and trial simulation model, based on COVID-19 and individually randomized two-arm trials with a binary outcome. We show that "ring" recruitment strategies, prioritizing participants at an imminent risk of infection, can result in substantial improvement in terms of power in the model we present. In addition, we introduce a novel method to make more efficient use of the data from the earliest cases of infection observed in the trial, whose infection may have been too early to be vaccine-preventable. Finally, we compare several methods of response-adaptive randomization (RAR), discussing their advantages and disadvantages in the context of our model and identifying particular adaptation strategies that preserve power and estimation properties, while slightly reducing the number of infections, given an effective vaccine.

Background: Adaptive designs offer great promise in improving the efficiency and patient-benefit of clinical trials. An important barrier to further increased use is a lack of understanding about which additional resources are required to conduct a high-quality adaptive clinical trial, compared to a traditional fixed design. The Costing Adaptive Trials (CAT) project investigated which additional resources may be required to support adaptive trials., Methods: We conducted a mock costing exercise amongst seven Clinical Trials Units (CTUs) in the UK. Five scenarios were developed, derived from funded clinical trials, where a non-adaptive version and an adaptive version were described. Each scenario represented a different type of adaptive design. CTU staff were asked to provide the costs and staff time they estimated would be needed to support the trial, categorised into specified areas (e.g. statistics, data management, trial management). This was calculated separately for the non-adaptive and adaptive version of the trial, allowing paired comparisons. Interviews with 10 CTU staff who had completed the costing exercise were conducted by qualitative researchers to explore reasons for similarities and differences., Results: Estimated resources associated with conducting an adaptive trial were always (moderately) higher than for the non-adaptive equivalent. The median increase was between 2 and 4% for all scenarios, except for sample size re-estimation which was 26.5% (as the adaptive design could lead to a lengthened study period). The highest increase was for statistical staff, with lower increases for data management and trial management staff. The percentage increase in resources varied across different CTUs. The interviews identified possible explanations for differences, including (1) experience in adaptive trials, (2) the complexity of the non-adaptive and adaptive design, and (3) the extent of non-trial specific core infrastructure funding the CTU had., Conclusions: This work sheds light on additional resources required to adequately support a high-quality adaptive trial. The percentage increase in costs for supporting an adaptive trial was generally modest and should not be a barrier to adaptive designs being cost-effective to use in practice. Informed by the results of this research, guidance for investigators and funders will be developed on appropriately resourcing adaptive trials., (© 2021. The Author(s).)

Pulmonary arterial hypertension is an unmet clinical need. Imatinib, a tyrosine kinase inhibitor, 200 to 400 mg daily reduces pulmonary artery pressure and increases functional capacity in this patient group, but is generally poorly tolerated at the higher dose. We have designed an open-label, single-arm clinical study to investigate whether there is a tolerated dose of imatinib that can be better targeted to patients who will benefit. The study consists of two parts. Part 1 seeks to identify the best tolerated dose of Imatinib in the range from 100 and up to 400 mg using a Bayesian Continuous Reassessment Method. Part 2 will measure efficacy after 24 weeks treatment with the best tolerated dose using a Simon's two-stage design. The primary efficacy endpoint is a binary variable. For patients with a baseline pulmonary vascular resistance (PVR) >1000 dynes · s · cm -5 , success is defined by an absolute reduction in PVR of ≥300 dynes · s · cm -5 at 24 weeks. For patients with a baseline PVR ≤1000 dynes · s · cm -5 , success is a 30% reduction in PVR at 24 weeks. PVR will also be evaluated as a continuous variable by genotype as an exploratory analysis. Evaluating the response to that dose by genotype may inform a prospective biomarker-driven study., (© The Author(s) 2021.)

Lay Abstract: Autistic children who speak few or no words or who have an intellectual disability are the most in need of new understandings and treatments, but the most often left out of the research that can bring these benefits. Researchers perceive difficulties around compliance with instructions, testing, challenging behaviours and family stress. Although research with these children can indeed be difficult, their continuing exclusion is unethical and unacceptable. Drawing on our experiences testing a possible treatment for children with profound autism, we provide 10 practical guidelines related to (1) interacting physically, (2) combining play and testing, (3) responding to challenging behaviour, (4) finding suitable tests, (5) relationships with parents, (6) relationships with siblings, (7) involving stakeholders, (8) planning the testing times, (9) the role of the clinical supervisor and (10) recruiting and retaining participants. We hope that these guidelines will prepare and embolden other research teams to work with profoundly autistic children, ending their historical exclusion from research. These guidelines also could be useful for conducting research with children with intellectual disabilities.

