scholarly journals The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1193
Author(s):  
Perrine Janiaud ◽  
Cathrine Axfors ◽  
Janneke van't Hooft ◽  
Ramon Saccilotto ◽  
Arnav Agarwal ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Treatment Options ◽  
Descriptive Analysis ◽  
Public Attention ◽  
Clinical Trial Research ◽  
Target Sample Size ◽  
Vaccine Trials ◽  
Care Workers ◽  
The Usa

Background: Never before have clinical trials drawn as much public attention as those testing interventions for COVID-19. We aimed to describe the worldwide COVID-19 clinical research response and its evolution over the first 100 days of the pandemic. Methods: Descriptive analysis of planned, ongoing or completed trials by April 9, 2020 testing any intervention to treat or prevent COVID-19, systematically identified in trial registries, preprint servers, and literature databases. A survey was conducted of all trials to assess their recruitment status up to July 6, 2020. Results: Most of the 689 trials (overall target sample size 396,366) were small (median sample size 120; interquartile range [IQR] 60-300) but randomized (75.8%; n=522) and were often conducted in China (51.1%; n=352) or the USA (11%; n=76). 525 trials (76.2%) planned to include 155,571 hospitalized patients, and 25 (3.6%) planned to include 96,821 health-care workers. Treatments were evaluated in 607 trials (88.1%), frequently antivirals (n=144) or antimalarials (n=112); 78 trials (11.3%) focused on prevention, including 14 vaccine trials. No trial investigated social distancing. Interventions tested in 11 trials with >5,000 participants were also tested in 169 smaller trials (median sample size 273; IQR 90-700). Hydroxychloroquine alone was investigated in 110 trials. While 414 trials (60.0%) expected completion in 2020, only 35 trials (4.1%; 3,071 participants) were completed by July 6. Of 112 trials with detailed recruitment information, 55 had recruited <20% of the targeted sample; 27 between 20-50%; and 30 over 50% (median 14.8% [IQR 2.0-62.0%]). Conclusions: The size and speed of the COVID-19 clinical trials agenda is unprecedented. However, most trials were small investigating a small fraction of treatment options. The feasibility of this research agenda is questionable, and many trials may end in futility, wasting research resources. Much better coordination is needed to respond to global health threats.

scholarly journals The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1193 ◽  
Author(s):  
Perrine Janiaud ◽  
Cathrine Axfors ◽  
Janneke van't Hooft ◽  
Ramon Saccilotto ◽  
Arnav Agarwal ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Treatment Options ◽  
Descriptive Analysis ◽  
Public Attention ◽  
Clinical Trial Research ◽  
Target Sample Size ◽  
Vaccine Trials ◽  
Care Workers ◽  
The Usa

Background: Never before have clinical trials drawn as much public attention as those testing interventions for COVID-19. We aimed to describe the worldwide COVID-19 clinical research response and its evolution over the first 100 days of the pandemic. Methods: Descriptive analysis of planned, ongoing or completed trials by April 9, 2020 testing any intervention to treat or prevent COVID-19, systematically identified in trial registries, preprint servers, and literature databases. A survey was conducted of all trials to assess their recruitment status up to July 6, 2020. Results: Most of the 689 trials (overall target sample size 396,366) were small (median sample size 120; interquartile range [IQR] 60-300) but randomized (75.8%; n=522) and were often conducted in China (51.1%; n=352) or the USA (11%; n=76). 525 trials (76.2%) planned to include 155,571 hospitalized patients, and 25 (3.6%) planned to include 96,821 health-care workers. Treatments were evaluated in 607 trials (88.1%), frequently antivirals (n=144) or antimalarials (n=112); 78 trials (11.3%) focused on prevention, including 14 vaccine trials. No trial investigated social distancing. Interventions tested in 11 trials with >5,000 participants were also tested in 169 smaller trials (median sample size 273; IQR 90-700). Hydroxychloroquine alone was investigated in 110 trials. While 414 trials (60.0%) expected completion in 2020, only 35 trials (4.1%; 3,071 participants) were completed by July 6. Of 112 trials with detailed recruitment information, 55 had recruited <20% of the targeted sample; 27 between 20-50%; and 30 over 50% (median 14.8% [IQR 2.0-62.0%]). Conclusions: The size and speed of the COVID-19 clinical trials agenda is unprecedented. However, most trials were small investigating a small fraction of treatment options. The feasibility of this research agenda is questionable, and many trials may end in futility, wasting research resources. Much better coordination is needed to respond to global health threats.

