Bayesian adaptive trials offer advantages in comparative effectiveness trials: an example in status epilepticus

Whole-of-Life Inclusion in Bayesian Adaptive Platform Clinical Trials

Importance

There is a recognized unmet need for clinical trials to provide evidence-informed care for infants, children and adolescents. This Special Communication outlines the capacity of 3 distinct trial design strategies, sequential, parallel, and a unified adult-pediatric bayesian adaptive design, to incorporate children into clinical trials and transform this current state of evidence inequity. A unified adult-pediatric whole-of-life clinical trial is demonstrated through the Staphylococcus aureus Network Adaptive Platform (SNAP) trial.

Observations

Bayesian methods provide a framework for synthesizing data in the form of a probability model that can be used in the design and analysis of a clinical trial. Three trial design strategies are compared: (1) a sequential adult-pediatric bayesian approach that involves a separate, deferred pediatric trial that incorporates existing adult trial data into the analysis model to potentially reduce the pediatric trial sample size; (2) a parallel adult-pediatric bayesian trial whereby separate pediatric enrollment occurs in a parallel trial, running alongside an adult randomized clinical trial; and (3) a unified adult-pediatric bayesian adaptive design that supports the enrollment of both children and adults simultaneously in a whole-of-life bayesian adaptive randomized clinical trial. The SNAP trial whole-of-life design uses a bayesian hierarchical model that allows information sharing (also known as borrowing) between trial age groups by linking intervention effects of children and adults, thereby improving inference in both groups.

Conclusion and Relevance

Bayesian hierarchical models may provide more precision for estimates of safety and efficacy of treatments in trials with heterogenous populations compared to traditional methods of analysis. They facilitate the inclusion of children in clinical trials and a shift from children deemed therapeutic orphans to the vision of no child left behind in clinical trials to ensure evidence for clinical practice exists across the life course. The SNAP trial provides an example of a bayesian adaptive whole-of-life inclusion design that enhances trial population inclusivity and diversity overall, as well as generalizability and translation of findings into clinical practice.

Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN

Background

Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information.

Methods

PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments.

Results

Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin.

Conclusions

Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials.

Evolution of Phase II Oncology Trial Design: from Single Arm to Master Protocol

The recent development of novel anticancer treatments with diverse mechanisms of action has accelerated the detection of treatment candidates tremendously. The rapidly changing drug development landscapes and the high failure rates in Phase III trials both underscore the importance of more efficient and robust phase II designs. The goals of phase II oncology studies are to explore the preliminary efficacy and toxicity of the investigational product and to inform future drug development strategies such as go/no-go decisions for phase III development, or dose/indication selection. These complex purposes of phase II oncology designs call for efficient, flexible, and easy-to-implement clinical trial designs. Therefore, innovative adaptive study designs with the potential of improving the efficiency of the study, protecting patients, and improving the quality of information gained from trials have been commonly used in Phase II oncology studies. Although the value of adaptive clinical trial methods in early phase drug development is generally well accepted, there is no comprehensive review and guidance on adaptive design methods and their best practice for phase II oncology trials. In this paper, we review the recent development and evolution of phase II oncology design, including frequentist multistage design, Bayesian continuous monitoring, master protocol design, and innovative design methods for randomized phase II studies. The practical considerations and the implementation of these complex design methods are also discussed.

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

What have we learned from the RAMPART and ESETT randomized controlled trials?

Lessons learned from the Rapid Anticonvulsant Medication Prior to Arrival Trial (RAMPART) and Established Status Epilepticus Treatment Trial (ESETT) trials are considered in three ways. First, by asking about the questions the trials were primarily designed to answer, then about the context of the intervention and characteristics of the patients described in secondary analyses, and finally, about what was learned about how to conduct trials in this space. The talk concludes with suggestions and a vision for how to best conduct future trials investigating status epilepticus. This paper was presented at the 8th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures held in September 2022.

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

BACKGROUND

Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time.

METHODS

To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics.

RESULTS AND CONCLUSIONS

Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

Feasibility of a Platform Trial Design for the Development of Mobile Health Applications: A Review

Background: A novel adaptive trial design called platform trials (PTs) may offer an effective, efficient, and unbiased approach to evaluate different developer versions of mobile health (m-health) apps. However, the feasibility of their use for this purpose is yet to be explored. Objective: This literature review aims to explore the reported challenges associated with the adaptive PT design to assess its feasibility for the development of m-health apps. Methods: A descriptive literature review using two databases (MEDLINE and Embase) was conducted. Documents published in English between 1947 and September 20, 2020, were eligible for inclusion. Results: The titles and abstracts of 758 records were screened after which 179 full-text articles were assessed for eligibility. A total of 41 articles were included in the synthesis, all published after the year 2000. The synthesis yielded eight distinct categories of challenging issues with PTs relevant to their application in m-health app development, along with potential solutions. These categories are ethical issues (e.g., related to informed consent, equipoise, justice) (with 19 articles contributing content), biases (7 articles), temporal drift (4 articles), miscellaneous statistical issues (3 articles), logistical issues (e.g., cost and human resources, frequent amendments; 6 articles), sample size and power conflict (2 articles), generalizability of the results (2 articles), and operational challenges (1 article). Conclusion: Although PT designs are relatively new, they have promising feasibility for the seamless evaluation of interventions that undergo continuous development, including m-health apps; however, various challenges may hinder their successful implementation.

Prophylaxis in healthcare workers during a pandemic: a model for a multi-centre international randomised controlled trial using Bayesian analyses

BACKGROUND

Coronavirus disease 2019 (COVID-19) has exposed the disproportionate effects of pandemics on frontline workers and the ethical imperative to provide effective prophylaxis. We present a model for a pragmatic randomised controlled trial (RCT) that utilises Bayesian methods to rapidly determine the efficacy or futility of a prophylactic agent.

