Project description:The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs.
Project description:Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
Project description:Children usually present with milder symptoms of COVID-19 as compared with adults. Supportive care alone is appropriate for most children with COVID-19. Antiviral therapy may be required for those with severe or critical diseases. Currently there has been a rapid development of vaccines globally to prevent COVID-19 and several vaccines are being evaluated in children and adolescents. Currently, only the Pfizer-BioNTech messenger RNA vaccine is approved for emergency authorization use in the pediatric population ages 16 years and older.
Project description:COVID-19, the greatest public health emergency of the 21st century, has affected 215 countries and territories around the world resulting in 15,151,738 confirmed cases and 621,121 deaths. The outbreak has continued at breakneck pace despite stringent public health measures, ravaging the global economy and causing profound human casualties. Vaccination is currently the best bet for the prevention of COVID-19. Still, in its absence, there has been considerable interest in repurposing existing therapeutic agents to reduce the severity of the illness and ease the burden on the already strained healthcare systems. This review outlines the current evidence regarding proposed treatments- experimental or repurposed, for COVID-19, and gives an insight into the clinical trial landscape for drugs as well as vaccines.
Project description:Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.
Project description:Racial/ethnic minority, low socioeconomic status, and rural populations are disproportionately affected by COVID-19. Developing and evaluating interventions to address COVID-19 testing and vaccination among these populations are crucial to improving health inequities. The purpose of this paper is to describe the application of a rapid-cycle design and adaptation process from an ongoing trial to address COVID-19 among safety-net healthcare system patients. The rapid-cycle design and adaptation process included: (a) assessing context and determining relevant models/frameworks; (b) determining core and modifiable components of interventions; and (c) conducting iterative adaptations using Plan-Do-Study-Act (PDSA) cycles. PDSA cycles included: Plan. Gather information from potential adopters/implementers (e.g., Community Health Center [CHC] staff/patients) and design initial interventions; Do. Implement interventions in single CHC or patient cohort; Study. Examine process, outcome, and context data (e.g., infection rates); and, Act. If necessary, refine interventions based on process and outcome data, then disseminate interventions to other CHCs and patient cohorts. Seven CHC systems with 26 clinics participated in the trial. Rapid-cycle, PDSA-based adaptations were made to adapt to evolving COVID-19-related needs. Near real-time data used for adaptation included data on infection hot spots, CHC capacity, stakeholder priorities, local/national policies, and testing/vaccine availability. Adaptations included those to study design, intervention content, and intervention cohorts. Decision-making included multiple stakeholders (e.g., State Department of Health, Primary Care Association, CHCs, patients, researchers). Rapid-cycle designs may improve the relevance and timeliness of interventions for CHCs and other settings that provide care to populations experiencing health inequities, and for rapidly evolving healthcare challenges such as COVID-19.
Project description:IntroductionClinical trial designs based on the assumption of independent observations are well established. Clustered clinical trial designs, where all observational units belong to a cluster and outcomes within clusters are expected to be correlated, have also received considerable attention. However, many clinical trials involve partially clustered data, where only some observational units belong to a cluster. Examples of such trials occur in neonatology, where participants include infants from both singleton and multiple births, and ophthalmology, where one or two eyes per participant may need treatment. Partial clustering can also arise in trials of group-based treatments (e.g. group education or counselling sessions) or treatments administered individually by a discrete number of health care professionals (e.g. surgeons or physical therapists), when this is compared to an unclustered control arm. Trials involving partially clustered data have received limited attention in the literature and the current lack of standardised terminology may be hampering the development and dissemination of methods for designing and analysing these trials.Methods and examplesIn this article, we present an overarching definition of partially clustered trials, bringing together several existing trial designs including those for group-based treatments, clustering due to facilitator effects and the re-randomisation design. We define and describe four types of partially clustered trial designs, characterised by whether the clustering occurs pre-randomisation or post-randomisation and, in the case of pre-randomisation clustering, by the method of randomisation that is used for the clustered observations (individual randomisation, cluster randomisation or balanced randomisation within clusters). Real life examples are provided to highlight the occurrence of partially clustered trials across a variety of fields. To assess how partially clustered trials are currently reported, we review published reports of partially clustered trials.DiscussionOur findings demonstrate that the description of these trials is often incomplete and the terminology used to describe the trial designs is inconsistent, restricting the ability to identify these trials in the literature. By adopting the definitions and terminology presented in this article, the reporting of partially clustered trials can be substantially improved, and we present several recommendations for reporting these trial designs in practice. Greater awareness of partially clustered trials will facilitate more methodological research into their design and analysis, ultimately improving the quality of these trials.
Project description:By January of 2023, the COVID-19 pandemic had led to a reported total of 6,700,883 deaths and 662,631,114 cases worldwide. To date, there have been no effective therapies or standardized treatment schemes for this disease; therefore, the search for effective prophylactic and therapeutic strategies is a primary goal that must be addressed. This review aims to provide an analysis of the most efficient and promising therapies and drugs for the prevention and treatment of severe COVID-19, comparing their degree of success, scope, and limitations, with the aim of providing support to health professionals in choosing the best pharmacological approach. An investigation of the most promising and effective treatments against COVID-19 that are currently available was carried out by employing search terms including "Convalescent plasma therapy in COVID-19" or "Viral polymerase inhibitors" and "COVID-19" in the Clinicaltrials.gov and PubMed databases. From the current perspective and with the information available from the various clinical trials assessing the efficacy of different therapeutic options, we conclude that it is necessary to standardize certain variables-such as the viral clearance time, biomarkers associated with severity, hospital stay, requirement of invasive mechanical ventilation, and mortality rate-in order to facilitate verification of the efficacy of such treatments and to better assess the repeatability of the most effective and promising results.
Project description:Randomized controlled trials (RCT) were impacted by the COVID-19 pandemic, but no systematic analysis has evaluated the overall impact of COVID-19 on non-COVID-19-related RCTs. The ClinicalTrials.gov database was queried in February 2020. Eligible studies included all randomized trials with a start date after 1 January 2010 and were active during the period from 1 January 2015 to 31 December 2020. The effect of the pandemic period on non-COVID-19 trials was determined by piece-wise regression models using 11 March 2020 as the start of the pandemic and by time series analysis (models fitted using 2015-2018 data and forecasted for 2019-2020). The study endpoints were early trial stoppage, normal trial completion, and trial activation. There were 161,377 non-COVID-19 trials analyzed. The number of active trials increased annually through 2019 but decreased in 2020. According to the piece-wise regression models, trial completion was not affected by the pandemic (p = 0.56) whereas trial stoppage increased (p = 0.001). There was a pronounced decrease in trial activation early during the pandemic (p < 0.001) which then recovered. The findings from the time series models were consistent comparing forecasted and observed results (trial completion p = 0.22; trial stoppage p < 0.01; trial activation, p = 0.01). During the pandemic, there was an increase in non-COVID-19 RCTs stoppage without changes in RCT completion. There was a sharp decline in new RCTs at the beginning of the pandemic, which later recovered.