scholarly journals Advance of Novel Coronavirus Registration Clinical Trial

2020 ◽  
Author(s):  
Gao Song ◽  
Meng Qun Cheng ◽  
Xian Wen Wei
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Treatment Options ◽  
Descriptive Analysis ◽  
Clinical Trial Registry ◽  
Chi Square ◽  
Plasma Therapy ◽  
Study Duration ◽  
Design Characteristics ◽  
Novel Coronavirus

AbstractBackgroundTo analyze the characteristics and heterogeneity of clinical trials of Novel Coronavirus(COVID-19) registered in the China Clinical Trial Registry (ChiCTR), and provide data bases and information references for clinical treatmentMethodsStatistics of COVID-19 clinical trials registered with ChiCTR as of February 24, 2020 were collected. Descriptive analysis of registration characteristics. The chi-square test is used to compare statistical differences between different study types, intervention methods, study stage, and Primary sponsor.Results232 COVID-19 studies registered at the ChiCTR were collected. The overall number of COVID-19 registrations was increased. Hubei Province, China has the largest number of registrations. There were significant differences between the number of participants(P=0.000), study duration(P=0.008), study assignment(P=0.000), and blind method(P=0.000) for different study types. Significant differences could be seen in the dimensions of multicenter study(P=0.022), of participants numbe(P=0.000), study duration(P=0.000) and study assignment(P=0.001) for the four intervention methods. There were significant differences in study assignment(P=0.043) between the early and late studies. CMT drugs with high research frequency are chloroquine, lopinavir / ritonavir, and I-IFN; BI was Cell therapy, plasma therapy, Thymosin, and M/P-AB.ConclusionsDifferent study design characteristics have led to significant differences in some aspects of the COVID-19 clinical trial. Timely summary analysis can provide more treatment options and evidence for clinical practice.

scholarly journals Statistical Issues and Lessons Learned from COVID-19 Clinical Trials with Lopinavir-Ritonavir and Remdesivir

2020 ◽  
Author(s):  
Guosheng Yin ◽  
Chenyang Zhang ◽  
Huaqing Jin
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Bootstrap Method ◽  
Lessons Learned ◽  
Type I ◽  
Target Sample Size ◽  
Statistical Issues ◽  
The Difference ◽  
Mean Time

AbstractBackgroundSince the outbreak of the novel coronavirus disease 2019 (COVID-19) in December 2019, it has rapidly spread in more than 200 countries or territories with over 8 million confirmed cases and 440,000 deaths by June 17, 2020. Recently, three randomized clinical trials on COVID-19 treatments were completed, one for lopinavir-ritonavir and two for remdesivir. One trial reported that remdesivir was superior to placebo in shortening the time to recovery, while the other two showed no benefit of the treatment under investigation. However, several statistical issues in the original design and analysis of the three trials are identified, which might shed doubts on their findings and the conclusions should be evaluated with cautions.ObjectiveFrom statistical perspectives, we identify several issues in the design and analysis of three COVID-19 trials and reanalyze the data from the cumulative incidence curves in the three trials using more appropriate statistical methods.MethodsThe lopinavir-ritonavir trial enrolled 39 additional patients due to insignificant results after the sample size reached the planned number, which led to inflation of the type I error rate. The remdesivir trial of Wang et al. failed to reach the planned sample size due to a lack of eligible patients, while the bootstrap method was used to predict the quantity of clinical interest conditionally and unconditionally if the trial had continued to reach the originally planned sample size. Moreover, we used a terminal (or cure) rate model and a model-free metric known as the restricted mean survival time or the restricted mean time to improvement (RMTI) in this context to analyze the reconstructed data due to the existence of death as competing risk and a terminal event. The remdesivir trial of Beigel et al. reported the median recovery time of the remdesivir and placebo groups and the rate ratio for recovery, while both quantities depend on a particular time point representing local information. We reanalyzed the data to report other percentiles of the time to recovery and adopted the bootstrap method and permutation test to construct the confidence intervals as well as the P values. The restricted mean time to recovery (RMTR) was also computed as a global and robust measure for efficacy.ResultsFor the lopinavir-ritonavir trial, with the increase of sample size from 160 to 199, the type I error rate was inflated from 0.05 to 0.071. The difference of terminal rates was −8.74% (95% CI [-21.04, 3.55]; P=.16) and the hazards ratio (HR) adjusted for terminal rates was 1.05 (95% CI [0.78, 1.42]; P=.74), indicating no significant difference. The difference of RMTIs between the two groups evaluated at day 28 was −1.67 days (95% CI [-3.62, 0.28]; P=.09) in favor of lopinavir-ritonavir but not statistically significant. For the remdesivir trial of Wang et al., the difference of terminal rates was −0.89% (95% CI [-2.84, 1.06]; P=.19) and the HR adjusted for terminal rates was 0.92 (95% CI [0.63, 1.35]; P=.67). The difference of RMTIs at day 28 was −0.89 day (95% CI [-2.84, 1.06]; P=.37). The planned sample size was 453, yet only 236 patients were enrolled. The conditional prediction shows that the HR estimates would reach statistical significance if the target sample size had been maintained, and both conditional and unconditional prediction delivered significant HR results if the trial had continued to double the target sample size. For the remdesivir trial of Beigel et al., the difference of RMTRs between the remdesivir and placebo groups up to day 30 was −2.7 days (95% CI [-4.0, −1.2]; P<.001), confirming the superiority of remdesivir. The difference in recovery time at the 25th percentile (95% CI [-3, 0]; P=.65) was insignificant, while the differences manifested to be statistically significant at larger percentiles.ConclusionsBased on the statistical issues and lessons learned from the recent three clinical trials on COVID-19 treatments, we suggest more appropriate approaches for the design and analysis for ongoing and future COVID-19 trials.

