Adaptive Platform Trial Research Articles

Analysis of adaptive platform trials using a network approach.

BackgroundAdaptive platform trials allow randomized controlled comparisons of multiple treatments using a common infrastructure and the flexibility to adapt key design features during the study. Nonetheless, they have been criticized due to the potential for time trends in the underlying risk level of the population. Such time trends lead to confounding between design features and risk level, which may introduce bias favoring one or more treatments. This is particularly true when experimental treatments are not all randomized during the same time period as the control, leading to the potential for bias from non-concurrent controls.MethodsTwo analysis methods addressing this bias are stratification and adjustment. Stratification uses only comparisons between treatment cohorts randomized during identical time periods and does not use non-concurrent randomizations. Adjustment uses a modeled analysis including time period adjustment, allowing all data to be used, even from periods without concurrent randomization. We show that these competing approaches may be embedded in a common framework using network meta-analysis principles. We interpret the stages between adaptations in a platform trial as separate fixed design trials. This allows platform trials to be viewed as networks of direct randomized comparisons and indirect non-randomized comparisons. Network meta-analysis methodology can be re-purposed to aggregate the total information from a platform trial and to transparently decompose this total information into direct randomized evidence and indirect non-randomized evidence. This allows sensitivity to indirect information to be assessed and the two analysis methods to be clearly compared.ResultsSimulations of platform trials were analyzed using a network approach implemented in the netmeta package in R. The results demonstrated bias of unadjusted methods in the presence of time trends in risk level. Adjustment and stratification were both unbiased when direct evidence and indirect evidence were consistent. Network tests of inconsistency may be used to diagnose inconsistency when it exists. In an illustrative network analysis of one of the treatment comparisons from the STAMPEDE platform trial in metastatic prostate cancer, indirect comparisons using non-concurrent controls were inconsistent with the information from direct randomized comparisons. This supports the primary analysis approach of STAMPEDE, which used only direct randomized comparisons.ConclusionNetwork meta-analysis provides a natural methodology for analyzing the network of direct and indirect treatment comparisons from a platform trial. Such analyses provide transparent separation of direct and indirect evidence, allowing assessment of the impact of non-concurrent controls. We recommend time-stratified analysis of concurrently controlled comparisons for primary analyses, with time-adjusted analyses incorporating non-concurrent controls reserved for secondary analyses. However, regardless of which methodology is used, a network analysis provides a useful supplement to the primary analysis.

Inhaled budesonide for COVID-19 in people at high risk of complications in the community in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial

Inhaled budesonide for COVID-19 in people at high risk of complications in the community in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial

SY13-3 SCRUM-Japan adaptive platform trials to accelerate precision oncology innovations

Advances in precision oncology have highlighted the necessity for genotyping in advanced cancer patients to ensure appropriate therapy selection. However, pace of precision oncology innovations remains limited due to logistical realities of patient identification. Furthermore, because many targets are present in only a small fraction of patients, hundreds or even thousands of patients need to be screened. Compounding this identification barrier are challenges associated with traditional trial designs - such as high cost of genotyping, lengthy screening periods, and limited access to screening populations - which lead to increasingly low enrollment rates of otherwise eligible patients.

An adaptive platform trial for evaluating treatments in patients with life-threatening hemorrhage from traumatic injuries: Planning and execution.

An adaptive platform trial for evaluating treatments in patients with life-threatening hemorrhage from traumatic injuries: Planning and execution.

An adaptive platform trial for evaluating treatments in patients with life-threatening hemorrhage from traumatic injuries: Ethical and US regulatory considerations.

An adaptive platform trial for evaluating treatments in patients with life-threatening hemorrhage from traumatic injuries: Ethical and US regulatory considerations.

An adaptive platform trial for evaluating treatments in patients with life-threatening hemorrhage from traumatic injuries: Rationale and proposal.

An adaptive platform trial for evaluating treatments in patients with life-threatening hemorrhage from traumatic injuries: Rationale and proposal.

GBM AGILE: A global, phase 2/3 adaptive platform trial to evaluate multiple regimens in newly diagnosed and recurrent glioblastoma.