METHODS

We initially planned to undertake a multicentre, phase III, parallel-group, open-label RCT, to determine if hydroxychloroquine (HCQ) taken once a week was effective in preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in healthcare workers (HCW) aged ≥ 18 years in New Zealand (NZ) and Ireland. Participants were to be randomised 2:1 to either HCQ (800 mg stat then 400 mg weekly) or no prophylaxis. The primary endpoint was time to Nucleic Acid Amplification Test-proven SARS-CoV-2 infection. Secondary outcome variables included mortality, hospitalisation, intensive care unit admissions and length of mechanical ventilation. The trial had no fixed sample size or duration of intervention. Bayesian adaptive analyses were planned to occur fortnightly, commencing with a weakly informative prior for the no prophylaxis group hazard rate and a moderately informative prior on the intervention log hazard ratio centred on 'no effect'. Stopping for expected success would be executed if the intervention had a greater than 0.975 posterior probability of reducing the risk of SARS-CoV-2 infection by more than 10%. Final success would be declared if, after completion of 8 weeks of follow-up (reflecting the long half-life of HCQ), the prophylaxis had at least a 0.95 posterior probability of reducing the risk of SARS-CoV-2 infection by more than 10%. Futility would be declared if HCQ was shown to have less than a 0.10 posterior probability of reducing acquisition of SARS-CoV-2 infection by more than 20%.

DISCUSSION

This study did not begin recruitment due to the marked reduction in COVID-19 cases in NZ and concerns regarding the efficacy and risks of HCQ treatment in COVID-19. Nonetheless, the model presented can be easily adapted for other potential prophylactic agents and pathogens, and pre-established collaborative models like this should be shared and incorporated into future pandemic preparedness planning.

TRIAL REGISTRATION

The decision not to proceed with the study was made before trial registration occurred.

Comparison of Bayesian vs Frequentist Adaptive Trial Design in the Stroke Hyperglycemia Insulin Network Effort Trial

IMPORTANCE

Bayesian adaptive trial design has the potential to create more efficient clinical trials. However, a barrier to the uptake of bayesian adaptive designs for confirmatory trials is limited experience with how they may perform compared with a frequentist design.

OBJECTIVE

To compare the performance of a bayesian and a frequentist adaptive clinical trial design.

DESIGN, SETTING, AND PARTICIPANTS

This prospective cohort study compared 2 trial designs for a completed multicenter acute stroke trial conducted within a National Institutes of Health neurologic emergencies clinical trials network, with individual patient-level data, including the timing and order of enrollments and outcome ascertainment, from 1151 patients with acute stroke and hyperglycemia randomized to receive intensive or standard insulin therapy. The implemented frequentist design had group sequential boundaries for efficacy and futility interim analyses at 90 days after randomization for 500, 700, 900, and 1100 patients. The bayesian alternative used predictive probability of trial success to govern early termination for efficacy and futility with a first interim analysis at 500 randomized patients and subsequent interims after every 100 randomizations.

MAIN OUTCOMES AND MEASURES

The main outcome was the sample size at end of study, which was defined as the sample size at which each of the studies stopped accrual of patients.

RESULTS

Data were collected from 1151 patients. As conducted, the frequentist design passed the futility boundary after 936 participants were randomized. Using the same sequence and timing of randomization and outcome data, the bayesian alternative crossed the futility boundary approximately 3 months earlier after 800 participants were randomized.

CONCLUSIONS AND RELEVANCE

Both trial designs stopped for futility before reaching the planned maximum sample size. In both cases, the clinical community and patients would benefit from learning the answer to the trial's primary question earlier. The common feature across the 2 designs was frequent interim analyses to stop early for efficacy or for futility. Differences between how these analyses were implemented between the 2 trials resulted in the differences in early stopping.

Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.

Advances in the management of generalized convulsive status epilepticus: what have we learned?

Convulsive status epilepticus is the most serious manifestation of an epileptic diathesis. In the early stages (5-30 min), there exists class A evidence to support the efficacy of benzodiazepines as first-line treatment. As status epilepticus progresses into the later stages, the evidence for treatment becomes less robust until we are depending upon short case series and case reports for the treatment of refractory status epilepticus. However, the past year saw the publication of three randomized controlled trials in the setting of benzodiazepine-resistant established convulsive status epilepticus: the EcLiPSE and ConSEPT studies, compared levetiracetam to phenytoin in children; and the ESETT study compared fosphenytoin, levetiracetam and sodium valproate in adults and children. In addition, the emergence of data from the SENSE study, a multicentre multinational prospective cohort study and the publication of a systematic review and meta-analysis of the mortality of status epilepticus over the past 30 years, has brought the treatment of status epilepticus into sharp focus. In this update we provide a detailed analysis of these studies and their impact on clinical practice. We review contentious areas of management in status epilepticus where a consensus is lacking and advance the case for more research on existing and alternative treatment strategies.

Do we need to adjust for interim analyses in a Bayesian adaptive trial design?

Background

Bayesian adaptive methods are increasingly being used to design clinical trials and offer several advantages over traditional approaches. Decisions at analysis points are usually based on the posterior distribution of the treatment effect. However, there is some confusion as to whether control of type I error is required for Bayesian designs as this is a frequentist concept.

Methods

We discuss the arguments for and against adjusting for multiplicities in Bayesian trials with interim analyses. With two case studies we illustrate the effect of including interim analyses on type I/II error rates in Bayesian clinical trials where no adjustments for multiplicities are made. We propose several approaches to control type I error, and also alternative methods for decision-making in Bayesian clinical trials.

Results

In both case studies we demonstrated that the type I error was inflated in the Bayesian adaptive designs through incorporation of interim analyses that allowed early stopping for efficacy and without adjustments to account for multiplicity. Incorporation of early stopping for efficacy also increased the power in some instances. An increase in the number of interim analyses that only allowed early stopping for futility decreased the type I error, but also decreased power. An increase in the number of interim analyses that allowed for either early stopping for efficacy or futility generally increased type I error and decreased power.