scholarly journals Use of an individual-based model of pneumococcal carriage for planning a randomized trial of a vaccine

2018 ◽  
Author(s):  
Francisco Y. Cai ◽  
Thomas Fussell ◽  
Sarah E. Cobey ◽  
Marc Lipsitch
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Simulation Studies ◽  
Pneumococcal Carriage ◽  
Vaccine Trials ◽  
Asymptomatic Carriage ◽  
Younger Age ◽  
Proportional Effect ◽  
Study Participants ◽  
The Relationship

AbstractFor encapsulated bacteria such asStreptococcus pneumoniae, asymptomatic carriage is more common and longer in duration than disease, and hence is often a more convenient endpoint for clinical trials of vaccines against these bacteria. However, using a carriage endpoint entails specific challenges. Carriage is almost always measured as prevalence, whereas the vaccine may act by reducing incidence or duration. Thus, to determine sample size requirements, its impact on prevalence must first be estimated. The relationship between incidence and prevalence (or duration and prevalence) is convex, saturating at 100% prevalence. For this reason, the proportional effect of a vaccine on prevalence is typically less than its proportional effect on incidence or duration. This relationship is further complicated in the presence of multiple pathogen strains. In addition, host immunity to carriage accumulates rapidly with frequent exposures in early years of life, creating potentially complex interactions with the vaccine’s effect. We conducted a simulation study to predict the impact of an inactivated whole cell pneumococcal vaccine—believed to reduce carriage duration—on carriage prevalence in different age groups and trial settings. We used an individual-based model of pneumococcal carriage that incorporates relevant immunological processes, both vaccine-induced and naturally acquired. Our simulations showed that for a wide range of vaccine efficacies, sampling time and age at vaccination are important determinants of sample size. There is a window of favorable sampling times during which the required sample size is relatively low, and this window is prolonged with a younger age at vaccination, and in a trial setting with lower transmission intensity. These results illustrate the ability of simulation studies to inform the planning of vaccine trials with carriage endpoints, and the methods we present here can be applied to trials evaluating other pneumococcal vaccine candidates or comparing alternative dosing schedules for the existing conjugate vaccines.Author SummaryStreptococcus pneumoniae, a bacterium carried in the nasopharynx of many healthy people, is also a leading cause of bacterial pneumonia, sepsis, and ear infections in children aged five years and younger. Vaccines targeting select strains ofS. pneumoniaehave been effective, and the development of new vaccines, particularly those that target all strains, can further lower disease burden. For clinical trials of these vaccines, the number of study participants needed depends on the expected effect of the vaccine on a conveniently measured outcome: asymptomatic carriage. The most economical way to test a vaccine for its effect on carriage is by measuring prevalence at a specific time, and comparing vaccinated to unvaccinated participants. The relationship between incidence (or duration) and prevalence is complex, and changes with time as children develop natural immunity. We explored this relationship using a mathematical model. Given a vaccine efficacy, our computer simulations predict that fewer study participants are needed if they are vaccinated at a younger age, taken from a population with intermediate levels of transmission, and sampled for carriage at a certain time window: 9 to 18 months after vaccination. Our study illustrates how simulation studies can help plan more efficient vaccine trials.