Background: The dose of protamine required following cardiopulmonary bypass (CPB) is often determined by the dose of heparin required pre-CPB, expressed as a fixed ratio. Dosing based on mathematical models of heparin clearance is postulated to improve protamine dosing precision and coagulation. We hypothesised that protamine dosing based on a 2-compartment model would improve thromboelastography (TEG) parameters and reduce the dose of protamine administered, relative to a fixed ratio., Methods and Findings: We undertook a 2-stage, adaptive randomised controlled trial, allocating 228 participants to receive protamine dosed according to a mathematical model of heparin clearance or a fixed ratio of 1 mg of protamine for every 100 IU of heparin required to establish anticoagulation pre-CPB. A planned, blinded interim analysis was undertaken after the recruitment of 50% of the study cohort. Following this, the randomisation ratio was adapted from 1:1 to 1:1.33 to increase recruitment to the superior arm while maintaining study power. At the conclusion of trial recruitment, we had randomised 121 patients to the intervention arm and 107 patients to the control arm. The primary endpoint was kaolin TEG r-time measured 3 minutes after protamine administration at the end of CPB. Secondary endpoints included ratio of kaolin TEG r-time pre-CPB to the same metric following protamine administration, requirement for allogeneic red cell transfusion, intercostal catheter drainage at 4 hours postoperatively, and the requirement for reoperation due to bleeding. The trial was listed on a clinical trial registry (ClinicalTrials.gov Identifier: NCT03532594). Participants were recruited between April 2018 and August 2019. Those in the intervention/model group had a shorter mean kaolin r-time (6.58 [SD 2.50] vs. 8.08 [SD 3.98] minutes; p = 0.0016) post-CPB. The post-protamine thromboelastogram of the model group was closer to pre-CPB parameters (median pre-CPB to post-protamine kaolin r-time ratio 0.96 [IQR 0.78-1.14] vs. 0.75 [IQR 0.57-0.99]; p < 0.001). We found no evidence of a difference in median mediastinal/pleural drainage at 4 hours postoperatively (140 [IQR 75-245] vs. 135 [IQR 94-222] mL; p = 0.85) or requirement (as a binary outcome) for packed red blood cell transfusion at 24 hours postoperatively (19 [15.8%] vs. 14 [13.1%] p = 0.69). Those in the model group had a lower median protamine dose (180 [IQR 160-210] vs. 280 [IQR 250-300] mg; p < 0.001). Important limitations of this study include an unblinded design and lack of generalisability to certain populations deliberately excluded from the study (specifically children, patients with a total body weight >120 kg, and patients requiring therapeutic hypothermia to <28°C)., Conclusions: Using a mathematical model to guide protamine dosing in patients following CPB improved TEG r-time and reduced the dose administered relative to a fixed ratio. No differences were detected in postoperative mediastinal/pleural drainage or red blood cell transfusion requirement in our cohort of low-risk patients., Trial Registration: ClinicalTrials.gov Unique identifier NCT03532594., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: FF has received speaking fees and research support from Abbott Laboratories and LivaNova. He is a member of the Scientific Advisory Committees for Abbott Point-of-Care, Werfen and LivaNova. The other authors have declared that no competing interests exist.

