Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients

Abstract

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.

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

“…Response adaptive randomization trials have grown rapidly in the last few decades, especially those utilizing Bayesian methods for analyses. Bayesian adaptive trials can be challenging to design and more difficult to implement than a fixed trial design [ 25 , 26 ]. Even though adaptive trials have grown rapidly, the literature detailing the execution and processes involved in running these trials is severely lacking.…”

Adaptive trials in stroke: Current use & future directions

“…STEP is a randomised, multi-factorial adaptive platform trial (NCT06289985) that aims to study which ischaemic stroke patients should be treated with endovascular therapy and how to optimise that care further, in terms of newer devices or techniques, adjunctive therapies, newer diagnostic strategies, and systems of care delivery 39 The first domain, STEP Endovascular Thrombectomy Indication Expansion (STEP-EVT Indication Expansion, NCT06289985), will assess endovascular thrombectomy versus medical management in acute ischaemic stroke patient subgroups with low NIHSS and/or medium and distal vessel arterial occlusions. Patients will be concurrently randomised to other domains, as they become funded by the NIH and initiated within the platform, and the model will estimate the effects of other domains using that domain's specific modelling.…”

“…STEP is a randomised, multi-factorial adaptive platform trial (NCT06289985) that aims to study which ischaemic stroke patients should be treated with endovascular therapy and how to optimise that care further, in terms of newer devices or techniques, adjunctive therapies, newer diagnostic strategies, and systems of care delivery 39 . The primary outcome is global functional outcome measured by the utility-weighted modified Rankin Scale assessed at 90 days.…”

“…During trial conduct, adaptations that occur at prespecified interim analyses (e.g., sample size reestimation, multi-arm, multi-stage) may require more intensive ongoing input than adaptations that are managed on a day-to-day basis by software that implement methods to adaptively randomise individual participants (e.g., covariate-adjusted, response-adaptive). However, such randomisation methods often require bespoke software 36, 39, 44 to be developed prior to trial start for which the code is generally not publicly available 45 . It is important to note such development skills are different to those required to perform trial design and analysis work reflecting a unique workforce requirement for some adaptive designs.…”

How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand‐Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic

“…8 Although these trials have shown promising results, there is still a need to assess their efficiency and effectiveness. Although many review articles have focused on the definition, design, statistical methods, conduct, and reporting of APTs, 5,6,[9][10][11][12][13] little is known about the quantitative efficiency of APTs compared with multiple stand-alone trials, especially in terms of total development time (e.g., the study period from first patient-in to final data analysis for primary efficacy end point) and total sample sizes for development. Because these factors have a significant impact on the feasibility of a clinical trial, the quantitative difference between APTs and stand-alone trials would provide useful guidance on the appropriate choice between the two trial designs for researchers, clinicians, and policymakers during a pandemic.…”