Planning the Size of Clinical Trials with Allowance for Patient Noncompliance

1990 ◽  
Vol 29 (03) ◽  
pp. 243-246 ◽  
Author(s):  
M. A. A. Moussa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Event Rate ◽  
Survival Functions ◽  
Time Intervals ◽  
Patient Noncompliance ◽  
Event Rates ◽  
Larger Sample

AbstractVarious approaches are considered for adjustment of clinical trial size for patient noncompliance. Such approaches either model the effect of noncompliance through comparison of two survival distributions or two simple proportions. Models that allow for variation of noncompliance and event rates between time intervals are also considered. The approach that models the noncompliance adjustment on the basis of survival functions is conservative and hence requires larger sample size. The model to be selected for noncompliance adjustment depends upon available estimates of noncompliance and event rate patterns.

ACE-2-Receptors of the Epidermis, Dermal Vascular Walls and Sebaceous Gland Cells: The Way of COVID-19 Entry into the Body?

2020 ◽  
Author(s):  
Stefan Bittmann
Keyword(s):  
Clinical Trials ◽  
Viral Infection ◽  
Sebaceous Gland ◽  
The Body ◽  
Viral Agent ◽  
Therapy Options ◽  
Vascular Walls ◽  
Fish Market ◽  
The World ◽  
The Usa

Since the outbreak near a fish market in Wuhan, China, in December 2019, researchers have been searching for an effective therapy to control the spreading of the new coronavirus SARS-CoV-2 and inhibit COVID-19 infection. Many countries like Italy, Spain, and the USA were ambushed by this viral agent. To date, more than 2.5 million people were infected with SARS-CoV-2. There is no clear answer, why SARS-CoV-2 infects so many people so fast. To date of April 2020, no effective drug has been found to treat this new severe viral infection. There are many therapy options under review and clinical trials were initiated to get clearer information, what kind of drug can help in this devastating and serious situation. The world has no time.

The Use of Digital Clinical Trial Methods in the Evaluation of Digital Therapeutics: A Scoping Review (Preprint)

2019 ◽  
Author(s):  
Allison Hirsch ◽  
Mahip Grewal ◽  
Anthony James Martorell ◽  
Brian Michael Iacoviello
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Research ◽  
Scoping Review ◽  
Digital Technologies ◽  
Good Clinical Practice ◽  
The United States ◽  
Clinical Trial Research ◽  
Digital Methods ◽  
Clinical Trial Methods

BACKGROUND Digital Therapeutics (DTx) provide evidence based therapeutic health interventions that have been clinically validated to deliver therapeutic outcomes, such that the software is the treatment. Digital methodologies are increasingly adopted to conduct clinical trials due to advantages they provide including increases in efficiency and decreases in trial costs. Digital therapeutics are digital by design and can leverage the potential of digital and remote clinical trial methods. OBJECTIVE The principal purpose of this scoping review is to review the literature to determine whether digital technologies are being used in DTx clinical research, which type are being used and whether publications are noting any advantages to their use. As DTx development is an emerging field there are likely gaps in the knowledge base regarding DTx and clinical trials, and the purpose of this review is to illuminate those gaps. A secondary purpose is to consider questions which emerged during the review process including whether fully remote digital clinical research is appropriate for all health conditions and whether digital clinical trial methods are inline with the principles of Good Clinical Practice. METHODS 1,326 records were identified by searching research databases and 1,227 reviewed at the full-article level in order to determine if they were appropriate for inclusion. Confirmation of clinical trial status, use of digital clinical research methods and digital therapeutic status as well as inclusion and exclusion criteria were applied in order to determine relevant articles. Digital methods employed in DTx research were extracted from each article and these data were synthesized in order to determine which digital methods are currently used in clinical trial research. RESULTS After applying our criteria for scoping review inclusion, 11 articles were identified. All articles used at least one form of digital clinical research methodology enabling an element of remote research. The most commonly used digital methods are those related to recruitment, enrollment and the assessment of outcomes. A small number of articles reported using other methods such as online compensation (n = 3), or digital reminders for participants (n = 5). The majority of digital therapeutics clinical research using digital methods is conducted in the United States and increasing number of articles using digital methods are published each year. CONCLUSIONS Digital methods are used in clinical trial research evaluating DTx, though not frequently as evidenced by the low proportion of articles included in this review. Fully remote clinical trial research is not yet the standard, more frequently authors are using partially remote methods. Additionally, there is tremendous variability in the level of detail describing digital methods within the literature. As digital technologies continue to advance and the clinical research DTx literature matures, digital methods which facilitate remote research may be used more frequently.

