Unleashing the full potential of digital outcome measures in clinical trials: eight questions that need attention.

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.

Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event.

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.

Implementing and assessing Bayesian response-adaptive randomisation for backfilling in dose-finding trials.

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.

Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.

Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study.

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.

Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals.

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.

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing.

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.

Continuous Positive Airway Pressure plus Mandibular Advancement Therapy (PAPMAT): study protocol for an adaptive randomised crossover trial comparing the benefits and costs of combining two established treatments for obstructive sleep apnoea.

BACKGROUND: Obstructive sleep apnoea (OSA) involves repeated breathing pauses during sleep due to upper airway obstruction. It causes excessive daytime sleepiness and has other health impacts. Continuous positive airway pressure (CPAP) therapy is effective first line treatment for moderate to severe OSA. Unfortunately, many patients have difficulty tolerating CPAP and pressure intolerance is probably an important contributing factor. Mandibular advancement devices (MAD) are an alternative to CPAP. They are worn in the mouth during sleep to reduce airway obstruction. There is some evidence that, when used in combination with CPAP, MADs improve airway anatomy enough to reduce the CPAP pressure required to treat OSA and that this combination therapy could improve CPAP adherence. METHODS: Consecutive patients starting on CPAP for moderate to severe OSA will be recruited at a regional NHS sleep service. Patients with high CPAP pressure requirements after initial titration, who satisfy all entry criteria and consent to participate, will undertake a 2-arm randomised crossover trial. The arms will be (i) standalone CPAP and (ii) CPAP + MAD therapy. Each arm will last 12 weeks, including 2 weeks acclimatisation. CPAP machines will be auto-titrating and with facility for data download, so the impact of MAD on CPAP pressure requirements and CPAP adherence can be easily measured. The primary outcome will be CPAP adherence. Secondary outcomes will include measures of OSA severity, patient-reported outcome measures including subjective daytime sleepiness, quality of life, and treatment preference at the trial exit and health service use. Cost-effectiveness analyses will be undertaken. DISCUSSION: If the intervention is shown to be effective and cost-effective in improving adherence in this standard CPAP-eligible OSA patient population it would be relatively straightforward to introduce into existing OSA treatment pathways, within the wider NHS and more widely. Both MAD and CPAP are already used by sleep services so their combination would require only minor adjustments to existing clinical pathways. It would be straightforward to disseminate the results of the study through regional, national, and international respiratory meetings. The health economics analysis would provide cost-effectiveness data to inform service planning and clinical guidelines through policy briefing papers, including those by NICE and SIGN. TRIAL REGISTRATION: PAPMAT was registered with ISRCTN prior to recruitment beginning (ISRCTN Registry 2021): https://www.isrctn.com/ISRCTN33966032 . Registered on 17th November 2021.

Response-adaptive randomization in clinical trials: from myths to practical considerations.

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.

A novel statistical test for treatment differences in clinical trials using a response-adaptive forward-looking Gittins Index Rule.

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.

Effect of Hypoxemia on Outcome in Respiratory Failure Supported With Extracorporeal Membrane Oxygenation: A Cardinality Matched Cohort Study.

Venovenous extracorporeal membrane oxygenation (ECMO) is recommended in adult patients with refractory acute respiratory failure (ARF), but there is limited evidence for its use in patients with less severe hypoxemia. Prior research has suggested a lower PaO 2 /FiO 2 at cannulation is associated with higher short-term mortality, but it is unclear whether this is due to less severe illness or a potential benefit of earlier ECMO support. In this exploratory cardinality-matched observational cohort study, we matched 668 patients who received venovenous ECMO as part of a national severe respiratory failure service into cohorts of "less severe" and "very severe" hypoxemia based on the median PaO 2 /FiO 2 at ECMO institution of 68 mmHg. Before matching, ICU mortality was 19% in the 'less severe' hypoxemia group and 28% in the "very severe" hypoxemia group (RR for mortality = 0.69, 95% CI 0.54-0.88). After matching on key prognostic variables including underlying diagnosis, this difference remained statistically present but smaller: (23% vs. 30%, RR = 0.76, 95% CI 0.59-0.99). This may suggest the observed survival benefit of venovenous ECMO is not solely due to reduced disease severity. Further research is warranted to examine the potential role of ECMO in ARF patients with less severe hypoxemia.

Some performance considerations when using multi-armed bandit algorithms in the presence of missing data.

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.

Practical guidance for planning resources required to support publicly-funded adaptive clinical trials.

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.

Effect of high-flow nasal therapy on patient-centred outcomes in patients at high risk of postoperative pulmonary complications after cardiac surgery: a statistical analysis plan for NOTACS, a multicentre adaptive randomised controlled trial.

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.

Effect of high-flow nasal therapy on patient-centred outcomes in patients at high risk of postoperative pulmonary complications after cardiac surgery: a study protocol for a multicentre adaptive randomised controlled trial.

BACKGROUND: High-flow nasal therapy is a non-invasive form of respiratory support that delivers low-level, flow dependent positive airway pressure. The device can be better tolerated by patients than alternatives such as continuous positive airway pressure. The primary objective is to determine if prophylactic high-flow nasal therapy after tracheal extubation can result in an increase in the number of days alive and at home within the first 90 days after surgery, when compared with standard oxygen therapy. The co-primary objective is to estimate the incremental cost-effectiveness and cost-utility of high-flow nasal therapy vs standard oxygen therapy at 90 days, from the view-point of the public sector, the health service and patients. METHODS: This is an adaptive, multicentre, international parallel-group, randomised controlled trial with embedded cost-effectiveness analysis comparing the use of high-flow nasal therapy with control in patients at high risk of respiratory complications following cardiac surgery. Participants will be randomised before tracheal extubation and allocated either high-flow nasal therapy or standard oxygen therapy for a minimum of 16 h immediately post extubation. Participants will be followed up until 90 days after surgery. The total sample size needed to detect a 2-day increase in DAH90 with 90% power with an intention to treat analysis is 850 patients. The adaptive design includes an interim sample size re-estimation which will provide protection against deviations from the original sample size assumptions made from the single-centre pilot study and will allow for a maximum sample size increase to 1152 patients. DISCUSSION: Evidence to support routine use of high-flow nasal therapy will inform the development of effective enhanced recovery care bundles. Reducing complications should reduce length of stay and re-admission to hospital and provide an important focus for cost reduction. However; high-quality studies evaluating the clinical and cost effectiveness of high-flow nasal therapy after cardiothoracic surgery are lacking. TRIAL REGISTRATION: The study has been registered with ISRCTN ( ISRCTN14092678 , 13/05/2020) Clinicaltrials.gov Registration Pending.

Quantifying Efficiency Gains of Innovative Designs of Two-Arm Vaccine Trials for COVID-19 Using an Epidemic Simulation Model.

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.

Response adaptive intervention allocation in stepped-wedge cluster randomized trials.

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.

Positioning imatinib for pulmonary arterial hypertension: A phase I/II design comprising dose finding and single-arm efficacy.

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.

Costs and staffing resource requirements for adaptive clinical trials: quantitative and qualitative results from the Costing Adaptive Trials project.

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.