1
|
Smith JA, DeVito N, Lee H, Tiplady C, Abhari RE, Kartsonaki C. Estimating the effect of COVID-19 on trial design characteristics: a registered report. ROYAL SOCIETY OPEN SCIENCE 2023; 10:201543. [PMID: 36686547 PMCID: PMC9832295 DOI: 10.1098/rsos.201543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 12/15/2022] [Indexed: 06/17/2023]
Abstract
There have been reports of poor-quality research during the COVID-19 pandemic. This registered report assessed design characteristics of registered clinical trials for COVID-19 compared to non-COVID-19 trials to empirically explore the design of clinical research during a pandemic and how it compares to research conducted in non-pandemic times. We did a retrospective cohort study with a 1 : 1 ratio of interventional COVID-19 registrations to non-COVID-19 registrations, with four trial design outcomes: use of control arm, randomization, blinding and prospective registration. Logistic regression was used to estimate the odds ratio of investigating COVID-19 versus not COVID-19 and estimate direct and total effects of investigating COVID-19 for each outcome. The primary analysis showed a positive direct and total effect of COVID-19 on the use of control arms and randomization. It showed a negative direct effect of COVID-19 on blinding but no evidence of a total effect. There was no evidence of an effect on prospective registration. Taken together with secondary and sensitivity analyses, our findings are inconclusive but point towards a higher prevalence of key design characteristics in COVID-19 trials versus controls. The findings do not support much existing COVID-19 research quality literature, which generally suggests that COVID-19 led to a reduction in quality. Limitations included some data quality issues, minor deviations from the pre-registered plan and the fact that trial registrations were analysed which may not accurately reflect study design and conduct. Following in-principle acceptance, the approved stage 1 version of this manuscript was pre-registered on the Open Science Framework at https://doi.org/10.17605/OSF.IO/5YAEB. This pre-registration was performed prior to data analysis.
Collapse
Affiliation(s)
- James A. Smith
- Botnar Research Centre and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX1 2JD, UK
- National Institute for Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Nicholas DeVito
- Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford OX1 2JD, UK
| | - Hopin Lee
- Botnar Research Centre and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX1 2JD, UK
| | - Catherine Tiplady
- Botnar Research Centre and Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX1 2JD, UK
| | - Roxanna E. Abhari
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | | |
Collapse
|
2
|
Hartsell EM, Gillespie MN, Langley RJ. Does acute and persistent metabolic dysregulation in COVID19 point to novel biomarkers and future therapeutic strategies? Eur Respir J 2021; 59:13993003.02417-2021. [PMID: 34675049 PMCID: PMC8542864 DOI: 10.1183/13993003.02417-2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 10/09/2021] [Indexed: 12/15/2022]
Abstract
When the coronavirus disease 2019 (COVID-19) pandemic first appeared in December of 2019, the pathophysiological underpinnings of the disease were largely unknown. Scientists, physicians and government institutions from around the globe took an “all-hands on deck” approach with the hope of identifying potential therapies to treat as well as understand the pathophysiology of the disease [1]. Currently, more than 4800 clinical trials listed on clinicaltrials.gov have been performed or proposed around the world, many with subjects from vastly different ethnic and racial backgrounds, as well as different standard-of-care strategies [2]. Despite this effort, apart from monoclonal antibodies, few therapies have emerged as effective treatments of COVID-19; vaccines remain the best approach to control and mitigate the pandemic [3]. Metabolomics changes in COVID-19 predict acute patient outcomes and suggest a role for a bioenergetic crisis. Thus, metabolomics changes in COVID-19 may serve as a biomarker and provide insight into pathogenic mechanisms and pharmacologic targets.https://bit.ly/2XkJeU8
Collapse
Affiliation(s)
- Emily M Hartsell
- Department of Pharmacology, University of South Alabama College of Medicine, Mobile, AL, USA
| | - Mark N Gillespie
- Department of Pharmacology, University of South Alabama College of Medicine, Mobile, AL, USA
| | - Raymond J Langley
- Department of Pharmacology, University of South Alabama College of Medicine, Mobile, AL, USA
| |
Collapse
|
3
|
Janiaud P, Hemkens LG, Ioannidis JPA. Challenges and Lessons Learned From COVID-19 Trials: Should We Be Doing Clinical Trials Differently? Can J Cardiol 2021; 37:1353-1364. [PMID: 34077789 PMCID: PMC8164884 DOI: 10.1016/j.cjca.2021.05.009] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/18/2021] [Accepted: 05/22/2021] [Indexed: 12/15/2022] Open
Abstract
The COVID-19 crisis led to a flurry of clinical trials activity. The COVID-evidence database shows 2814 COVID-19 randomized trials registered as of February 16, 2021. Most were small (only 18% have a planned sample size > 500) and the rare completed ones have not provided published results promptly (only 283 trial publications as of February 2021). Small randomized trials and observational, nonrandomized analyses have not had a successful track record and have generated misleading expectations. Different large trials on the same intervention have generally been far more efficient in producing timely and consistent evidence. The rapid generation of evidence and accelerated dissemination of results have led to new challenges for systematic reviews and meta-analyses (eg, rapid, living, and scoping reviews). Pressure to regulatory agencies has also mounted with massive emergency authorizations, but some of them have had to be revoked. Pandemic circumstances have disrupted the way trials are conducted; therefore, new methods have been developed and adopted more widely to facilitate recruitment, consent, and overall trial conduct. On the basis of the COVID-19 experience and its challenges, planning of several large, efficient trials, and wider use of adaptive designs might change the future of clinical research. Pragmatism, integration in clinical care, efficient administration, promotion of collaborative structures, and enhanced integration of existing data and facilities might be several of the legacies of COVID-19 on future randomized trials.
Collapse
Affiliation(s)
- Perrine Janiaud
- Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Lars G Hemkens
- Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland; Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA; Meta-Research Innovation Center Berlin (METRIC-B), Berlin Institute of Health, Berlin, Germany
| | - John P A Ioannidis
- Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA; Meta-Research Innovation Center Berlin (METRIC-B), Berlin Institute of Health, Berlin, Germany; Department of Medicine, Stanford University School of Medicine, Stanford, California, USA; Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA; Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA.
| |
Collapse
|