scholarly journals Therapeutic clinical trials to combat COVID-19 pandemic in India: analysis from trial registry

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Angelika Batta ◽  
Raj Khirasaria ◽  
Vinod Kapoor ◽  
Deepansh Varshney
Keyword(s):  
Clinical Trials ◽  
De Novo ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Trial Registry ◽  
Therapeutic Treatment ◽  
Clinical Trial Registry ◽  
Clear Cut ◽  
Therapeutic Trials ◽  
Microsoft Office

AbstractObjectivesWith the emergence of Novel corona virus, hunt for finding a preventive and therapeutic treatment options has already begun at a rapid pace with faster clinical development programs. The present study was carried out to give an insight of therapeutic interventional trials registered under clinical trial registry of India (CTRI) for COVID-19 pandemic.MethodsAll trials registered under CTRI were evaluated using keyword “COVID” from its inception till 9th June 2020. Out of which, therapeutic interventional studies were chosen for further analysis. Following information was collected for each trial: type of therapeutic intervention (preventive/therapeutic), treatment given, no. of centers (single center/multicentric), type of institution (government/private), study design (randomized/single-blinded/double-blinded) and sponsors (Government/private). Microsoft Office Excel 2007 was used for tabulation and analysis.ResultsThe search yielded total of 205 trials, out of which, 127 (62%) trials were interventional trials. Out of these, 71 (56%) were AYUSH interventions, 36 (28.3%) tested drugs, 9 (7%) tested a nondrug intervention, rest were nutraceuticals and vaccines. About 66 (56%) were therapeutic trials. Majority were single-centered trials, i.e. 87 (73.7%). Trials were government funded in 57 (48.3%) studies. Majority were randomized controlled trials, i.e. 67 (56.8%). AYUSH preparations included AYUSH-64, Arsenic Album, SamshamaniVati etc.ConclusionsThe number of therapeutic interventional clinical trials was fair in India. A clear-cut need exists for an increase in both quantity and quality of clinical trials for COVID-19. Drug repurposing approach in all systems of medicine can facilitate prompt clinical decisions at lower costs than de novo drug development.

scholarly journals Variation in treatment preferences of pulmonary exacerbations among Australian and New Zealand cystic fibrosis physicians

2021 ◽  
Vol 8 (1) ◽  
pp. e000956
Author(s):  
Grace Currie ◽  
Anna Tai ◽  
Tom Snelling ◽  
André Schultz
Keyword(s):  
Cystic Fibrosis ◽  
Clinical Trials ◽  
New Zealand ◽  
Treatment Options ◽  
Early Response ◽  
Treatment Preferences ◽  
Dornase Alfa ◽  
Clinical Scenarios ◽  
Professional Opinion ◽  
Pulmonary Exacerbations

BackgroundDespite advances in cystic fibrosis (CF) management and survival, the optimal treatment of pulmonary exacerbations remains unclear. Understanding the variability in treatment approaches among physicians might help prioritise clinical uncertainties to address through clinical trials.MethodsPhysicians from Australia and New Zealand who care for people with CF were invited to participate in a web survey of treatment preferences for CF pulmonary exacerbations. Six typical clinical scenarios were presented; three to paediatric and another three to adult physicians. For each scenario, physicians were asked to choose treatment options and provide reasons for their choices.ResultsForty-nine CF physicians (31 paediatric and 18 adult medicine) participated; more than half reported 10+ years of experience. There was considerable variation in primary antibiotic selection; none was preferred by more than half of respondents in any scenario. For secondary antibiotic therapy, respondents consistently preferred intravenous tobramycin and a third antibiotic was rarely prescribed, except in one scenario describing an adult patient. Hypertonic saline nebulisation and twice daily chest physiotherapy was preferred in most scenarios while dornase alfa use was more variable. Most CF physicians (>80%) preferred to change therapy if there was no early response. Professional opinion was the most common reason for antibiotic choice.ConclusionsVariation exists among CF physicians in their preferred choice of primary antibiotic and use of dornase alfa. These preferences are driven by professional opinion, possibly reflecting a lack of evidence to base policy recommendations. Evidence from high-quality clinical trials is needed to inform physician decision making.

scholarly journals Do patient characteristics matter when calculating sample size for eczema clinical trials?

2021 ◽  
Author(s):  
L. Howells ◽  
S. Gran ◽  
J. R. Chalmers ◽  
B. Stuart ◽  
M. Santer ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Patient Characteristics
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