Conclusions

Currently, regulators require demonstration of control of type I error for both frequentist and Bayesian adaptive designs, particularly for late-phase trials. To demonstrate control of type I error in Bayesian adaptive designs, adjustments to the stopping boundaries are usually required for designs that allow for early stopping for efficacy as the number of analyses increase. If the designs only allow for early stopping for futility then adjustments to the stopping boundaries are not needed to control type I error. If one instead uses a strict Bayesian approach, which is currently more accepted in the design and analysis of exploratory trials, then type I errors could be ignored and the designs could instead focus on the posterior probabilities of treatment effects of clinically-relevant values.

Efficacy of levetiracetam, fosphenytoin, and valproate for established status epilepticus by age group (ESETT): a double-blind, responsive-adaptive, randomised controlled trial

BACKGROUND

Benzodiazepine-refractory, or established, status epilepticus is thought to be of similar pathophysiology in children and adults, but differences in underlying aetiology and pharmacodynamics might differentially affect response to therapy. In the Established Status Epilepticus Treatment Trial (ESETT) we compared the efficacy and safety of levetiracetam, fosphenytoin, and valproate in established status epilepticus, and here we describe our results after extending enrolment in children to compare outcomes in three age groups.

METHODS

In this multicentre, double-blind, response-adaptive, randomised controlled trial, we recruited patients from 58 hospital emergency departments across the USA. Patients were eligible for inclusion if they were aged 2 years or older, had been treated for a generalised convulsive seizure of longer than 5 min duration with adequate doses of benzodiazepines, and continued to have persistent or recurrent convulsions in the emergency department for at least 5 min and no more than 30 min after the last dose of benzodiazepine. Patients were randomly assigned in a response-adaptive manner, using Bayesian methods and stratified by age group (<18 years, 18-65 years, and >65 years), to levetiracetam, fosphenytoin, or valproate. All patients, investigators, study staff, and pharmacists were masked to treatment allocation. The primary outcome was absence of clinically apparent seizures with improved consciousness and without additional antiseizure medication at 1 h from start of drug infusion. The primary safety outcome was life-threatening hypotension or cardiac arrhythmia. The efficacy and safety outcomes were analysed by intention to treat. This study is registered in ClinicalTrials.gov, NCT01960075.

FINDINGS

Between Nov 3, 2015, and Dec 29, 2018, we enrolled 478 patients and 462 unique patients were included: 225 children (aged <18 years), 186 adults (18-65 years), and 51 older adults (>65 years). 175 (38%) patients were randomly assigned to levetiracetam, 142 (31%) to fosphenyltoin, and 145 (31%) were to valproate. Baseline characteristics were balanced across treatments within age groups. The primary efficacy outcome was met in those treated with levetiracetam for 52% (95% credible interval 41-62) of children, 44% (33-55) of adults, and 37% (19-59) of older adults; with fosphenytoin in 49% (38-61) of children, 46% (34-59) of adults, and 35% (17-59) of older adults; and with valproate in 52% (41-63) of children, 46% (34-58) of adults, and 47% (25-70) of older adults. No differences were detected in efficacy or primary safety outcome by drug within each age group. With the exception of endotracheal intubation in children, secondary safety outcomes did not significantly differ by drug within each age group.

INTERPRETATION

Children, adults, and older adults with established status epilepticus respond similarly to levetiracetam, fosphenytoin, and valproate, with treatment success in approximately half of patients. Any of the three drugs can be considered as a potential first-choice, second-line drug for benzodiazepine-refractory status epilepticus.

FUNDING

National Institute of Neurological Disorders and Stroke, National Institutes of Health.

Bayesian adaptive designs for multi-arm trials: an orthopaedic case study

BACKGROUND

Bayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi-arm phase III clinical trials and assess potential benefits that these designs offer.

METHODS

We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs via simulation. We then virtually re-executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs.

RESULTS

We constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial.

CONCLUSIONS

Using CAST as an example, this case study shows how Bayesian adaptive designs can be constructed for phase III multi-arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.

TRIAL REGISTRATION

CAST study registration ISRCTN, ISRCTN37807450. Retrospectively registered on 25 April 2003.

Lessons from the Established Status Epilepticus Treatment Trial

Convulsive status epilepticus (SE) is a relatively common emergency condition affecting individuals of all ages. The primary goal of treatment is prompt termination of seizures. Where first-line treatment with benzodiazepine has failed to achieve this, a condition known as established SE (ESE), there is uncertainty about which agent to use next. The Established Status Epilepticus Treatment Trial (ESETT) is a 3-arm (valproate (VPA), fosphenytoin (FOS), levetiracetam (LEV)), phase III, double-blind randomized comparative effectiveness study in patients aged 2 years and above with established convulsive SE. Enrollment was completed in January 2019, and the results are expected later this year. We discuss lessons learnt during the conduct of the study in relation to the following: ethical considerations; trial design and practical implementation in emergency settings, including pediatric and adult populations; quality assurance; and outcome determination where treating emergency clinicians may lack specialist expertise. We consider that the ESETT is already informing both clinical practice and future trial design. This article is part of the Special Issue "Proceedings of the 7th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures".

Optimal delay time to initiate anticoagulation after ischemic stroke in atrial fibrillation (START): Methodology of a pragmatic, response-adaptive, prospective randomized clinical trial

RATIONALE

An estimated 15% of all strokes are associated with untreated atrial fibrillation. Long-term secondary stroke prevention in atrial fibrillation is anticoagulation, increasingly with non-vitamin K oral anticoagulants. The optimal time to initiate anticoagulation following an atrial fibrillation-related stroke that balances hemorrhagic conversion with recurrent stroke is not yet known.

AIMS

To determine if there is an optimal delay time to initiate anticoagulation after atrial fibrillation-related stroke that optimizes the composite outcome of hemorrhagic conversion and recurrent ischemic stroke.

SAMPLE SIZE ESTIMATES

The study will enroll 1500 total subjects split between a mild to moderate stroke cohort (1000) and a severe stroke cohort (500).

