Deploying Randomized Controlled Trials during the COVID-19 Pandemic: Reason and Bayesian Designs.
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ABSTRACT:
SUBMITTER: Zampieri FG
PROVIDER: S-EPMC7393781 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) altered the logistics of ongoing randomized controlled trials (RCTs). The need to reduce in-person research and clinical activities, however, presented an additional level of complexity in order to continue conducting RCTs that focused on the development of medications for Alcohol Use Disorder (AUD). The visits required a systematic objective evaluation from the physician and mental health professional and clinical staff, as many of the safety and efficacy assessments are self-reported. The following commentary addresses the successes and limitations our RCTs encountered during the coronavirus (COVID-19) pandemic.
| S-EPMC7832637 | biostudies-literature
Project description:Background: We provided a comprehensive evaluation of efficacy of available treatments for coronavirus disease 2019 (COVID-19). Methods: We searched for candidate COVID-19 studies in WHO COVID-19 Global Research Database up to August 19, 2021. Randomized controlled trials for suspected or confirmed COVID-19 patients published on peer-reviewed journals were included, regardless of demographic characteristics. Outcome measures included mortality, mechanical ventilation, hospital discharge and viral clearance. Bayesian network meta-analysis with fixed effects was conducted to estimate the effect sizes using posterior means and 95% equal-tailed credible intervals (CrIs). Odds ratio (OR) was used as the summary measure for treatment effect. Bayesian hierarchical models were used to estimate effect sizes of treatments grouped by the treatment classifications. Results: We identified 222 eligible studies with a total of 102,950 patients. Compared with the standard of care, imatinib, intravenous immunoglobulin and tocilizumab led to lower risk of death; baricitinib plus remdesivir, colchicine, dexamethasone, recombinant human granulocyte colony stimulating factor and tocilizumab indicated lower occurrence of mechanical ventilation; tofacitinib, sarilumab, remdesivir, tocilizumab and baricitinib plus remdesivir increased the hospital discharge rate; convalescent plasma, ivermectin, ivermectin plus doxycycline, hydroxychloroquine, nitazoxanide and proxalutamide resulted in better viral clearance. From the treatment class level, we found that the use of antineoplastic agents was associated with fewer mortality cases, immunostimulants could reduce the risk of mechanical ventilation and immunosuppressants led to higher discharge rates. Conclusions: This network meta-analysis identified superiority of several COVID-19 treatments over the standard of care in terms of mortality, mechanical ventilation, hospital discharge and viral clearance. Tocilizumab showed its superiority compared with SOC on preventing severe outcomes such as death and mechanical ventilation as well as increasing the discharge rate, which might be an appropriate treatment for patients with severe or mild/moderate illness. We also found the clinical efficacy of antineoplastic agents, immunostimulants and immunosuppressants with respect to the endpoints of mortality, mechanical ventilation and discharge, which provides valuable information for the discovery of potential COVID-19 treatments.
| S-EPMC8506153 | biostudies-literature
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.
