Adaptive Platform Trial Operations Special Interest Group: Trial Summaries
The Adaptive Platform Trial Operations Special Interest Group (APTO-SIG) was established in 2022 and aims to provide relevant and informative guidance on mitigating identified operational challenges of adaptive platform trials. The APTO-SIG provides a forum for members to share information and collaborate in problem solving, to increase awareness of different adaptive platform trials, and to develop relevant resources.
The Group’s main focus is to:
– Share common key challenges and opportunities for adaptive platform trial operations
– Provide and explore best practice knowledge and resource opportunities
– Explore ways to support and foster growth of the adaptive platform trial operations workforce
– Provide representation and connection with ACTA’s Innovative Trials Working Group and Statistics in Trials Special Interest Group
The group comprises of membership representing 16 adaptive platform trials in Australia and is seeking to expand representation to all funded APTs in Australia. A list of currently represented adaptive platform trials and their trial summaries can be found below.
Adaptive Platform Trial Summaries Register
Please find below a list of adaptive platform trials currently represented in our group membership, and their trial summaries.
ASCOT-ADAPT: The Australasian COVID-19 Trial
The SARS-CoV-2 virus has caused over 1,000,000 deaths globally due to COVID-19. The global response is working to accelerate diagnostics, vaccines, and therapeutics. Despite some promising treatments (e.g., remdesivir and dexamethasone), more effective therapies are needed. ASCOT-ADAPT is a multi-centre, randomised, adaptive platform clinical trial to assess clinical, virological, and immunological outcomes in patients with SARS-CoV-2 infection.
Patients are initially admitted to hospital with a positive SARS-CoV-2 PCR test. During screening patients must meet all core inclusion/exclusion eligibility criteria, and consent to trial domains and interventions. Following screening and consent, patients are randomised to all eligible and available domains. The three ASCOT-ADAPT domains are Antiviral, Therapeutic Antibody, and Anticoagulation, these domains closed on August 2022, February 2021, and April 2022, respectively. The ASCOT-ADAPT domains include the following interventions:
Antiviral Domain – Usual care vs. Nafamostat + Usual care.
Therapeutic Antibody Domain – No antibody therapy against SARS-CoV-2 vs. Convalescent Plasma.
Anticoagulation Domain – Prophylactic dose vs. Intermediate dose vs. Prophylactic dose + Aspirin (closed in September 2021) vs. Therapeutic anticoagulation (opened September 2021).
The primary outcome measure is death from any cause or requirement of new intensive respiratory support (invasive or non-invasive ventilation) or vasopressor / inotropic support in the 28 days after randomisation.
Statistical inference is based on the analysis of accumulated trial data using pre-specified Bayesian models at regularly scheduled analyses. The Bayesian adaptive platform incorporates critical design features which enable enhanced efficiency and implementation of findings, including:
A pragmatic trial design, embedded within routine care
Implementation of universal trial master protocol with multiple domains and therapeutic questions
Frequent interim analyses via Bayesian Hierarchical Model, enabling domain conclusion once robust statistical confidence is met
Response adaptive randomisation, which involves updating patient allocation based on interim results
ASCOT-ADAPT aims to identify the regimen (combination of interventions) associated with the highest chance of survival, free of advanced respiratory support or vasopressor / inotropic support at 28 days after randomisation, in adults hospitalized with COVID-19 but not requiring ICU-level care at baseline.
AuTOMATIC trial: a multi-arm Bayesian adaptive randomized controlled trial of text messaging to improve childhood immunisation coverage
While most Australian children are vaccinated, delays in vaccination can put them at risk from preventable infections. Widespread mobile phone ownership in Australia could allow automated short message service (SMS) reminders to be used as a low-cost strategy to effectively ‘nudge’ parents towards vaccinating their children on time.
AuTOMATIC is an adaptive randomised trial which aims to both evaluate and optimise the use of SMS reminders for improving the timely vaccination of children at primary care clinics across Australia. The trial will utilize high levels of digital automation to effect, including eligibility assessment, randomisation, delivery of intervention, data extraction and analysis, thereby allowing healthcare-embedded trial delivery. Up to 10,000 parents attending participating primary care clinics will be randomised to one of 12 different active SMS vaccine reminder content and timing arms or usual practice only (no SMS reminder). The primary outcome is vaccine receipt within 28 days of the scheduled date for the index vaccine (the first scheduled vaccine after randomisation). Secondary analyses will assess receipt and timeliness for all vaccine occasions in all children. Regular scheduled analyses will be performed using Bayesian inference and pre-specifed trial decision rules, enabling response adaptive randomisation, suspension of any poorly performing arms and early stopping if a single best message is identified.
This study will aim to optimise SMS reminders for childhood vaccination in primary care clinics, directly comparing alternative message framing and message timing. Methods and findings from this study will help to inform strategies for implementing reminders and embedding analytics in primary health care settings.
BEAT-COVID-19: A BayEsian Adaptive platform, randomised controlled Trial to evaluate the efficacy and safety of interventions for COVID-19
Coronavirus disease 2019 (COVID-19) is a recent and one of the deadliest examples of cross-species viral transmission affecting the human population. By the end of 2022, COVID-19 has resulted in more than 665 million confirmed cases and 6.7 million confirmed deaths worldwide. The range of severity of disease is broad, ranging from no symptoms, through to respiratory failure, and death.
