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:In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown a particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real-world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.
| S-EPMC10981207 | biostudies-literature
Project description:BackgroundEndpoint choice for randomized controlled trials of treatments for novel coronavirus-induced disease (COVID-19) is complex. Trials must start rapidly to identify treatments that can be used as part of the outbreak response, in the midst of considerable uncertainty and limited information. COVID-19 presentation is heterogeneous, ranging from mild disease that improves within days to critical disease that can last weeks to over a month and can end in death. While improvement in mortality would provide unquestionable evidence about the clinical significance of a treatment, sample sizes for a study evaluating mortality are large and may be impractical, particularly given a multitude of putative therapies to evaluate. Furthermore, patient states in between "cure" and "death" represent meaningful distinctions. Clinical severity scores have been proposed as an alternative. However, the appropriate summary measure for severity scores has been the subject of debate, particularly given the variable time course of COVID-19. Outcomes measured at fixed time points, such as a comparison of severity scores between treatment and control at day 14, may risk missing the time of clinical benefit. An endpoint such as time to improvement (or recovery) avoids the timing problem. However, some have argued that power losses will result from reducing the ordinal scale to a binary state of "recovered" versus "not recovered."MethodsWe evaluate statistical power for possible trial endpoints for COVID-19 treatment trials using simulation models and data from two recent COVID-19 treatment trials.ResultsPower for fixed time-point methods depends heavily on the time selected for evaluation. Time-to-event approaches have reasonable statistical power, even when compared with a fixed time-point method evaluated at the optimal time.DiscussionTime-to-event analysis methods have advantages in the COVID-19 setting, unless the optimal time for evaluating treatment effect is known in advance. Even when the optimal time is known, a time-to-event approach may increase power for interim analyses.
| S-EPMC7611901 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
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-EPMC7334919 | 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