Aims: International early warning scores (EWS) including the additive National Early Warning Score (NEWS) and logistic EWS currently utilise physiological snapshots to predict clinical deterioration. We hypothesised that a dynamic score including vital sign trajectory would improve discriminatory power., Methods: Multicentre retrospective analysis of electronic health record data from postoperative patients admitted to cardiac surgical wards in four UK hospitals. Least absolute shrinkage and selection operator-type regression (LASSO) was used to develop a dynamic model (DyniEWS) to predict a composite adverse event of cardiac arrest, unplanned intensive care re-admission or in-hospital death within 24 h., Results: A total of 13,319 postoperative adult cardiac patients contributed 442,461 observations of which 4234 (0.96%) adverse events in 24 h were recorded. The new dynamic model (AUC = 0.80 [95% CI 0.78-0.83], AUPRC = 0.12 [0.10-0.14]) outperforms both an updated snapshot logistic model (AUC = 0.76 [0.73-0.79], AUPRC = 0.08 [0.60-0.10]) and the additive National Early Warning Score (AUC = 0.73 [0.70-0.76], AUPRC = 0.05 [0.02-0.08]). Controlling for the false alarm rates to be at current levels using NEWS cut-offs of 5 and 7, DyniEWS delivers a 7% improvement in balanced accuracy and increased sensitivities from 41% to 54% at NEWS 5 and 18% to -30% at NEWS 7., Conclusions: Using an advanced statistical approach, we created a model that can detect dynamic changes in risk of unplanned readmission to intensive care, cardiac arrest or in-hospital mortality and can be used in real time to risk-prioritise clinical workload., (Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)

Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

Background: Extracorporeal membrane oxygenation (ECMO) is increasingly used to support adults with severe respiratory failure refractory to conventional measures. In 2011, NHS England commissioned a national service to provide ECMO to adults with refractory acute respiratory failure. Our aims were to characterise the patients admitted to the service, report their outcomes, and highlight characteristics potentially associated with survival., Methods: An observational cohort study was conducted of all patients treated by the NHS England commissioned ECMO service between December 1, 2011 and December 31, 2017. Analysis was conducted according to a prespecified protocol (NCT: 03979222). Data are presented as median [inter-quartile range, IQR]., Results: A total of 1205 patients were supported with ECMO during the study period; the majority (n=1150; 95%) had veno-venous ECMO alone. The survival rate at ECMO ICU discharge was 74% (n=887). Survivors had a lower median age (43 yr [32-52]), compared with non-survivors (49 y [39-60]). Increased severity of hypoxaemia at time of decision-to-cannulate was associated with a lower probability of survival: survivors had a median Sao 2 of 90% (84-93%; median Pao 2 /Fio 2 , 9.4 kPa [7.7-12.6]), compared with non-survivors (Sao 2 88% [80-92%]; Pao 2 /Fio 2 ratio: 8.5 kPa [7.1-11.5]). Patients requiring ECMO because of asthma were more likely to survive (95% survival rate (95% CI, 91-99%), compared with a survival of 71% (95% CI, 69-74%) in patients with respiratory failure attributable to other diagnoses., Conclusion: A national ECMO service can achieve good short-term outcomes for patients with undifferentiated respiratory failure refractory to conventional management., Clinical Trial Registration: NCT03979222., (Copyright © 2020 British Journal of Anaesthesia. All rights reserved.)

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., (© 2019 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

NHS England recently mandated that the National Early Warning Score of vital signs be used in all acute hospital trusts in the UK despite limited validation in the postoperative setting. We undertook a multicentre UK study of 13,631 patients discharged from intensive care after risk-stratified cardiac surgery in four centres, all of which used VitalPAC TM to electronically collect postoperative National Early Warning Score vital signs. We analysed 540,127 sets of vital signs to generate a logistic score, the discrimination of which we compared with the national additive score for the composite outcome of: in-hospital death; cardiac arrest; or unplanned intensive care admission. There were 578 patients (4.2%) with an outcome that followed 4300 sets of observations (0.8%) in the preceding 24 h: 499 out of 578 (86%) patients had unplanned re-admissions to intensive care. Discrimination by the logistic score was significantly better than the additive score. Respective areas (95%CI) under the receiver-operating characteristic curve with 24-h and 6-h vital signs were: 0.779 (0.771-0.786) vs. 0.754 (0.746-0.761), p < 0.001; and 0.841 (0.829-0.853) vs. 0.813 (0.800-0.825), p < 0.001, respectively. Our proposed logistic Early Warning Score was better than the current National Early Warning Score at discriminating patients who had an event after cardiac surgery from those who did not., (© 2019 Crown copyright. Anaesthesia © 2019 Association of Anaesthetists.)