The COVID-19 pandemic has demonstrated unprecedented challenges in terms of healthcare and research delivery. As the clinical management of COVID-19 shifted from the hospital to community setting, the unmet need for an evidence base within community COVID-19 has unfolded.
BEAT COVID-19 is an investigator initiated, randomised controlled, adaptive platform trial, intended as a durable framework for delivering decentralised, community-based research with an aim to accelerate the assessment of interventions for preventing hospitalisation and death in adults with confirmed COVID-19 managed in the community. The study has initiated with a single domain evaluating an oral corticosteroid inhaler versus Placebo, recruiting its first participant on 16 March 2022 and has since recruited 100+ participants. Utilisation of a platform design allows adaption in time, for the study to integrate additional interventions as they become suitable for evaluation within the setting. To date there have not been any additional domains added to the platform. An independent Candidate Intervention Expert Committee will assess potential new interventions for integration into the platform.
The primary objective is to assess the comparative effectiveness and safety of a range of interventions in reducing the incidence of hospital admission or death within 28 days of randomisation, in community-based adults with -confirmed SARS-CoV-2 infection.
BEAT-BK: An adaptive, randomised controlled trial to treat polymoavirus infections (BKPyV) in kidney and simultaneous kidney pancreas transplant recipients
BEAT-Calci: Better Evidence And Translation for Calciphylaxis
BEAT CF: Bayesian Evidence Adaptive Treatment of Cystic Fibrosis
FORMaT: Finding the Optimal Regimen for Mycobacterium abscessus Treatment
M-FIT: Structured exercise program to reduce fatigue in patients receiving dialysis: an adaptive trial
Optimum: OPTimising IMmunisation Using Mixed schedules
The ORVAC Trial: Optimising Rotavirus Vaccine in Aboriginal Children
PICOBOO: The Platform trial In COVID-19 priming and BOOsting.
PICOBOO is a pragmatic Bayesian Adaptive, Randomised Controlled trial. Immunocompetent children and adults will receive a COVID-19 vaccine(s) authorised for use (including emergency use authorisation) by the Therapeutic Goods Administration (TGA) or equivalent regulatory authority. Allocation of COVID-19 vaccine to participants will be centrally determined using computer generated random sequences according to the participant’s stratum. Equal assignment probabilities will be used for the COVID-19 vaccine interventions.
Aims of the study are to generate evidence to compare the:
1. protective immune responses to alternative COVID-19 vaccines.
2. reactogenicity of alternative COVID-19 vaccines.
3. safety of alternative COVID-19 vaccines and schedules.
The primary objective is for Australians, eligible for enrolment, stratified by prior vaccination history and age cohort, to generate high-quality evidence of the immunogenicity and reactogenicity of alternative COVID-19 vaccination strategies against SARS-CoV-2.
The primary outcome, is the concentration of anti-spike SARS-CoV-2 IgG antibodies against SARS-CoV-2 measured ~28 days after receipt of the assigned COVID-19 vaccine and summarised as the geometric mean concentration (GMC), estimated for each COVID-19 vaccine in each stratum.
Analysis is through Bayesian Hierarchical models.
HREC Name: Child and Adolescent Health Service HREC (Perth) – HREA Link to ANZCTR registration: Australian New Zealand Clinical Trials Registry (ACTRN12622000238774) Trial contact email: PICOBOO@telethonkids.org.au Trial website:
REMAP-CAP (Randomized, Embedded, Multifactorial Adaptive Platform Trial for Community-Acquired Pneumonia)
Respiratory infections that are of sufficient severity to require admission to hospital are associated with substantial mortality. Hospitalized patients with respiratory infections will receive therapy that consists of a combination of multiple different treatments, however current conventional clinical trial methods used to assess the efficacy of treatments for respiratory infections generally compare two treatment options in a series of separate and sequential trials. This takes an inordinate length of time to study all available treatment options and does not allow for the evaluation of interactions between treatment options. In addition, with each trial, there is a risk of an indeterminate result.
REMAP-CAP utilizes a number of novel trial design features to allow for the efficient evaluation of multiple interventions simultaneously; avoid indeterminate results; and adapt seamlessly over time to add or remove interventions, to answer emerging clinical questions including evaluating candidate interventions in the event of a respiratory pandemic.
REMAP-CAP enrols hospitalised patients with respiratory tract infections using a design known as a REMAP, which is a type of adaptive platform trial. Within this REMAP, eligible participants will be randomised to receive one intervention in each of one or more “domains” of therapy. The adaptive design allows new interventions to be added or removed over time. Routine analyses are performed on accumulating trial data using a Bayesian hierarchical model, with results reported once pre-defined statistical thresholds are reached.
The primary objective of this REMAP is, for patients hospitalized with respiratory tract infection, to identify the effect of a range of interventions to improve patient outcomes.
HREC name: Sydney Local Health District – RPAH Zone HREC Link to ANZCTR registration: ClinicalTrials.gov reference: NCT02735707 Trial Contact email: firstname.lastname@example.org Trial website link: www.remapcap.org