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1.
Trials ; 22(1): 153, 2021 Feb 18.
Article in English | MEDLINE | ID: covidwho-1090626

ABSTRACT

BACKGROUND: The sharing of individual participant-level data from COVID-19 trials would allow re-use and secondary analysis that can help accelerate the identification of effective treatments. The sharing of trial data is not the norm, but the unprecedented pandemic caused by SARS-CoV-2 may serve as an impetus for greater data sharing. We sought to assess the data sharing intentions of interventional COVID-19 trials as declared in trial registrations and publications. METHODS: We searched ClinicalTrials.gov and PubMed for COVID-19 interventional trials. We analyzed responses to ClinicalTrials.gov fields regarding intent to share individual participant level data and analyzed the data sharing statements in eligible publications. RESULTS: Nine hundred twenty-four trial registrations were analyzed. 15.7% were willing to share, of which 38.6% were willing to share immediately upon publication of results. 47.6% declared they were not willing to share. Twenty-eight publications were analyzed representing 26 unique COVID-19 trials. Only seven publications contained data sharing statements; six indicated a willingness to share data whereas one indicated that data was not available for sharing. CONCLUSIONS: At a time of pressing need for researchers to work together to combat a global pandemic, intent to share individual participant-level data from COVID-19 interventional trials is limited.


Subject(s)
/therapy , Clinical Trials as Topic/statistics & numerical data , Information Dissemination , Publications/statistics & numerical data , Research Design/statistics & numerical data , /epidemiology , Humans , Intention , Pandemics/prevention & control
2.
Genome Med ; 12(1): 115, 2020 12 28.
Article in English | MEDLINE | ID: covidwho-992546

ABSTRACT

The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.


Subject(s)
/epidemiology , Case-Control Studies , Genomics/methods , Pandemics , Research Design , /genetics , Computer Simulation , Confounding Factors, Epidemiologic , Exposome , False Negative Reactions , Genetic Predisposition to Disease , Genetic Variation , Host-Pathogen Interactions/genetics , Humans , Research Design/statistics & numerical data , Reverse Transcriptase Polymerase Chain Reaction , Risk , Sensitivity and Specificity
4.
Medicine (Baltimore) ; 99(43): e22840, 2020 Oct 23.
Article in English | MEDLINE | ID: covidwho-894696

ABSTRACT

Up-to-date information on the current progress made in the research and development to control the global COVID-19 pandemic is important. The study aimed to analyze the clinical trial characteristics and vaccine development progress of the new Coronavirus Disease 2019 (COVID-19) registered with the World Health Organization International Clinical Trial Registry Platform (WHO ICTRP).A comprehensive search of COVID-19 clinical trials since the establishment of the ICTRP to June 11, 2020, was conducted to record and analyze relevant characteristics. Chi-Squared test was used to compare the statistical differences between different research types, interventions, and sources.A total of 3282 COVID-19 clinical trials in 17 clinical trial registration centers were registered with the WHO ICTRP. The main research sources for the present study were ClinicalTrials.gov and ChiCTR. There were significant differences in the parameters of study location (P = .000), number of participants (P = .000), study duration (P = .001), research stage (P = .000), randomization procedure (P = .000), and blinding method (P = .000) between the 2 registration sources. There were significant differences in all the parameters between different kinds of intervention methods. Hydroxychloroquine, plasma therapy, and Xiyanping injection were the high-frequency research drugs used. Ten different vaccine studies were registered under phases I-II.Amongst the studies researched, heterogeneity existed for various parameters. Differences in the type of study, interventions, and registration sources of the studies led to significant differences in certain parameters of the COVID-19 clinical trials. The statistics of high-frequency drugs and the progress of vaccine trials may provide an informative reference for the prevention and control of COVID-19.


Subject(s)
Betacoronavirus , Clinical Trials as Topic/methods , Coronavirus Infections/therapy , Pneumonia, Viral/therapy , Registries , Research Design , World Health Organization , Clinical Trials as Topic/standards , Clinical Trials as Topic/statistics & numerical data , Coronavirus Infections/prevention & control , Humans , Pandemics , Quality Improvement , Research Design/standards , Research Design/statistics & numerical data , Viral Vaccines
6.
BMC Med Res Methodol ; 20(1): 208, 2020 08 12.
Article in English | MEDLINE | ID: covidwho-713161

ABSTRACT

BACKGROUND: The coronavirus pandemic (Covid-19) presents a variety of challenges for ongoing clinical trials, including an inevitably higher rate of missing outcome data, with new and non-standard reasons for missingness. International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking. METHODS: We present a four-step strategy for handling missing outcome data in the analysis of randomised trials that are ongoing during a pandemic. We consider handling missing data arising due to (i) participant infection, (ii) treatment disruptions and (iii) loss to follow-up. We consider both settings where treatment effects for a 'pandemic-free world' and 'world including a pandemic' are of interest. RESULTS: In any trial, investigators should; (1) Clarify the treatment estimand of interest with respect to the occurrence of the pandemic; (2) Establish what data are missing for the chosen estimand; (3) Perform primary analysis under the most plausible missing data assumptions followed by; (4) Sensitivity analysis under alternative plausible assumptions. To obtain an estimate of the treatment effect in a 'pandemic-free world', participant data that are clinically affected by the pandemic (directly due to infection or indirectly via treatment disruptions) are not relevant and can be set to missing. For primary analysis, a missing-at-random assumption that conditions on all observed data that are expected to be associated with both the outcome and missingness may be most plausible. For the treatment effect in the 'world including a pandemic', all participant data is relevant and should be included in the analysis. For primary analysis, a missing-at-random assumption - potentially incorporating a pandemic time-period indicator and participant infection status - or a missing-not-at-random assumption with a poorer response may be most relevant, depending on the setting. In all scenarios, sensitivity analysis under credible missing-not-at-random assumptions should be used to evaluate the robustness of results. We highlight controlled multiple imputation as an accessible tool for conducting sensitivity analyses. CONCLUSIONS: Missing data problems will be exacerbated for trials active during the Covid-19 pandemic. This four-step strategy will facilitate clear thinking about the appropriate analysis for relevant questions of interest.


Subject(s)
Outcome Assessment, Health Care/statistics & numerical data , Practice Guidelines as Topic , Randomized Controlled Trials as Topic/statistics & numerical data , Research Design/statistics & numerical data , Betacoronavirus/physiology , Comorbidity , Coronavirus Infections/epidemiology , Coronavirus Infections/therapy , Coronavirus Infections/virology , Humans , Outcome Assessment, Health Care/methods , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/therapy , Pneumonia, Viral/virology , Randomized Controlled Trials as Topic/methods , Reproducibility of Results
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