Background: Point OutWords is a caregiver-delivered, iPad-assisted intervention for non-verbal or minimally verbal children with autism. It aims to develop prerequisite skills for communication such as manual and oral motor skills, sequencing, and symbolic representation. This feasibility trial aims to determine the viability of evaluating the clinical efficacy of Point OutWords., Methodology: We aim to recruit 46 non-verbal or minimally verbal children with autism and their families, approximately 23 per arm. Children in the intervention group will use Point OutWords for half an hour, five times a week, for 8 weeks. Children in the control group will have equal caregiver-led contact time with the iPad using a selection of control apps (e.g. sensory apps, drawing apps). Communication, motor, and daily living skills are assessed at baseline and post-intervention. Parents will keep diaries during the intervention period and will take part in focus groups when the intervention is completed., Discussion: Point OutWords was developed in collaboration with children with autism and their caregivers, to provide an intervention for a subgroup of autism that has been historically underserved. As autism is a heterogeneous condition, it is unlikely that one style of intervention will address all aspects of its symptomatology; the motor skills approach of Point OutWords can complement other therapies that address core autistic symptoms of social cognition and communication more directly. The current feasibility trial can inform the selection of outcome measures and design for future full-scale randomised controlled trials of Point OutWords and of other early interventions in autism., Trial Registration: ISRCTN, ISRCTN12808402. Prospectively registered on 12 March 2019.

Background: Chronic thromboembolic pulmonary hypertension (CTEPH) is an uncommon complication of acute pulmonary emboli necessitating lifelong anticoagulation. Despite this, few data exist on the safety and efficacy of vitamin K antagonists (VKAs) in CTEPH and none for direct oral anticoagulants (DOACs)., Objectives: To evaluate outcomes and complication rates in CTEPH following pulmonary endarterectomy (PEA) for individuals receiving VKAs or DOACs., Methods: Consecutive CTEPH patients undergoing PEA between 2007 and 2018 were included in a retrospective analysis. Postoperative outcomes, recurrent venous thromboembolism (VTE), and bleeding events were obtained from patient medical records., Results: Seven hundred ninety-four individuals were treated with VKAs and 206 with DOACs following PEA. Mean observation period was 612 (standard deviation: 702) days. Significant improvements in hemodynamics and functional status were observed in both groups following PEA (P < .001). Major bleeding events were equivalent (P = 1) in those treated with VKAs (0.67%/person-year) and DOACs (0.68%/person-year). The VTE recurrence was proportionately higher (P = .008) with DOACs (4.62%/person-year) than VKAs (0.76%/person-year), although survival did not differ., Conclusions: Post-PEA functional and hemodynamic outcomes appear unaffected by anticoagulant choice. Bleeding events were similar, but recurrent VTE rates significantly higher in those receiving DOACs. Our study provides a strong rationale for prospective registry data and/or studies to evaluate the safety of DOACs in CTEPH., (© 2019 International Society on Thrombosis and Haemostasis.)

Objectives: Because the mortality rate is very low in thoracic surgery, its use as a quality discriminator is limited. Acute kidney injury (AKI) is a candidate measure because it is associated with increased rates of morbidity and mortality and is partly preventable. The incidence of AKI after thoracic surgery is not well documented. We conducted an audit to determine the incidence and outcomes of AKI. This audit became a pilot project, and the results indicate the feasibility of a larger study., Methods: Retrospective data on renal function post-thoracic surgery were collected at a tertiary cardiothoracic unit over 12 months. Renal impairment was classified according to the Kidney Disease Improving Global Outcomes criteria., Results: Of 568 patients (mean = 59  ±  SD 18; 38% women), AKI was diagnosed in 86 (15.1%) within 72 h post-thoracic surgery based on the Kidney Disease Improving Global Outcomes staging system (stage 1, n = 55; stage 2, n = 25; stage 3, n = 6). Significant differences were found in postoperative length of stay (3 vs 5 days; P < 0.001) of patients with and without AKI. There was a significant difference between the age groups of patients with and without AKI (P < 0.05) in the open surgical group but not in the group having video-assisted thoracoscopic surgery (VATS). There was no significant difference in the mortality rates between patients with and without AKI., Conclusions: The incidence of AKI after thoracic surgery was 15.1%. AKI was associated with longer hospital stays and was more likely in ≥60-year-old patients after open surgery than after VATS. Reducing AKI could improve patient outcomes. We propose that AKI may be a useful quality measure in thoracic surgery. We are developing a multicentre audit based on this approach., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)

