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 | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
Deploying Randomized Controlled Trials during the COVID-19 Pandemic: Reason and Bayesian Designs.
Annals of the American Thoracic Society 20200801 8
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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:Over recent decades, adaptive trial designs have been used more and more often for clinical trials, including randomized controlled trials (RCTs). This rise in the use of adaptive RCTs has been accompanied by debates about whether such trials offer ethical and methodological advantages over traditional, fixed RCTs. This study examined how experts on clinical trial methods and ethics believe that adaptive RCTs, compared to fixed ones, affect the ethical character of clinical research. We conducted in-depth interviews with 17 researchers from bioethics, epidemiology, biostatistics, and/or medical backgrounds. While about half believed that adaptive trials are more complex and may thus threaten autonomy, these respondents also expressed that this challenge is not insurmountable. Most respondents expressed that efficiency and potential for participant benefit were the main justifications for adaptive trials. There was tension about whether adaptive randomization in response to increasing information disrupts clinical equipoise, with some respondents insisting that uncertainty still exists and therefore clinical equipoise is not disrupted. These findings suggest that further discussion is needed to increase the awareness and utility of these study designs.
| S-EPMC10739783 | biostudies-literature
Project description: Not available
| S-EPMC7508503 | biostudies-literature
Project description:Aim: Adaptive designs are frequently used in drug randomized controlled trials (RCTs). However, their use in medical device RCTs remains unclear. We aimed to characterize medical device RCTs with adaptive designs. Materials & methods: We searched for adaptive RCTs in the following databases: ClinicalTrials.gov, International Clinical Trials Registry Platform and the International Standard Randomised Controlled Trial Number registry. Adaptive design keywords and medical device corporation names were used as terms to search the trial records registered between 1 January 2000 and 18 October 2024 in the databases. The annual number and proportions of adaptive trials were analyzed, and characteristics such as design type, sponsor, therapeutic area, trial stage and regulatory status were summarized. Results: Overall, 105 adaptive RCTs were identified from ClinicalTrials.gov, accounting for 2.112 per 1000 trials in 49,721 medical device clinical trials registered in ClinicalTrials.gov during the period. The average annual number of adaptive RCTs per 1000 clinical trials was the highest (8.55 ± 11.65) during 2005-2010, reduced to 3.33 ± 2.35 during 2011-2016, and significantly decreased to 1.29 ± 0.85 during 2017-2024 (p = 0.011). The most common adaptive designs were group sequential design (GSD, 50.5%), sample size reassessment (SSR, 17.1%) and investigating both superiority and non-inferiority (10.5%). Most RCTs were sponsored by the private sector (62.9%), conducted in Europe/North America (95.2%), in the field of heart disease (46.7%) and post-market trials (76.2%). Compared with pre-market RCTs, post-market RCTs showed more diverse adaptive designs such as response-adaptive randomization and adaptive enrichment. Conclusion: The average annual proportions of adaptive medical device RCTs in ClinicalTrials.gov has reduced in the last 10 years. The most-used adaptive designs in medical device RCTs are GSD, SSR and investigating both superiority and non-inferiority.
| S-EPMC11650385 | 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:BackgroundIn the context of the COVID-19 pandemic, randomized controlled trials (RCTs) are essential to support clinical decision-making. We aimed (1) to assess and compare the reporting characteristics of RCTs between preprints and peer-reviewed publications and (2) to assess whether reporting improves after the peer review process for all preprints subsequently published in peer-reviewed journals.MethodsWe searched the Cochrane COVID-19 Study Register and L·OVE COVID-19 platform to identify all reports of RCTs assessing pharmacological treatments of COVID-19, up to May 2021. We extracted indicators of transparency (e.g., trial registration, data sharing intentions) and assessed the completeness of reporting (i.e., some important CONSORT items, conflict of interest, ethical approval) using a standardized data extraction form. We also identified paired reports published in preprint and peer-reviewed publications.ResultsWe identified 251 trial reports: 121 (48%) were first published in peer-reviewed journals, and 130 (52%) were first published as preprints. Transparency was poor. About half of trials were prospectively registered (n = 140, 56%); 38% (n = 95) made their full protocols available, and 29% (n = 72) provided access to their statistical analysis plan report. A data sharing statement was reported in 68% (n = 170) of the reports of which 91% stated their willingness to share. Completeness of reporting was low: only 32% (n = 81) of trials completely defined the pre-specified primary outcome measures; 57% (n = 143) reported the process of allocation concealment. Overall, 51% (n = 127) adequately reported the results for the primary outcomes while only 14% (n = 36) of trials adequately described harms. Primary outcome(s) reported in trial registries and published reports were inconsistent in 49% (n = 104) of trials; of them, only 15% (n = 16) disclosed outcome switching in the report. There were no major differences between preprints and peer-reviewed publications. Of the 130 RCTs published as preprints, 78 were subsequently published in a peer-reviewed journal. There was no major improvement after the journal peer review process for most items.ConclusionsTransparency, completeness, and consistency of reporting of COVID-19 clinical trials were insufficient both in preprints and peer-reviewed publications. A comparison of paired reports published in preprint and peer-reviewed publication did not indicate major improvement.
| S-EPMC9510360 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other