TPS2078 Background: GBM AGILE (Glioblastoma Adaptive, Global, Innovative Learning Environment) is a biomarker based, multi-arm, international, seamless Phase 2/3 Response Adaptive Randomization platform trial designed to rapidly identify experimental therapies that improve overall survival and confirm efficacious experimental therapies and associated biomarker signatures to support new drug approvals and registration. GBM AGILE is a collaboration between academic investigators, patient organizations and industry to support new drug applications for newly diagnosed and recurrent GBM. With its adaptable structure, GBM AGILE has continued trial activation, inclusion of new investigational therapies, and enrollment globally through the challenges of a global pandemic. Methods: The primary objective of GBM AGILE is to identify therapies that effectively improve the overall survival in patients with ND or recurrent GBM. Bayesian response adaptive randomization is used within subtypes of the disease to assign participants to investigational arms based on their performance. Operating under a Master Protocol, GBM AGILE allows multiple drugs from different pharmaceutical/biotech companies to be evaluated simultaneously and/or over time against a common control. New experimental therapies are added as new information about promising new drugs is identified while other therapies are removed as they complete their evaluation. The master protocol/ trial infrastructure includes efficiencies through an adaptive trial design, shared control arm and operational processes such as risk-based monitoring and enhanced remote activities. GBM AGILE has screened over 1000 patients and enrollment rates are 3 to 4 times greater than traditional GBM trials, with active sites averaging 0.75 to 1 patients/sites/month. While enrollment had an initial dip during the early stages of the pandemic (April-May, 2020), with flexible processes including remote based monitoring, minimizing in person visits, and remote provision of IMP, the enrollment rebounded by June, 2020. Through the use of improved and efficient processes allowed within a master protocol/adaptive platform trial infrastructure, GBM AGILE has been seamlessly operating a global trial during a global pandemic. Clinical trial information: NCT03970447.

I-SPY COVID adaptive platform trial for COVID-19 acute respiratory failure: rationale, design and operations

IntroductionThe COVID-19 pandemic brought an urgent need to discover novel effective therapeutics for patients hospitalised with severe COVID-19. The Investigation of Serial studies to Predict Your Therapeutic Response with Imaging...

A Bayesian model with application for adaptive platform trials having temporal changes.

Temporal changes exist in clinical trials. Over time, shifts in patients' characteristics, trial conduct, and other features of a clinical trial may occur. In typical randomized clinical trials, temporal effects, that is, the impact of temporal changes on clinical outcomes and study analysis, are largely mitigated by randomization and usually need not be explicitly addressed. However, temporal effects can be a serious obstacle for conducting clinical trials with complex designs, including the adaptive platform trials that are gaining popularity in recent medical product development. In this paper, we introduce a Bayesian robust prior for mitigating temporal effects based on a hidden Markov model, and propose a particle filtering algorithm for computation. We conduct simulation studies to evaluate the performance of the proposed method and provide illustration examples based on trials of Ebola virus disease therapeutics and hemostat in vascularsurgery.