The measurement of extravascular lung water is a relatively new technology which has not yet been well validated as a clinically useful tool. We studied its utility in patients undergoing pulmonary endarterectomy as they frequently suffer reperfusion lung injury and associated oedematous lungs. Such patients are therefore ideal for evaluating this new monitor. We performed a prospective observational cohort study during which extravascular lung water index measurements were taken before and immediately after surgery and postoperatively in intensive care. Data were analysed for 57 patients; 21 patients (37%) experienced severe reperfusion lung injury. The first extravascular lung water index measurement after cardiopulmonary bypass failed to predict severe reperfusion lung injury, area under the receiver operating characteristic curve 0.59 (95%CI 0.44-0.74). On intensive care, extravascular lung water index correlated most strongly at 36 h, area under the receiver operating characteristic curve 0.90 (95%CI 0.80-1.00). Peri-operative extravascular lung water index is not a useful measure to predict severe reperfusion lung injury after pulmonary endarterectomy, however, it does allow monitoring and measurement during the postoperative period. This study implies that extravascular lung water index can be used to directly assess pulmonary fluid overload and that monitoring patients by measuring extravascular lung water index during their intensive care stay is useful and correlates with their clinical course. This may allow directed, pre-empted therapy to attenuate the effects and improve patient outcomes and should prompt further studies., (© 2019 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.)

