scholarly journals Detection of significant antiviral drug effects on COVID-19 using viral load and PCR-positive rate in randomized controlled trials

2021 ◽  
Vol 3 (3) ◽  
pp. 85-88
Author(s):  
Marwa AKAO ◽  
Joohyeon WOO ◽  
Shingo IWAMI ◽  
Shoya IWANAMI
Keyword(s):  
Viral Load ◽  
Randomized Controlled Trials ◽  
Antiviral Drug ◽  
Drug Effects ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Positive Rate

scholarly journals Efficacy of remdesivir versus placebo for the treatment of COVID-19: A protocol for systematic review and meta-analysis of randomized controlled trials

2020 ◽  
Author(s):  
Desye Gebrie ◽  
Desalegn Getnet ◽  
Tsegahun Manyazewal
Keyword(s):  
Systematic Review ◽  
Randomized Controlled Trials ◽  
Meta Analysis ◽  
Antiviral Drug ◽  
Risk Of Bias ◽  
Cochrane Library ◽  
Controlled Trials ◽  
Significant Heterogeneity ◽  
List Type ◽  
Randomized Controlled

AbstractBackgroundIn spite of the global containment on prevention efforts, the spread of coronavirus disease 2019 (COVID-19) is continuing to rise, with 1.1 million confirmed cases and 60,124 deaths recorded worldwide since 04 April 2020. The outbreak has a significant threat to international health and economy. At present, there is no approved vaccine or treatment for the disease, while efforts are underway. Remdesivir, a nucleotide-analogue antiviral drug developed for Ebola, is determined to prevent and stop infections with COVID-19, while results are yet controversial. Here, we aim to conduct a systematic review and meta-analysis of randomized controlled trials to compare the effectiveness of remdesivir and placebo in patients with COVID-19.Method and analysisWe will search MEDLINE-PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and Google scholar databases without restriction in year of publication. We will include randomized controlled trials that assessed the effectiveness of remdesivir versus placebo for patients confirmed with COVID-19. We will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA 2015) guidelines for the design and reporting of the results. The primary endpoint will be time to clinical recovery. The secondary endpoints will be all cause mortality, discharged date, frequency of respiratory progression, and treatment-emergent adverse events. Two independent authors will perform study selection, data extraction, and methodology quality assessment. RevMan 5.3 software will be used for statistical analysis. Random/fixed effect model will be carried out to calculate mean differences for continuous outcomes and risk ratio for dichotomous outcomes between remdesivir and placebo.Ethics and disseminationThis study does not require ethical approval, because no participant’s data will be involved in this systematic review and meta-analysis. The findings of this study will be published in reputable and peer-reviewed journal.RegistrationThis review protocol is submitted in PROSPERO database for registration and we will include the registration number in the revised version of the manuscript.Strengths and limitations of this study➣This systematic review and meta-analysis will be derived from only randomized controlled trials which will increase the quality of evidences.➣This systematic review and meta-analysis will be derived from only randomized controlled trials which will reduce between study heterogeneity.➣Subgroup and sensitivity analysis will be carried out to identify possible reasons that may cause significant heterogeneity between studies.➣The use of Cochrane risk of bias tool to assess risk of bias for each included studies to extract and synthesize evidence based conclusions.➣One of the limitation of this study might be the restriction of trials published in English language.

scholarly journals Correction: Randomized Controlled Trials to Define Viral Load Thresholds for Cytomegalovirus Pre-Emptive Therapy

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0185298
Author(s):  
Paul D. Griffiths ◽  
Emily Rothwell ◽  
Mohammed Raza ◽  
Stephanie Wilmore ◽  
Tomas Doyle ◽  
...  
Keyword(s):  
Viral Load ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled

scholarly journals Randomized Controlled Trials to Define Viral Load Thresholds for Cytomegalovirus Pre-Emptive Therapy

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0163722 ◽  
Author(s):  
Paul D. Griffiths ◽  
Emily Rothwell ◽  
Mohammed Raza ◽  
Stephanie Wilmore ◽  
Tomas Doyle ◽  
...  
Keyword(s):  
Viral Load ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled

scholarly journals Detection of significant antiviral drug effects on COVID-19 with reasonable sample sizes in randomized controlled trials: A modeling study

PLoS Medicine ◽  
2021 ◽  
Vol 18 (7) ◽  
pp. e1003660
Author(s):  
Shoya Iwanami ◽  
Keisuke Ejima ◽  
Kwang Su Kim ◽  
Koji Noshita ◽  
Yasuhisa Fujita ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Viral Load ◽  
Randomized Controlled Trials ◽  
Sample Size ◽  
Antiviral Effect ◽  
Controlled Trials ◽  
Virus Dynamics ◽  
Viral Dynamics ◽  
Dynamics Model ◽  
Randomized Controlled

Background Development of an effective antiviral drug for Coronavirus Disease 2019 (COVID-19) is a global health priority. Although several candidate drugs have been identified through in vitro and in vivo models, consistent and compelling evidence from clinical studies is limited. The lack of evidence from clinical trials may stem in part from the imperfect design of the trials. We investigated how clinical trials for antivirals need to be designed, especially focusing on the sample size in randomized controlled trials. Methods and findings A modeling study was conducted to help understand the reasons behind inconsistent clinical trial findings and to design better clinical trials. We first analyzed longitudinal viral load data for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) without antiviral treatment by use of a within-host virus dynamics model. The fitted viral load was categorized into 3 different groups by a clustering approach. Comparison of the estimated parameters showed that the 3 distinct groups were characterized by different virus decay rates (p-value < 0.001). The mean decay rates were 1.17 d−1 (95% CI: 1.06 to 1.27 d−1), 0.777 d−1 (0.716 to 0.838 d−1), and 0.450 d−1 (0.378 to 0.522 d−1) for the 3 groups, respectively. Such heterogeneity in virus dynamics could be a confounding variable if it is associated with treatment allocation in compassionate use programs (i.e., observational studies). Subsequently, we mimicked randomized controlled trials of antivirals by simulation. An antiviral effect causing a 95% to 99% reduction in viral replication was added to the model. To be realistic, we assumed that randomization and treatment are initiated with some time lag after symptom onset. Using the duration of virus shedding as an outcome, the sample size to detect a statistically significant mean difference between the treatment and placebo groups (1:1 allocation) was 13,603 and 11,670 (when the antiviral effect was 95% and 99%, respectively) per group if all patients are enrolled regardless of timing of randomization. The sample size was reduced to 584 and 458 (when the antiviral effect was 95% and 99%, respectively) if only patients who are treated within 1 day of symptom onset are enrolled. We confirmed the sample size was similarly reduced when using cumulative viral load in log scale as an outcome. We used a conventional virus dynamics model, which may not fully reflect the detailed mechanisms of viral dynamics of SARS-CoV-2. The model needs to be calibrated in terms of both parameter settings and model structure, which would yield more reliable sample size calculation. Conclusions In this study, we found that estimated association in observational studies can be biased due to large heterogeneity in viral dynamics among infected individuals, and statistically significant effect in randomized controlled trials may be difficult to be detected due to small sample size. The sample size can be dramatically reduced by recruiting patients immediately after developing symptoms. We believe this is the first study investigated the study design of clinical trials for antiviral treatment using the viral dynamics model.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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