METHODS AND DESIGN

This study is a multi-center, prospective, randomized, pragmatic, adaptive trial that randomizes subjects to four arms of time to start of anticoagulation. The four arms for mild to moderate stroke are: Day 3, Day 6, Day 10, and Day 14. The time intervals for severe stroke are: Day 6, Day 10, Day 14, and Day 21. Allocation involves a response adaptive randomization via interim analyses to favor the arms that have a better risk-benefit profile.

STUDY OUTCOMES

The primary outcome event is the composite occurrence of an ischemic or hemorrhagic event within 30 days of the index stroke. Secondary outcomes are also collected at 30 and 90 days.

DISCUSSION

The optimal timing of direct oral anticoagulants post-ischemic stroke requires prospective randomized testing. A pragmatically designed trial with adaptive allocation and randomization to multiple time intervals such as the START trial is best suited to answer this question in order to directly inform current practice on this question.

Randomized Trial of Three Anticonvulsant Medications for Status Epilepticus

BACKGROUND

The choice of drugs for patients with status epilepticus that is refractory to treatment with benzodiazepines has not been thoroughly studied.

METHODS

In a randomized, blinded, adaptive trial, we compared the efficacy and safety of three intravenous anticonvulsive agents - levetiracetam, fosphenytoin, and valproate - in children and adults with convulsive status epilepticus that was unresponsive to treatment with benzodiazepines. The primary outcome was absence of clinically evident seizures and improvement in the level of consciousness by 60 minutes after the start of drug infusion, without additional anticonvulsant medication. The posterior probabilities that each drug was the most or least effective were calculated. Safety outcomes included life-threatening hypotension or cardiac arrhythmia, endotracheal intubation, seizure recurrence, and death.

RESULTS

A total of 384 patients were enrolled and randomly assigned to receive levetiracetam (145 patients), fosphenytoin (118), or valproate (121). Reenrollment of patients with a second episode of status epilepticus accounted for 16 additional instances of randomization. In accordance with a prespecified stopping rule for futility of finding one drug to be superior or inferior, a planned interim analysis led to the trial being stopped. Of the enrolled patients, 10% were determined to have had psychogenic seizures. The primary outcome of cessation of status epilepticus and improvement in the level of consciousness at 60 minutes occurred in 68 patients assigned to levetiracetam (47%; 95% credible interval, 39 to 55), 53 patients assigned to fosphenytoin (45%; 95% credible interval, 36 to 54), and 56 patients assigned to valproate (46%; 95% credible interval, 38 to 55). The posterior probability that each drug was the most effective was 0.41, 0.24, and 0.35, respectively. Numerically more episodes of hypotension and intubation occurred in the fosphenytoin group and more deaths occurred in the levetiracetam group than in the other groups, but these differences were not significant.

CONCLUSIONS

In the context of benzodiazepine-refractory convulsive status epilepticus, the anticonvulsant drugs levetiracetam, fosphenytoin, and valproate each led to seizure cessation and improved alertness by 60 minutes in approximately half the patients, and the three drugs were associated with similar incidences of adverse events. (Funded by the National Institute of Neurological Disorders and Stroke; ESETT ClinicalTrials.gov number, NCT01960075.).

An introduction to clinical trial design

Clinicians and other decision makers in healthcare use results from clinical trials to inform practice. Interpretation of clinical trial results can be challenging, as weaknesses in trial design, data collection, analysis or reporting, can compromise the usefulness of results. A good working knowledge of clinical trial design is essential to expertly interpret and determine the validity and generalizability of the results. This manuscript will give a brief overview of clinical trial design including the strengths and limitations of various approaches. The focus will be on confirmatory clinical trials.

Comparison of methods for control allocation in multiple arm studies using response adaptive randomization

BACKGROUND/AIMS

Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting.

METHODS

We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial.

RESULTS

The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation.

CONCLUSION

Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.

Management of convulsive status epilepticus in children: an adapted clinical practice guideline for pediatricians in Saudi Arabia

Objective:

To increase the use of evidence-based approaches in the diagnosis, investigations and treatment of Convulsive Status Epilepticus (CSE) in children in relevant care settings.

Method:

A Clinical Practice Guideline (CPG) adaptation group was formulated at a university hospital in Riyadh. The group utilized 2 CPG validated tools including the ADAPTE method and the AGREE II instrument.

Results:

The group adapted 3 main categories of recommendations from one Source CPG. The recommendations cover; (i)first-line treatment of CSE in the community; (ii)treatment of CSE in the hospital; and (iii)refractory CSE. Implementation tools were built to enhance knowledge translation of these recommendations including a clinical algorithm, audit criteria, and a computerized provider order entry.

Conclusion:

A clinical practice guideline for the Saudi healthcare context was formulated using a guideline adaptation process to support relevant clinicians managing CSE in children.

Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials

Limited resources are a challenge when planning comparative effectiveness studies of multiple promising treatments, often prompting study planners to reduce the sample size to meet the financial constraints. The practical solution is often to increase the efficiency of this sample size by selecting a pair of treatments among the pool of promising treatments before the clinical trial begins. The problem with this approach is that the investigator may inadvertently leave out the most beneficial treatment. This paper demonstrates a possible solution to this problem by using Bayesian adaptive designs. We use a planned comparative effectiveness clinical trial of treatments for sialorrhea in amyotrophic lateral sclerosis as an example of the approach. Rather than having to guess at the two best treatments to compare based on limited data, we suggest putting more arms in the trial and letting response adaptive randomization (RAR) determine better arms. To ground this study relative to previous literature we first compare RAR, adaptive equal randomization (ER), arm(s) dropping, and a fixed design. Given the goals of this trial we demonstrate that we may avoid 'type III errors' - inadvertently leaving out the best treatment - with little loss in power compared to a two-arm design, even when choosing the correct two arms for the two-armed design. There are appreciable gains in power when the two arms are prescreened at random.