Effect of Early Treatment with Ivermectin among Patients with Covid-19

BackgroundThe efficacy of ivermectin in preventing hospitalization or extended observation in an emergency setting among outpatients with acutely symptomatic coronavirus disease 2019 (Covid-19), the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is unclear.MethodsWe conducted a double-blind, randomized, placebo-controlled, adaptive platform trial involving symptomatic SARS-CoV-2–positive adults recruited from 12 public health clinics in Brazil. Patients who had had symptoms of Covid-19 for up to 7 days and had at least one risk factor for disease progression were randomly assigned to receive ivermectin (400 μg per kilogram of body weight) once daily for 3 days or placebo. (The trial also involved other interventions that are not reported here.) The primary composite outcome was hospitalization due to Covid-19 within 28 days after randomization or an emergency department visit due to clinical worsening of Covid-19 (defined as the participant remaining under observation for >6 hours) within 28 days after randomization.ResultsA total of 3515 patients were randomly assigned to receive ivermectin (679 patients), placebo (679), or another intervention (2157). Overall, 100 patients (14.7%) in the ivermectin group had a primary-outcome event, as compared with 111 (16.3%) in the placebo group (relative risk, 0.90; 95% Bayesian credible interval, 0.70 to 1.16). Of the 211 primary-outcome events, 171 (81.0%) were hospital admissions. Findings were similar to the primary analysis in a modified intention-to-treat analysis that included only patients who received at least one dose of ivermectin or placebo (relative risk, 0.89; 95% Bayesian credible interval, 0.69 to 1.15) and in a per-protocol analysis that included only patients who reported 100% adherence to the assigned regimen (relative risk, 0.94; 95% Bayesian credible interval, 0.67 to 1.35). There were no significant effects of ivermectin use on secondary outcomes or adverse events.ConclusionsTreatment with ivermectin did not result in a lower incidence of medical admission to a hospital due to progression of Covid-19 or of prolonged emergency department observation among outpatients with an early diagnosis of Covid-19. (Funded by FastGrants and the Rainwater Charitable Foundation; TOGETHER ClinicalTrials.gov number, NCT04727424.)

Colchicine for COVID-19 in the community (PRINCIPLE): a randomised, controlled, adaptive platform trial.

BackgroundColchicine has been proposed as a COVID-19 treatment.AimTo determine whether colchicine reduces time to recovery and COVID-19-related admissions to hospital and/or deaths among people in the community.Design and settingProspective, multicentre, open-label, multi-arm, randomised, controlled, adaptive platform trial (PRINCIPLE).MethodAdults aged ≥65 years or ≥18 years with comorbidities or shortness of breath, and unwell for ≤14 days with suspected COVID-19 in the community, were randomised to usual care, usual care plus colchicine (500 µg daily for 14 days), or usual care plus other interventions. The co-primary endpoints were time to first self-reported recovery and admission to hospital/death related to COVID-19, within 28 days, analysed using Bayesian models.ResultsThe trial opened on 2 April 2020. Randomisation to colchicine started on 4 March 2021 and stopped on 26 May 2021 because the prespecified time to recovery futility criterion was met. The primary analysis model included 2755 participants who were SARS-CoV-2 positive, randomised to colchicine (n = 156), usual care (n = 1145), and other treatments (n = 1454). Time to first self-reported recovery was similar in the colchicine group compared with usual care with an estimated hazard ratio of 0.92 (95% credible interval (CrI) = 0.72 to 1.16) and an estimated increase of 1.4 days in median time to self-reported recovery for colchicine versus usual care. The probability of meaningful benefit in time to recovery was very low at 1.8%. COVID-19-related admissions to hospital/deaths were similar in the colchicine group versus usual care, with an estimated odds ratio of 0.76 (95% CrI = 0.28 to 1.89) and an estimated difference of −0.4% (95% CrI = −2.7 to 2.4).ConclusionColchicine did not improve time to recovery in people at higher risk of complications with COVID-19 in the community.

Effect of Antiplatelet Therapy on Survival and Organ Support–Free Days in Critically Ill Patients With COVID-19