Background: The incretin hormone glucagon-like peptide 1 (GLP-1) has been shown to protect against lethal ischemia-reperfusion injury in animal models and against nonlethal ischemia reperfusion injury in humans. Furthermore, GLP-1 receptor agonists have been shown to reduce major adverse cardiovascular and cerebrovascular events (MACCE) in large-scale studies. We sought to investigate whether GLP-1 reduced percutaneous coronary intervention (PCI)-associated myocardial infarction (PMI) during elective PCI., Methods: The study was a randomized, double-blind controlled trial in which patients undergoing elective PCI received an intravenous infusion of either GLP-1 at 1.2 pmol/kg/min or matched 0.9% saline placebo before and during the procedure. Randomization was performed in 1:1 fashion, with stratification for diabetes mellitus. Six-hour cardiac troponin I (cTnI) was measured with a primary end point of PMI defined as rise ≫×5 upper limit of normal (280 ng/L). Secondary end points included cTnI rise and MACCE at 12 months., Results: A total of 192 patients were randomized with 152 (79%) male and a mean age of 68.1 ± 8.9 years. No significant differences in patient demographics were noted between the groups. There was no difference in the rate of PMI between GLP-1 and placebo (9 [9.8%] vs 8 [8.3%], P = 1.0) or in the secondary end points of difference in median cTnI between groups (9.5 [0-88.5] vs 20 [0-58.5] ng/L, P = .25) and MACCE at 12 months (7 [7.3%] vs 9 [9.4%], P = .61)., Conclusions: In this randomized, placebo-controlled trial, GLP-1 did not reduce the low incidence of PMI or abrogate biomarker rise during elective PCI, nor did it influence the 12-month MACCE rate which also remained low., Clinical Trial Registration: Clinicaltrials.gov Number: NCT02127996https://clinicaltrials.gov/ct2/show/NCT02127996., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Chronic thromboembolic pulmonary hypertension (CTEPH) is an important consequence of pulmonary embolism that is associated with abnormalities in haemostasis. We investigated the ADAMTS13-von Willebrand factor (VWF) axis in CTEPH, including its relationship with disease severity, inflammation, ABO groups and ADAMTS13 genetic variants.ADAMTS13 and VWF plasma antigen levels were measured in patients with CTEPH (n=208), chronic thromboembolic disease without pulmonary hypertension (CTED) (n=35), resolved pulmonary embolism (n=28), idiopathic pulmonary arterial hypertension (n=30) and healthy controls (n=68). CTEPH genetic ABO associations and protein quantitative trait loci were investigated. ADAMTS13-VWF axis abnormalities were assessed in CTEPH and healthy control subsets by measuring ADAMTS13 activity, D-dimers and VWF multimeric size.Patients with CTEPH had decreased ADAMTS13 (adjusted β -23.4%, 95% CI -30.9- -15.1%, p<0.001) and increased VWF levels (β +75.5%, 95% CI 44.8-113%, p<0.001) compared to healthy controls. ADAMTS13 levels remained low after reversal of pulmonary hypertension by pulmonary endarterectomy surgery and were equally reduced in CTED. We identified a genetic variant near the ADAMTS13 gene associated with ADAMTS13 protein that accounted for ∼8% of the variation in levels.The ADAMTS13-VWF axis is dysregulated in CTEPH. This is unrelated to pulmonary hypertension, disease severity or markers of systemic inflammation and implicates the ADAMTS13-VWF axis in CTEPH pathobiology., Competing Interests: Conflict of interest: M. Newnham reports education support (travel, registration and accommodation) to attend conferences from MSD and GSK, outside the submitted work. Conflict of interest: K. South has nothing to disclose. Conflict of interest: M. Bleda has nothing to disclose. Conflict of interest: W.R. Auger reports grants and non-financial support for advisory board work (travel support only) from Bayer, outside the submitted work. Conflict of interest: J.A. Barberà has nothing to disclose. Conflict of interest: H. Bogaard has nothing to disclose. Conflict of interest: K. Bunclark has nothing to disclose. Conflict of interest: J.E. Cannon reports grants for educational meetings from Actelion, GSK and MSD, outside the submitted work. Conflict of interest: M. Delcroix has nothing to disclose. Conflict of interest: C. Hadinnapola has nothing to disclose. Conflict of interest: L.S. Howard reports grants from Bayer PLC, during the conduct of the study. Conflict of interest: D. Jenkins reports grants and personal fees from Bayer, and personal fees from Actelion, outside the submitted work. Conflict of interest: E. Mayer reports speaker and consultancy fees from Actelion, Bayer and MSD, and speaker fees from Pfizer, outside the submitted work. Conflict of interest: C. Ng has nothing to disclose. Conflict of interest: C.J. Rhodes has nothing to disclose. Conflict of interest: N. Screaton has nothing to disclose. Conflict of interest: K. Sheares reports educational support (travel, registration and accommodation) to attend conferences from Actelion, Bayer, MSD and GSK, and has been on an advisory board for Actelion, outside the submitted work. Conflict of interest: M.A. Simpson has a contract of service with Genomics PLC, outside the submitted work. Conflict of interest: M. Southwood has nothing to disclose. Conflict of interest: L. Su has nothing to disclose. Conflict of interest: D. Taboada reports speaker honoraria and education/travel grants from Actelion, Bayer, GlaxoSmithKline, Lilly, MDS and Pfizer, outside the submitted work. Conflict of interest: M. Traylor has nothing to disclose. Conflict of interest: R.C. Trembath has nothing to disclose. Conflict of interest: S.S. Villar has nothing to disclose. Conflict of interest: M.R. Wilkins has nothing to disclose. Conflict of interest: J. Wharton reports personal fees for advisory board work from Actelion Pharmaceuticals Ltd, outside the submitted work. Conflict of interest: S. Gräf has nothing to disclose. Conflict of interest: J. Pepke-Zaba (or her institution) has received research/educational grants and has been serving on advisory boards for Actelion, Bayer, Merck and GSK. Conflict of interest: M. Laffan reports grants from British Heart Foundation during the conduct of the study; and support to attend conferences from Shire, outside the submitted work. Conflict of interest: D.A. Lane has nothing to disclose. Conflict of interest: N.W. Morrell has nothing to disclose. Conflict of interest: M. Toshner reports grants and personal fees from Bayer, Merck and Actelion, personal fees from GSK, and grants from Roche, during the conduct of the study., (Copyright ©ERS 2019.)