Recommendations for the design of therapeutic trials for neonatal seizures

Although seizures have a higher incidence in neonates than any other age group and are associated with significant mortality and neurodevelopmental disability, treatment is largely guided by physician preference and tradition, due to a lack of data from well-designed clinical trials. There is increasing interest in conducting trials of novel drugs to treat neonatal seizures, but the unique characteristics of this disorder and patient population require special consideration with regard to trial design. The Critical Path Institute formed a global working group of experts and key stakeholders from academia, the pharmaceutical industry, regulatory agencies, neonatal nurse associations, and patient advocacy groups to develop consensus recommendations for design of clinical trials to treat neonatal seizures. The broad expertise and perspectives of this group were invaluable in developing recommendations addressing: (1) use of neonate-specific adaptive trial designs, (2) inclusion/exclusion criteria, (3) stratification and randomization, (4) statistical analysis, (5) safety monitoring, and (6) definitions of important outcomes. The guidelines are based on available literature and expert consensus, pharmacokinetic analyses, ethical considerations, and parental concerns. These recommendations will ultimately facilitate development of a Master Protocol and design of efficient and successful drug trials to improve the treatment and outcome for this highly vulnerable population.

Using Bayesian adaptive designs to improve phase III trials: a respiratory care example

BACKGROUND

Bayesian adaptive designs can improve the efficiency of trials, and lead to trials that can produce high quality evidence more quickly, with fewer patients and lower costs than traditional methods. The aim of this work was to determine how Bayesian adaptive designs can be constructed for phase III clinical trials in critical care, and to assess the influence that Bayesian designs would have on trial efficiency and study results.

METHODS

We re-designed the High Frequency OSCillation in Acute Respiratory distress syndrome (OSCAR) trial using Bayesian adaptive design methods, to allow for the possibility of early stopping for success or futility. We constructed several alternative designs and studied their operating characteristics via simulation. We then performed virtual re-executions by applying the Bayesian adaptive designs using the OSCAR data to demonstrate the practical applicability of the designs.

RESULTS

We constructed five alternative Bayesian adaptive designs and identified a preferred design based on the simulated operating characteristics, which had similar power to the original design but recruited fewer patients on average. The virtual re-executions showed the Bayesian sequential approach and original OSCAR trial yielded similar trial conclusions. However, using a Bayesian sequential design could have led to a reduced sample size and earlier completion of the trial.

CONCLUSIONS

Using the OSCAR trial as an example, this case study found that Bayesian adaptive designs can be constructed for phase III critical care trials. If the OSCAR trial had been run using one of the proposed Bayesian adaptive designs, it would have terminated at a smaller sample size with fewer deaths in the trial, whilst reaching the same conclusions. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.

TRIAL REGISTRATION

OSCAR Trial registration ISRCTN, ISRCTN10416500 . Retrospectively registered 13 June 2007.

Time-trend impact on treatment estimation in two-arm clinical trials with a binary outcome and Bayesian response adaptive randomization

Clinical trial design and analysis often assume study population homogeneity, although patient baseline profile and standard of care may evolve over time, especially in trials with long recruitment periods. The time-trend phenomenon can affect the treatment estimation and the operating characteristics of trials with Bayesian response adaptive randomization (BRAR). The mechanism of time-trend impact on BRAR is increasingly being studied but some aspects remain unclear. The goal of this research is to quantify the bias in treatment effect estimation due to the use of BRAR in the presence of time-trend. In addition, simulations are conducted to compare the performance of three commonly used BRAR algorithms under different time-trend patterns with and without early stopping rules. The results demonstrate that using these BRAR methods in a two-arm trial with time-trend may cause type I error inflation and treatment effect estimation bias. The magnitude and direction of the bias are affected by the parameters of the BRAR algorithm and the time-trend pattern.

Trial Refresh: A Case for an Adaptive Platform Trial for Pulmonary Exacerbations of Cystic Fibrosis

Cystic fibrosis is a genetic disease typically characterized by progressive lung damage and premature mortality. Pulmonary exacerbations, or flare-ups of the lung disease, often require hospitalization for intensive treatment. Approximately 25% of patients with cystic fibrosis do not recover their baseline lung function after pulmonary exacerbations. There is a relative paucity of evidence to inform treatment strategies for exacerbations. Compounding this lack of evidence, there are a large number of treatment options already as well as becoming available. This results in significant variability between medication regimens prescribed by different physicians, treatment centers and regions with potentially adverse impact to patients. The conventional strategy is to undertake essential randomized clinical trials to inform treatment decisions and improve outcomes for patients with exacerbations. However, over the past several decades, clinical trials have generally failed to provide information critical to improved treatment and management of exacerbations. Bayesian adaptive platform trials hold the promise of addressing clinical uncertainties and informing treatment. Using modeling and response adaptive randomization, they allow for the evaluation of multiple treatments across different management domains, and progressive improvement in patient outcomes throughout the course of the trial. Bayesian adaptive platform trials require substantial amounts of preparation. Basic preparation includes extensive stakeholder involvement including elicitation of consumer preferences and clinician understanding of the research topic, defining the research questions, determining the best outcome measures, delineating study sub-groups, in depth statistical modeling, designing end-to-end digital solutions seamlessly supporting clinicians, researchers and patients, constructing randomisation algorithms and importantly, defining pre-determined intra-study end-points. This review will discuss the motivation and necessary steps required to embark on a Bayesian adaptive platform trial to optimize medication regimens for the treatment of pulmonary exacerbations of cystic fibrosis.