The efficacy of antiplatelet therapy in critically ill patients with COVID-19 is uncertain. To determine whether antiplatelet therapy improves outcomes for critically ill adults with COVID-19. In an ongoing adaptive platform trial (REMAP-CAP) testing multiple interventions within multiple therapeutic domains, 1557 critically ill adult patients with COVID-19 were enrolled between October 30, 2020, and June 23, 2021, from 105 sites in 8 countries and followed up for 90 days (final follow-up date: July 26, 2021). Patients were randomized to receive either open-label aspirin (n = 565), a P2Y12 inhibitor (n = 455), or no antiplatelet therapy (control; n = 529). Interventions were continued in the hospital for a maximum of 14 days and were in addition to anticoagulation thromboprophylaxis. The primary end point was organ support-free days (days alive and free of intensive care unit-based respiratory or cardiovascular organ support) within 21 days, ranging from -1 for any death in hospital (censored at 90 days) to 22 for survivors with no organ support. There were 13 secondary outcomes, including survival to discharge and major bleeding to 14 days. The primary analysis was a bayesian cumulative logistic model. An odds ratio (OR) greater than 1 represented improved survival, more organ support-free days, or both. Efficacy was defined as greater than 99% posterior probability of an OR greater than 1. Futility was defined as greater than 95% posterior probability of an OR less than 1.2 vs control. Intervention equivalence was defined as greater than 90% probability that the OR (compared with each other) was between 1/1.2 and 1.2 for 2 noncontrol interventions. The aspirin and P2Y12 inhibitor groups met the predefined criteria for equivalence at an adaptive analysis and were statistically pooled for further analysis. Enrollment was discontinued after the prespecified criterion for futility was met for the pooled antiplatelet group compared with control. Among the 1557 critically ill patients randomized, 8 patients withdrew consent and 1549 completed the trial (median age, 57 years; 521 [33.6%] female). The median for organ support-free days was 7 (IQR, -1 to 16) in both the antiplatelet and control groups (median-adjusted OR, 1.02 [95% credible interval {CrI}, 0.86-1.23]; 95.7% posterior probability of futility). The proportions of patients surviving to hospital discharge were 71.5% (723/1011) and 67.9% (354/521) in the antiplatelet and control groups, respectively (median-adjusted OR, 1.27 [95% CrI, 0.99-1.62]; adjusted absolute difference, 5% [95% CrI, -0.2% to 9.5%]; 97% posterior probability of efficacy). Among survivors, the median for organ support-free days was 14 in both groups. Major bleeding occurred in 2.1% and 0.4% of patients in the antiplatelet and control groups (adjusted OR, 2.97 [95% CrI, 1.23-8.28]; adjusted absolute risk increase, 0.8% [95% CrI, 0.1%-2.7%]; 99.4% probability of harm). Among critically ill patients with COVID-19, treatment with an antiplatelet agent, compared with no antiplatelet agent, had a low likelihood of providing improvement in the number of organ support-free days within 21 days. ClinicalTrials.gov Identifier: NCT02735707.

Clindamycin adjunctive therapy for severe Staphylococcus aureus treatment evaluation (CASSETTE)-an open-labelled pilot randomized controlled trial.

BackgroundCombination antibiotic therapy with an antitoxin agent, such as clindamycin, is included in some guidelines for severe, toxin-mediated Staphylococcus aureus infections. The evidence to support this practice is currently limited to in vitro, animal and observational human case-series data, with no previous randomized controlled trials (RCTs).ObjectivesThis pilot RCT aimed to determine the feasibility of conducting a clinical trial to examine if adjunctive clindamycin with standard therapy has greater efficacy than standard therapy alone for S. aureus infections.MethodsWe performed an investigator-initiated, open-label, multicentre, pilot RCT (ACTRN12617001416381p) in adults and children with severe S. aureus infections, randomized to standard antibiotic therapy with or without clindamycin for 7 days.ResultsOver 28 months, across nine sites, 127 individuals were screened and 34 randomized, including 11 children (32%). The primary outcome—number of days alive and free of systemic inflammatory response syndrome ≤14 days—was similar between groups: clindamycin (3 days [IQR 1–6]) versus standard therapy (4 days [IQR 0–8]). The 90 day mortality was 0% (0/17) in the clindamycin group versus 24% (4/17) in the standard therapy group. Secondary outcomes—microbiological relapse, treatment failure or diarrhoea—were similar between groups.ConclusionsAs the first clinical trial assessing adjunctive clindamycin for S. aureus infections, this study indicates feasibility and that adults and children can be incorporated into one trial using harmonized endpoints, and there were no safety concerns. The CASSETTE trial will inform the definitive S. aureus Network Adaptive Platform (SNAP) trial, which includes an adjunctive clindamycin domain and participants with non-severe disease.

From hydroxychloroquine to ivermectin: how unproven “cures” can go viral

From hydroxychloroquine to ivermectin: how unproven “cures” can go viral

Experiences of running a stratified medicine adaptive platform trial: Challenges and lessons learned from 10 years of the FOCUS4 trial in metastatic colorectal cancer.