In a rare life-threatening disease setting the number of patients in the trial is a high proportion of all patients with the condition (if not all of them). Further, this number is usually not enough to guarantee the required statistical power to detect a treatment effect of a meaningful size. In such a context, the idea of prioritizing patient benefit over hypothesis testing as the goal of the trial can lead to a trial design that produces useful information to guide treatment, even if it does not do so with the standard levels of statistical confidence. The idealised model to consider such an optimal design of a clinical trial is known as a classic multi-armed bandit problem with a finite patient horizon and a patient benefit objective function. Such a design maximises patient benefit by balancing the learning and earning goals as data accumulates and given the patient horizon. On the other hand, optimally solving such a model has a very high computational cost (many times prohibitive) and more importantly, a cumbersome implementation, even for populations as small as a hundred patients. Several computationally feasible heuristic rules to address this problem have been proposed over the last 40 years in the literature. In this article we study a novel heuristic approach to solve it based on the reformulation of the problem as a Restless bandit problem and the derivation of its corresponding Whittle index rule. Such rule was recently proposed in the context of a clinical trial in Villar et al (2015). We perform extensive computational studies to compare through both exact value calculations and simulated values the performance of this rule, other index rules and simpler heuristics previously proposed in the literature. Our results suggest that for the two and three-armed case and a patient horizon less or equal than a hundred patients, all index rules are a priori practically identical in terms of the expected proportion of success attained when all arms start with a uniform prior. However, we find that a posteriori, for specific values of the parameters of interest, the index policies outperform the simpler rules in every instance and specially so in the case of many arms and a larger, though still relatively small, total number of patients with the diseases. The very good performance of bandit rules in terms of patient benefit (i.e. expected number of successes and mean number of patients allocated to the best arm, if it exists) makes them very appealing in context of the challenge posed by drug development for rare life threatening diseases.

We introduce a non-myopic, covariate-adjusted response adaptive (CARA) allocation design for multi-armed clinical trials. The allocation scheme is a computationally tractable procedure based on the Gittins index solution to the classic multi-armed bandit problem and extends the procedure recently proposed in Villar et al. (2015). Our proposed CARA randomization procedure is defined by reformulating the bandit problem with covariates into a classic bandit problem in which there are multiple combination arms, considering every arm per each covariate category as a distinct treatment arm. We then apply a heuristically modified Gittins index rule to solve the problem and define allocation probabilities from the resulting solution. We report the efficiency, balance, and ethical performance of our approach compared to existing CARA methods using a recently published clinical trial as motivation. The net savings in terms of expected number of treatment failures is considerably larger and probably enough to make this design attractive for certain studies where known covariates are expected to be important, stratification is not desired, treatment failures have a high ethical cost, and the disease under study is rare. In a two-armed context, this patient benefit advantage comes at the expense of increased variability in the allocation proportions and a reduction in statistical power. However, in a multi-armed context, simple modifications of the proposed CARA rule can be incorporated so that an ethical advantage can be offered without sacrificing power in comparison with balanced designs., (© 2017, The International Biometric Society.)

Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability towards better performing treatments as data accumulates. There is considerable interest in using RAR designs in drug development for rare diseases, where traditional designs are not either feasible or ethically questionable. In this paper, we discuss and address a major criticism levelled at RAR: namely, type I error inflation due to an unknown time trend over the course of the trial. The most common cause of this phenomenon is changes in the characteristics of recruited patients-referred to as patient drift. This is a realistic concern for clinical trials in rare diseases due to their lengthly accrual rate. We compute the type I error inflation as a function of the time trend magnitude to determine in which contexts the problem is most exacerbated. We then assess the ability of different correction methods to preserve type I error in these contexts and their performance in terms of other operating characteristics, including patient benefit and power. We make recommendations as to which correction methods are most suitable in the rare disease context for several RAR rules, differentiating between the 2-armed and the multi-armed case. We further propose a RAR design for multi-armed clinical trials, which is computationally efficient and robust to several time trends considered., (© 2017 The Authors. Pharmaceutical Statistics Published by John Wiley & Sons Ltd.)

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.

Adaptive designs for multi-armed clinical trials have become increasingly popular recently because of their potential to shorten development times and to increase patient response. However, developing response-adaptive designs that offer patient-benefit while ensuring the resulting trial provides a statistically rigorous and unbiased comparison of the different treatments included is highly challenging. In this paper, the theory of Multi-Armed Bandit Problems is used to define near optimal adaptive designs in the context of a clinical trial with a normally distributed endpoint with known variance. We report the operating characteristics (type I error, power, bias) and patient-benefit of these approaches and alternative designs using simulation studies based on an ongoing trial. These results are then compared to those recently published in the context of Bernoulli endpoints. Many limitations and advantages are similar in both cases but there are also important differences, specially with respect to type I error control. This paper proposes a simulation-based testing procedure to correct for the observed type I error inflation that bandit-based and adaptive rules can induce., Competing Interests: Disclosure statement No potential conflict of interest was reported by the authors.

Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using the framework of finite-horizon Markov decision processes and dynamic programming (DP), a novel randomised response-adaptive design is proposed which maximises the total number of patient successes in the trial and penalises if a minimum number of patients are not recruited to each treatment arm. Several performance measures of the proposed design are evaluated and compared to alternative designs through extensive simulation studies using a recently published trial as motivation. For simplicity, a two-armed trial with binary endpoints and immediate responses is considered. Simulation results for the proposed design show that: (i) the percentage of patients allocated to the superior arm is much higher than in the traditional fixed randomised design; (ii) relative to the optimal DP design, the power is largely improved upon and (iii) it exhibits only a very small bias and mean squared error of the treatment effect estimator. Furthermore, this design is fully randomised which is an advantage from a practical point of view because it protects the trial against various sources of bias. As such, the proposed design addresses some of the key issues that have been suggested as preventing so-called bandit models from being implemented in clinical practice.

Motivated by a class of Partially Observable Markov Decision Processes with application in surveillance systems in which a set of imperfectly observed state processes is to be inferred from a subset of available observations through a Bayesian approach, we formulate and analyze a special family of multi-armed restless bandit problems. We consider the problem of finding an optimal policy for observing the processes that maximizes the total expected net rewards over an infinite time horizon subject to the resource availability. From the Lagrangian relaxation of the original problem, an index policy can be derived, as long as the existence of the Whittle index is ensured. We demonstrate that such a class of reinitializing bandits in which the projects' state deteriorates while active and resets to its initial state when passive until its completion possesses the structural property of indexability and we further show how to compute the index in closed form. In general, the Whittle index rule for restless bandit problems does not achieve optimality. However, we show that the proposed Whittle index rule is optimal for the problem under study in the case of stochastically heterogenous arms under the expected total criterion, and it is further recovered by a simple tractable rule referred to as the 1-limited Round Robin rule. Moreover, we illustrate the significant suboptimality of other widely used heuristic: the Myopic index rule, by computing in closed form its suboptimality gap. We present numerical studies which illustrate for the more general instances the performance advantages of the Whittle index rule over other simple heuristics.

The Gittins index provides a well established, computationally attractive, optimal solution to a class of resource allocation problems known collectively as the multi-arm bandit problem. Its development was originally motivated by the problem of optimal patient allocation in multi-arm clinical trials. However, it has never been used in practice, possibly for the following reasons: (1) it is fully sequential, i.e., the endpoint must be observable soon after treating a patient, reducing the medical settings to which it is applicable; (2) it is completely deterministic and thus removes randomization from the trial, which would naturally protect against various sources of bias. We propose a novel implementation of the Gittins index rule that overcomes these difficulties, trading off a small deviation from optimality for a fully randomized, adaptive group allocation procedure which offers substantial improvements in terms of patient benefit, especially relevant for small populations. We report the operating characteristics of our approach compared to existing methods of adaptive randomization using a recently published trial as motivation., (© 2015 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first publication of the optimal solution of the classic MABP by a dynamic index rule, the bandit literature quickly diversified and emerged as an active research topic. Across this literature, the use of bandit models to optimally design clinical trials became a typical motivating application, yet little of the resulting theory has ever been used in the actual design and analysis of clinical trials. To this end, we review two MABP decision-theoretic approaches to the optimal allocation of treatments in a clinical trial: the infinite-horizon Bayesian Bernoulli MABP and the finite-horizon variant. These models possess distinct theoretical properties and lead to separate allocation rules in a clinical trial design context. We evaluate their performance compared to other allocation rules, including fixed randomization. Our results indicate that bandit approaches offer significant advantages, in terms of assigning more patients to better treatments, and severe limitations, in terms of their resulting statistical power. We propose a novel bandit-based patient allocation rule that overcomes the issue of low power, thus removing a potential barrier for their use in practice.