Bayesian Model Selection Maps for Group Studies Using M/EEG Data

Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called "Bayesian brains" continuously create and update generative models of the world, inferring (hidden) causes from (sensory) consequences. Neuroimaging datasets enable the detailed investigation of such modeling and updating processes, and these datasets can themselves be analyzed with Bayesian approaches. These offer methodological advantages over classical statistics. Specifically, any number of models can be compared, the models need not be nested, and the "null model" can be accepted (rather than only failing to be rejected as in frequentist inference). This methodological paper explains how to construct posterior probability maps (PPMs) for Bayesian Model Selection (BMS) at the group level using electroencephalography (EEG) or magnetoencephalography (MEG) data. The method has only recently been used for EEG data, after originally being developed and applied in the context of functional magnetic resonance imaging (fMRI) analysis. Here, we describe how this method can be adapted for EEG using the Statistical Parametric Mapping (SPM) software package for MATLAB. The method enables the comparison of an arbitrary number of hypotheses (or explanations for observed responses), at each and every voxel in the brain (source level) and/or in the scalp-time volume (scalp level), both within participants and at the group level. The method is illustrated here using mismatch negativity (MMN) data from a group of participants performing an audio-spatial oddball attention task. All data and code are provided in keeping with the Open Science movement. In doing so, we hope to enable others in the field of M/EEG to implement our methods so as to address their own questions of interest.

Antimicrobials for the treatment of drug-resistant Acinetobacter baumannii pneumonia in critically ill patients: a systemic review and Bayesian network meta-analysis

BACKGROUND

An optimal therapy for the treatment of pneumonia caused by drug-resistant Acinetobacter baumannii remains unclear. This study aims to compare various antimicrobial strategies and to determine the most effective therapy for pneumonia using a network meta-analysis.

METHODS

Systematic search and quality assessment were performed to select eligible studies reporting one of the following outcomes: all-cause mortality, clinical cure, and microbiological eradication. The primary outcome was all-cause mortality. A network meta-analysis was conducted with a Bayesian approach. Antimicrobial treatments were ranked based on surface under the cumulative ranking curve (SUCRA) value along with estimated median outcome rate and corresponding 95% credible intervals (CrIs). Two treatments were considered significantly different if a posterior probability of superiority (P) was greater than 97.5%.

RESULTS

Twenty-three studies evaluating 15 antimicrobial treatments were included. Intravenous colistin monotherapy (IV COL) was selected as a common comparator, serving as a bridge for developing the network. Five treatments ranked higher than IV COL (SUCRA, 57.1%; median all-cause mortality 0.45, 95% CrI 0.41-0.48) for reducing all-cause mortality: sulbactam monotherapy (SUL, 100.0%; 0.18, 0.04-0.42), high-dose SUL (HD SUL, 85.7%; 0.31, 0.07-0.71), fosfomycin plus IV COL (FOS + IV COL, 78.6%; 0.34, 0.19-0.54), inhaled COL plus IV COL (IH COL + IV COL, 71.4%; 0.39, 0.32-0.46), and high-dose tigecycline (HD TIG, 71.4%; 0.39, 0.16-0.67). Those five treatments also ranked higher than IV COL (SUCRA, 45.5%) for improving clinical cure (72.7%, 72.7%, 63.6%, 81.8%, and 90.9%, respectively). Among the five treatments, SUL (P = 98.1%) and IH COL + IV COL (P = 99.9%) were significantly superior to IV COL for patient survival and clinical cure, respectively. In terms of microbiological eradication, FOS + IV COL (P = 99.8%) and SUL (P = 98.9%) were significantly superior to IV COL.

CONCLUSIONS

This Bayesian network meta-analysis demonstrated the comparative effectiveness of fifteen antimicrobial treatments for drug-resistant A. baumannii pneumonia in critically ill patients. For survival benefit, SUL appears to be the best treatment followed by HD SUL, FOS + IV COL, IH COL + IV COL, HD TIG, and IV COL therapy, in numerical order.

The life cycles of six multi-center adaptive clinical trials focused on neurological emergencies developed for the Advancing Regulatory Science initiative of the National Institutes of Health and US Food and Drug Administration: Case studies from the Adaptive Designs Accelerating Promising Treatments Into Trials Project

OBJECTIVES

Clinical trials are complicated, expensive, time-consuming, and frequently do not lead to discoveries that improve the health of patients with disease. Adaptive clinical trials have emerged as a methodology to provide more flexibility in design elements to better answer scientific questions regarding whether new treatments are efficacious. Limited observational data exist that describe the complex process of designing adaptive clinical trials. To address these issues, the Adaptive Designs Accelerating Promising Treatments Into Trials project developed six, tailored, flexible, adaptive, phase-III clinical trials for neurological emergencies, and investigators prospectively monitored and observed the processes. The objective of this work is to describe the adaptive design development process, the final design, and the current status of the adaptive trial designs that were developed.

METHODS

To observe and reflect upon the trial development process, we employed a rich, mixed methods evaluation that combined quantitative data from visual analog scale to assess attitudes about adaptive trials, along with in-depth qualitative data about the development process gathered from observations.

RESULTS

The Adaptive Designs Accelerating Promising Treatments Into Trials team developed six adaptive clinical trial designs. Across the six designs, 53 attitude surveys were completed at baseline and after the trial planning process completed. Compared to baseline, the participants believed significantly more strongly that the adaptive designs would be accepted by National Institutes of Health review panels and non-researcher clinicians. In addition, after the trial planning process, the participants more strongly believed that the adaptive design would meet the scientific and medical goals of the studies.

CONCLUSION

Introducing the adaptive design at early conceptualization proved critical to successful adoption and implementation of that trial. Involving key stakeholders from several scientific domains early in the process appears to be associated with improved attitudes towards adaptive designs over the life cycle of clinical trial development.

Bayesian dose selection design for a binary outcome using restricted response adaptive randomization

Background

In phase II trials, the most efficacious dose is usually not known. Moreover, given limited resources, it is difficult to robustly identify a dose while also testing for a signal of efficacy that would support a phase III trial. Recent designs have sought to be more efficient by exploring multiple doses through the use of adaptive strategies. However, the added flexibility may potentially increase the risk of making incorrect assumptions and reduce the total amount of information available across the dose range as a function of imbalanced sample size.

Methods

To balance these challenges, a novel placebo-controlled design is presented in which a restricted Bayesian response adaptive randomization (RAR) is used to allocate a majority of subjects to the optimal dose of active drug, defined as the dose with the lowest probability of poor outcome. However, the allocation between subjects who receive active drug or placebo is held constant to retain the maximum possible power for a hypothesis test of overall efficacy comparing the optimal dose to placebo. The design properties and optimization of the design are presented in the context of a phase II trial for subarachnoid hemorrhage.