Background:Complex innovative design trials are becoming increasingly common and offer potential for improving patient outcomes in a faster time frame. FOCUS4 was the first molecularly stratified trial in metastatic colorectal cancer and it remains one of the first umbrella trial designs to be launched globally. Here, we aim to describe lessons learned from delivery of the trial over the last 10 years.Methods:FOCUS4 was a Phase II/III molecularly stratified umbrella trial testing the safety and efficacy of targeted therapies in metastatic colorectal cancer. It used adaptive statistical methodology to decide which sub-trial should close early, and new therapies were added as protocol amendments. Patients with newly diagnosed metastatic colorectal cancer were registered, and central laboratory testing was used to stratify their tumour into molecular subtypes. Following 16 weeks of first-line therapy, patients with stable or responding disease were eligible for randomisation into either a molecularly stratified sub-trial (FOCUS4-B, C or D) or non-stratified FOCUS4-N. The primary outcome for all studies was progression-free survival comparing the intervention with active monitoring/placebo. At the close of the trial, feedback was elicited from all investigators through surveys and interviews and consolidated into a series of recommendations and lessons learned for the delivery of similar future trials.Results:Between January 2014 and October 2020, 1434 patients were registered from 88 UK hospitals. Of the 20 drug combinations that were explored for inclusion in the platform trial, three molecularly targeted sub-trials were activated: FOCUS4-D (February 2014–March 2016) evaluated AZD8931 in the BRAF-PIK3CA-RAS wildtype subgroup; FOCUS4-B (February 2016–July 2018) evaluated aspirin in the PIK3CA mutant subgroup and FOCUS4-C (June 2017–October 2020) evaluated adavosertib in the RAS+TP53 double mutant subgroup. FOCUS4-N was active throughout and evaluated capecitabine monotherapy versus a treatment break. A total of 361 (25%) registered patients were randomised into a sub-trial. Feedback on the experiences of delivery of FOCUS4 could be grouped into three main areas of challenge: funding/infrastructure, biomarker testing procedures and trial design efficiencies within which 20 recommendations are summarised.Conclusion:Adaptive stratified medicine platform studies are feasible in common cancers but present challenges. Our stakeholder feedback has helped to inform how these trial designs can succeed and answer multiple questions efficiently, providing resource is adequate.

Adaptive Platform Trials to Transform Amyotrophic Lateral Sclerosis Therapy Development.

Current therapeutic development in amyotrophic lateral sclerosis (ALS) relies on individual randomized clinical trials to test a specific investigational product in a single patient population. This approach has intrinsic limitations, including cost, time, and lack of flexibility. Adaptive platform trials represent a novel approach to investigate several interventions for a single disease in a continuous manner. Already in use in oncology, this approach is now being employed more often in neurology. Here, we describe a newly launched platform trial for ALS. The Healey ALS Platform Trial is testing multiple investigational products concurrently in people with ALS, with the goal of rapidly identifying novel treatments, biomarkers, and trial endpoints. ANN NEUROL 2022;91:165-175.

National Institute for Health and Care Research to fund new phase of platform trial to assess treatments for severe flu

A platform trial used to assess treatments for COVID-19 during the pandemic will, for the first time, look at treatments for people hospitalised with severe influenza. The ‘Randomised, embedded, multi-factorial, adaptive platform trial for community-acquired pneumonia‘ (REMAP-CAP) was originally established in 2016 by researchers from several countries to assess treatments for community-acquired pneumonia during pandemics. […]

Characteristics and trends of globally registered glioma clinical trials in the past 16 years.