| S-EPMC8011492 | biostudies-literature
Project description: Not available
| S-EPMC7508503 | biostudies-literature
Project description:BackgroundThe therapeutic evidence collected from well-designed studies is needed to help manage the global pandemic of the coronavirus disease 2019 (COVID-19). Evaluating the quality of therapeutic data collected during this most recent pandemic is important for improving future clinical research under similar circumstances.ObjectiveTo assess the methodological quality and variability in implementation of randomized controlled trials (RCTs) for treating COVID-19, and to analyze the support that should be provided to improve data collected during an urgent pandemic situation.Search strategyPubMed, Excerpta Medica Database, China National Knowledge Infrastructure, Wanfang, and Chongqing VIP, and the preprint repositories including Social Science Research Network and MedRxiv were systematically searched, up to September 30, 2020, using the keywords "coronavirus disease 2019 (COVID-19)," "2019 novel coronavirus (2019-nCoV)," "severe acute respiratory syndrome-related coronavirus-2 (SARS-CoV-2)," "novel coronavirus pneumonia (NCP)," "randomized controlled trial (RCT)" and "random."Inclusion criteriaRCTs studying the treatment of COVID-19 were eligible for inclusion.Data extraction and analysisScreening of published RCTs for inclusion and data extraction were each conducted by two researchers. Analysis of general information on COVID-19 RCTs was done using descriptive statistics. Methodological quality was assessed using the risk-of-bias tools in the Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). Variability in implementation was assessed by comparing consistency between RCT reports and registration information.ResultsA total of 5886 COVID-19 RCTs were identified. Eighty-one RCTs were finally included, of which, 45 had registration information. Methodological quality of the RTCs was not optimal due to deficiencies in five main domains: allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective reporting. Comparisons of consistency between published protocols and registration information showed that the 45 RCTs with registration information had common deviations in seven items: inclusion and exclusion criteria, sample size, outcomes, research sites of recruitment, interventions, and blinding.ConclusionThe methodological quality of COVID-19 RCTs conducted in early to mid 2020 was consistently low and variability in implementation was common. More support for implementing high-quality methodology is needed to obtain the quality of therapeutic evidence needed to provide positive guidance for clinical care. We make an urgent appeal for accelerating the construction of a collaborative sharing platform and preparing multidisciplinary talent and professional teams to conduct excellent clinical research when faced with epidemic diseases of the future. Further, variability in RCT implementation should be clearly reported and interpreted to improve the utility of data resulting from those trials.
| S-EPMC7970417 | biostudies-literature
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.
| S-EPMC8011600 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
Project description: Not available
| S-EPMC8733489 | biostudies-literature
Project description: Not available
| S-EPMC8292190 | biostudies-literature
Project description:BackgroundSocietal measures to contain the spread of COVID-19 (eg, lockdown and contact restrictions) have been associated with decreased health and well-being. A multitude of prepandemic studies identified the beneficial effects of physical exercise on both physical and mental health.ObjectiveWe report on the feasibility of a remote physical exercise intervention and its stress-buffering potential in 2 untrained cohorts: a pre-COVID-19 cohort that completed the intervention in 2019 and a lockdown cohort that started the intervention shortly before pandemic-related restrictions were implemented.MethodsIn a randomized controlled trial, participants were assigned to either an intervention group (IG; pre-COVID-19 cohort: n=7 and lockdown cohort: n=9) or a control group (CG; pre-COVID-19 cohort: n=6 and lockdown cohort: n=6). IG participants received weekly individualized training recommendations delivered via web-based support. The intervention period was initially planned for 8 weeks, which was adhered to in the pre-COVID-19 cohort (mean 8.3, SD 0.5 weeks) but was extended to an average of 17.7 (SD 2.0) weeks in the lockdown cohort. Participants' health parameters were assessed before and after the intervention: aerobic capacity was measured as peak oxygen uptake (VO2peak) via cardiopulmonary exercise testing. Depressive symptoms were scored via the depression subscale of the Brief Symptom Inventory-18.ResultsDropout rates were low in both cohorts in the IG (pre-COVID-19 cohort: n=0, 0% and lockdown cohort: n=2, 16.7%) and the CG (pre-COVID-19 cohort: n=0, 0% and lockdown cohort: n=2, 20%). The mean adherence to the training sessions of the IG for both cohorts was 84% (pre-COVID-19 cohort: SD 5.5% and lockdown cohort: SD 11.6%). Aligned rank transform ANOVAs in the lockdown cohort indicated deterioration of VO2peak and depressive symptoms from before to after the intervention in the CG but no longitudinal changes in the IG. Analyses in the pre-COVID-19 cohort revealed significant increases in VO2peak for the IG compared to the CG (P=.04) but no intervention effects on depressive symptoms.ConclusionsWith low dropout rates and high adherence, the remote intervention was feasible for healthy adults under regular conditions and in the face of pandemic-related stressors. Moreover, our results hint at a stress-buffering effect as well as a buffering of a lockdown-induced deconditioning of remote physical exercise interventions in the pandemic scenario, which can be used in future studies to overcome equally stressful periods of life. However, due to limited statistical power, these findings should be replicated in similar scenarios.Trial registrationGerman Clinical Trials Register DRKS00018078; https://drks.de/search/en/trial/DRKS00018078.
| S-EPMC11342007 | biostudies-literature