Results

For a fixed total sample size, a trade-off exists between the ability to select the optimal dose and the probability of rejecting the null hypothesis. This relationship is modified by the allocation ratio between active and control subjects, the choice of RAR algorithm, and the number of subjects allocated to an initial fixed allocation period. While a responsive RAR algorithm improves the ability to select the correct dose, there is an increased risk of assigning more subjects to a worse arm as a function of ephemeral trends in the data. A subarachnoid treatment trial is used to illustrate how this design can be customized for specific objectives and available data.

Conclusions

Bayesian adaptive designs are a flexible approach to addressing multiple questions surrounding the optimal dose for treatment efficacy within the context of limited resources. While the design is general enough to apply to many situations, future work is needed to address interim analyses and the incorporation of models for dose response.

Impact of adaptation algorithm, timing, and stopping boundaries on the performance of Bayesian response adaptive randomization in confirmative trials with a binary endpoint

Despite the concerns of time trend in subject profiles, the use of Bayesian response adaptive randomization (BRAR) in large multicenter phase 3 confirmative trials has been reported in recent years, motivated by the potential benefits in subject ethics and/or trial efficiency. However three issues remain unclear to investigators: 1) among several BRAR algorithms, how to choose one for the specific trial setting; 2) when to start and how frequently to update the allocation ratio; and 3) how to choose the interim analyses stopping boundaries to preserve the type 1 error. In this paper, three commonly used BRAR algorithms are evaluated based on type 1 error, power, sample size, the proportion of subjects assigned to the better performing arm, and the total number of failures, under two specific trial settings and different allocation ratio update timing and frequencies. Simulation studies show that for two-arm superiority trials, none of the three BRAR algorithms has predominant benefits in both patient ethics and trial efficiency when compared to fixed equal allocation design. For a specific trial aiming to identify the best or the worst among three treatments, a properly selected BRAR algorithm and its implementation parameters are able to gain ethical and efficiency benefits simultaneously. Although the simulation results come from a specific trial setting, the methods described in this paper are generally applicable to other trials.

Changing the default for tobacco-cessation treatment in an inpatient setting: study protocol of a randomized controlled trial

BACKGROUND

Most health care providers do not treat tobacco dependence routinely. This may in part be due to the treatment "default." Current treatment guidelines recommend that providers (1) ask patients if they are willing to quit and (2) provide cessation-focused medications and counseling only to smokers who state that they are willing to quit. The default is that patients have to "opt in" to receive cessation assistance: providers ask smokers if they are willing to quit, and only offer medications and cessation support to those who say "yes." This drastically limits the reach of cessation services because, at any given encounter, only one in three smokers say that they are ready to quit. The objective of this study is to determine the impact of providing all smokers with tobacco-cessation treatment unless they refuse it (OPT OUT) versus current practice-screening for readiness and only offering treatment to smokers who say they are ready to quit (OPT IN).

METHODS

This individually randomized clinical trial is conducted in a tertiary-care hospital. We will conduct the trial among up to 1000 randomly selected hospitalized smokers to determine the population impact of changing the treatment default, identify mediators of outcome, and determine the cost-effectiveness of this new, highly proactive approach. This is a population-based study that targets an endpoint of vital interest; applies minimal eligibility criteria to broaden generalizability; and utilizes hospital staff for interventions to ensure long-term sustainability. The study employs delayed consent and an innovative Bayesian adaptive design to evaluate a major shift in our approach to care. If effective, this change would expand the reach of tobacco-cessation treatment from 30% to 100% of smokers.

DISCUSSION

Regardless of outcome, the trial will provide a model of how to alter and evaluate the impact of health care defaults. If OPT OUT proves to be more effective, it will expand the population eligible for cessation treatment by over 300%. It will also simplify the tobacco-cessation treatment algorithm, and relieve busy health care providers of the burden of evaluating readiness to quit.

TRIAL REGISTRATION

Clinical Trials Registration, ID: NCT02721082 . Registered on 22 March 2016.

Does accounting for seizure frequency variability increase clinical trial power?

OBJECTIVE

Seizure frequency variability is associated with placebo responses in randomized controlled trials (RCT). Increased variability can result in drug misclassification and, hence, decreased statistical power. We investigated a new method that directly incorporated variability into RCT analysis, Z_V.

METHODS

Two models were assessed: the traditional 50%-responder rate (RR50), and the variability-corrected score, Z_V. Each predicted seizure frequency upper and lower limits using prior seizures. Accuracy was defined as percentage of time-intervals when the observed seizure frequencies were within the predicted limits. First, we tested the Z_V method on three datasets (SeizureTracker: n=3016, Human Epilepsy Project: n=107, and NeuroVista: n=15). An additional independent SeizureTracker validation dataset was used to generate a set of 200 simulated trials each for 5 different sample sizes (total N=100 to 500 by 100), assuming 20% dropout and 30% drug efficacy. "Power" was determined as the percentage of trials successfully distinguishing placebo from drug (p<0.05).

RESULTS

Prediction accuracy across datasets was, Z_V: 91-100%, RR50: 42-80%. Simulated RCT Z_V analysis achieved >90% power at N=100 per arm while RR50 required N=200 per arm.

SIGNIFICANCE

Z_V may increase the statistical power of an RCT relative to the traditional RR50.

A conceptual model for the development process of confirmatory adaptive clinical trials within an emergency research network

BACKGROUND

Adaptive clinical trials use accumulating data from enrolled subjects to alter trial conduct in pre-specified ways based on quantitative decision rules. In this research, we sought to characterize the perspectives of key stakeholders during the development process of confirmatory-phase adaptive clinical trials within an emergency clinical trials network and to build a model to guide future development of adaptive clinical trials.