Background:Advancement in the treatment of glioma has been vacant since temozolomide has proved its therapeutic value in glioblastoma in 2005.Aim:To help investigators understand the landscape of glioma clinical research, we analyzed the characteristics and trends of globally registered glioma trials in the past decades.Methods:This is a cross-sectional analysis of glioma trials registered on ClinicalTrials.gov between January 2006 and December 2021. Characteristics regarding phase, enrollment number, study design and type, funding source, tumor site, pathology, patient status, age of population, trial purpose, and participating country were abstracted, and chronological shifts were analyzed.Results:There were 1531 registered glioma trials involved 58 participating countries. The trial purpose concerning surgery, radiotherapy, chemotherapy, targeted therapy, tumor-treating fields, immunotherapy, other antiglioma therapy and non-antiglioma research trial accounts for 3.5%, 6.5%, 9.5%, 28.9%, 2.0%, 16.4%, 12.5%, and 20.6%, respectively. In the past 16 years, the numbers of chemotherapy and targeted therapy trials declined; tumor-treating fields and immune checkpoint inhibitor application trials sprang at the latter half period; Immunotherapy, other antiglioma therapy and non-antiglioma research trials escalated (all above ptrend < 0.005). The trend also showed the phased trials registered diminishingly and that the trials which focused on glioblastoma registered incrementally (those two ptrend < 0.05). Among 784 drug therapy trials, it was included 45 cytotoxic drugs, 186 targeted drugs, 19 immune checkpoint inhibitors, 78 other drugs, and five immunomodulatory drugs. Two trials belonged to Bayesian adaptive randomized design. By the end of December 2021, 309 trials had publications. Only everolimus and tumor-treating fields exhibited meaningful survival benefit in specific glioma patients in phase 3 trials.Conclusion:Meaningful effective treatments regarding drugs or methods for glioma were difficult to be found. Bayesian adaptive platform trials may accelerate clinical research in glioma. Development of novel treatment modalities for glioma is still challenged.

Launching a comparative effectiveness adaptive platform trial of monoclonal antibodies for COVID-19 in 21 days

Outpatient treatments that limit progression to severe coronavirus disease 2019 (COVID-19) are of vital importance to optimise patient outcomes and public health. Monoclonal antibodies (mAb) demonstrated ability to decrease hospitalizations in randomized, clinical trials. However, there are many barriers to mAb treatment such as patient access and clinician education. There are no data comparing efficacy or safety of available mAbs. We sought to rapidly launch an adaptive platform trial with the goals of enhancing access to treatment, regardless of geography and socioeconomic status, and evaluating comparative efficacy and safety of available mAbs. Within 21 days from idea genesis, we allocated mAb treatment to all patients within the context of this clinical trial. Within 2 months, we closed the gap of the likelihood of receiving mAb, conditional on background positivity rate, between Black and White patients (Black patients 0.238; White patients 0.241). We describe trial infrastructure, lessons learned, and future directions for a culture of learning while doing.

Design of a novel clinical trial of prehospital pediatric airway management.

Emergency Medical Services personnel are often the first to intervene in the care of critically ill children. Airway management is a fundamental step in prehospital resuscitation, yet there is significant variation in current prehospital airway management practices. Our objective is to present a methodologic approach to determine the optimal strategy for prehospital pediatric airway management. We describe the conceptual premise for the Pediatric Prehospital Airway Resuscitation Trial, a novel Bayesian adaptive sequential platform trial. We developed an innovative design to enable comparison of the three predominant prehospital pediatric airway techniques (bag-mask-ventilation, supraglottic airway insertion, and endotracheal intubation) in three distinct disease groups (cardiac arrest, major trauma, and other respiratory failure). We used a Bayesian statistical approach to provide flexible modeling that can adapt based on prespecified rules according to accumulating trial data with patient enrollment continuing until stopping rules are met. The approach also allows the comparison of multiple interventions in sequence across the different disease states. This Bayesian hierarchical model will be the primary analysis method for the Pediatric Prehospital Airway Resuscitation Trial. The model integrates information across subgroups, a technique known as "borrowing" to generate accurate global and subgroup-specific estimates of treatment effects and enables comparisons of airway intervention arms within the overarching trial. We will use this Bayesian hierarchical linear model that adjusts for subgroup to estimate treatment effects within each subgroup. The model will predict a patient-centered score of 30-day intensive care unit-free survival using arm, subgroup, and emergency medical services agency as predictors. The novel approach of Pediatric Prehospital Airway Resuscitation Trial will provide a feasible method to determine the optimal strategy for prehospital pediatric airway management and may transform the design of future prehospital resuscitation trials.