METHODS

We used an ethnographic, qualitative approach to evaluate key stakeholders' views about the adaptive clinical trial development process. Stakeholders participated in a series of multidisciplinary meetings during the development of five adaptive clinical trials and completed a Strengths-Weaknesses-Opportunities-Threats questionnaire. In the analysis, we elucidated overarching themes across the stakeholders' responses to develop a conceptual model.

RESULTS

Four major overarching themes emerged during the analysis of stakeholders' responses to questioning: the perceived statistical complexity of adaptive clinical trials and the roles of collaboration, communication, and time during the development process. Frequent and open communication and collaboration were viewed by stakeholders as critical during the development process, as were the careful management of time and logistical issues related to the complexity of planning adaptive clinical trials.

CONCLUSION

The Adaptive Design Development Model illustrates how statistical complexity, time, communication, and collaboration are moderating factors in the adaptive design development process. The intensity and iterative nature of this process underscores the need for funding mechanisms for the development of novel trial proposals in academic settings.

A Bayesian response-adaptive trial in tuberculosis: The endTB trial

PURPOSE

To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis.

METHODS

We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios.

RESULTS

When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm.

CONCLUSION

Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.

Attitudes and opinions regarding confirmatory adaptive clinical trials: a mixed methods analysis from the Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT) project

Background

Adaptive designs have been increasingly used in the pharmaceutical and device industries, but adoption within the academic setting has been less widespread — particularly for confirmatory phase trials. We sought to understand perceptions about understanding, acceptability, and scientific validity of adaptive clinical trials (ACTs).

Methods

We used a convergent mixed methods design using survey and mini-focus group data collection procedures to elucidate attitudes and opinions among “trial community” stakeholders regarding understanding, acceptability, efficiency, scientific validity, and speed of discovery with adaptive designs. Data were collected about various aspects of ACTs using self-administered surveys (paper or Web-based) with visual analog scales (VASs) with free text responses and with mini-focus groups of key stakeholders. Participants were recruited as part of an ongoing NIH/FDA-funded research project exploring the incorporation of ACTs into an existing NIH network that focuses on confirmatory phase clinical trials in neurological emergencies. “Trial community” representatives, namely, clinical investigators, biostatisticians, NIH officials, and FDA scientists involved in the planning of four clinical trials, were eligible to participate. In addition, recent and current members of a clinical trial-oriented NIH study section were also eligible.

Results

A total of 76 stakeholders completed the survey (out of 91 who were offered it, response rate 84 %). While the VAS attitudinal data showed substantial variability across respondents about acceptability and understanding of ACTs by various constituencies, respondents perceived clinicians to be less likely to understand ACTs and that ACTs probably would increase the efficiency of discovery. Textual and focus group responses emerged into several themes that enhanced understanding of VAS attitudinal data including the following: acceptability of adaptive designs depends on constituency and situation; there is variable understanding of ACTs (limited among clinicians, perceived to be higher at FDA); views about the potential for efficiency depend on the situation and implementation. Participants also frequently mentioned a need for greater education within the academic community. Finally, the empiric, non-quantitative selection of treatments for phase III trials based on limited phase II trials was highlighted as an opportunity for improvement and a potential explanation for the high number of neutral confirmatory trials.

Conclusions

These data show considerable variations in attitudes and beliefs about ACTs among trial community representatives. For adaptive trials to be fully considered when appropriate and for the research enterprise to realize the full potential of adaptive designs will likely require extensive experience and trust building within the trial community.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1493-z) contains supplementary material, which is available to authorized users.

Hyperbaric oxygen brain injury treatment (HOBIT) trial: a multifactor design with response adaptive randomization and longitudinal modeling

The goals of phase II clinical trials are to gain important information about the performance of novel treatments and decide whether to conduct a larger phase III trial. This can be complicated in cases when the phase II trial objective is to identify a novel treatment having several factors. Such multifactor treatment scenarios can be explored using fixed sample size trials. However, the alternative design could be response adaptive randomization with interim analyses and additionally, longitudinal modeling whereby more data could be used in the estimation process. This combined approach allows a quicker and more responsive adaptation to early estimates of later endpoints. Such alternative clinical trial designs are potentially more powerful, faster, and smaller than fixed randomized designs. Such designs are particularly challenging, however, because phase II trials tend to be smaller than subsequent confirmatory phase III trials. The phase II trial may need to explore a large number of treatment variations to ensure that the efficacy of optimal clinical conditions is not overlooked. Adaptive trial designs need to be carefully evaluated to understand how they will perform and to take full advantage of their potential benefits. This manuscript discusses a Bayesian response adaptive randomization design with a longitudinal model that uses a multifactor approach for predicting phase III study success via the phase II data. The approach is based on an actual clinical trial design for the hyperbaric oxygen brain injury treatment trial. Specific details of the thought process and the models informing the trial design are provided. Copyright © 2016 John Wiley & Sons, Ltd.

Mass weighted urn design--A new randomization algorithm for unequal allocations

Unequal allocations have been used in clinical trials motivated by ethical, efficiency, or feasibility concerns. Commonly used permuted block randomization faces a tradeoff between effective imbalance control with a small block size and accurate allocation target with a large block size. Few other unequal allocation randomization designs have been proposed in literature with applications in real trials hardly ever been reported, partly due to their complexity in implementation compared to the permuted block randomization. Proposed in this paper is the mass weighted urn design, in which the number of balls in the urn equals to the number of treatments, and remains unchanged during the study. The chance a ball being randomly selected is proportional to the mass of the ball. After each treatment assignment, a part of the mass of the selected ball is re-distributed to all balls based on the target allocation ratio. This design allows any desired optimal unequal allocations be accurately targeted without approximation, and provides a consistent imbalance control throughout the allocation sequence. The statistical properties of this new design is evaluated with the Euclidean distance between the observed treatment distribution and the desired treatment distribution as the treatment imbalance measure; and the Euclidean distance between the conditional allocation probability and the target allocation probability as the allocation predictability measure. Computer simulation results are presented comparing the mass weighted urn design with other randomization designs currently available for unequal allocations.