scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research

10.2196/26718 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e26718
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research (Preprint)

2020 ◽  
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

UNSTRUCTURED This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.

scholarly journals Nervous and Muscular Adverse Events after COVID-19 Vaccination: A Systematic Review and Meta-Analysis of Clinical Trials

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 939
Author(s):  
Jiaxin Chen ◽  
Yuangui Cai ◽  
Yicong Chen ◽  
Anthony P. Williams ◽  
Yifang Gao ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Adverse Events ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Phase 1 ◽  
Cochrane Library ◽  
Categorical Variables ◽  
Control Group ◽  
Open Label

Background: Nervous and muscular adverse events (NMAEs) have garnered considerable attention after the vaccination against coronavirus disease (COVID-19). However, the incidences of NMAEs remain unclear. We aimed to calculate the pooled event rate of NMAEs after COVID-19 vaccination. Methods: A systematic review and meta-analysis of clinical trials on the incidences of NMAEs after COVID-19 vaccination was conducted. The PubMed, Medline, Embase, Cochrane Library, and Chinese National Knowledge Infrastructure databases were searched from inception to 2 June 2021. Two independent reviewers selected the study and extracted the data. Categorical variables were analyzed using Pearson’s chi-square test. The pooled odds ratio (OR) with the corresponding 95% confidence intervals (CIs) were estimated and generated with random or fixed effects models. The protocol of the present study was registered on PROSPERO (CRD42021240450). Results: In 15 phase 1/2 trials, NMAEs occurred in 29.2% vs. 21.6% (p < 0.001) vaccinated participants and controls. Headache and myalgia accounted for 98.2% and 97.7%, and their incidences were 16.4% vs. 13.9% (OR = 1.97, 95% CI = 1.28–3.06, p = 0.002) and 16.0% vs. 7.9% (OR = 3.31, 95% CI = 2.05–5.35, p < 0.001) in the vaccine and control groups, respectively. Headache and myalgia were more frequent in the newly licensed vaccines (OR = 1.97, 95% CI = 1.28–3.06, p = 0.02 and OR = 3.31, 95% CI = 2.05–5.35, p < 0.001) and younger adults (OR = 1.40, 95% CI = 1.12–1.75, p = 0.003 and OR = 1.54, 95% CI = 1.20–1.96, p < 0.001). In four open-label trials, the incidences of headache, myalgia, and unsolicited NMAEs were 38.7%, 27.4%, and 1.5%. Following vaccination in phase 3 trials, headache and myalgia were still common with a rate of 29.5% and 19.2%, although the unsolicited NMAEs with incidence rates of ≤ 0.7% were not different from the control group in each study. Conclusions: Following the vaccination, NMAEs are common of which headache and myalgia comprised a considerable measure, although life-threatening unsolicited events are rare. NMAEs should be continuously monitored during the ongoing global COVID-19 vaccination program.

Large-scale randomized evidence: Trials and meta-analyses of trials

2020 ◽  
pp. 51-66
Author(s):  
Colin Baigent ◽  
Richard Peto ◽  
Richard Gray ◽  
Natalie Staplin ◽  
Sarah Parish ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Large Scale ◽  
Meta Analysis ◽  
False Negative ◽  
Premature Deaths ◽  
Common Causes ◽  
The Common ◽  
Meta Analyses ◽  
Positive Results

Clinical trials generally need to be able to detect or to refute realistically moderate (but still worthwhile) differences between treatments in long-term disease outcome. Large-scale randomized evidence should be able to detect such effects, but medium-sized trials or medium-sized meta-analyses can, and often do, yield false-negative or exaggeratedly positive results. Hundreds of thousands of premature deaths each year could be avoided by seeking appropriately large-scale randomized evidence about various widely practicable treatments for the common causes of death, and by disseminating this evidence appropriately. This chapter takes a look at the use of large-scale randomized evidence—produced from trials and meta-analysis of trials—and how this data should be handled in order to produce accurate result.

scholarly journals Design and analysis of a clinical trial using previous trials as historical control

2019 ◽  
Vol 16 (5) ◽  
pp. 531-538 ◽  
Author(s):  
David Alan Schoenfeld ◽  
Dianne M Finkelstein ◽  
Eric Macklin ◽  
Neta Zach ◽  
David L Ennist ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Amyotrophic Lateral Sclerosis ◽  
Sample Size ◽  
Standard Error ◽  
Controlled Trial ◽  
Sample Size Calculation ◽  
Control Group ◽  
Controlled Trials ◽  
Number Of Patients ◽  
Lateral Sclerosis

Background/AimsFor single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation.MethodsWe fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials.ResultsWe find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial.ConclusionThis article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

Therapeutic efficacy of specific immunotherapy for glioma: a systematic review and meta-analysis

2018 ◽  
Vol 29 (4) ◽  
pp. 443-461 ◽  
Author(s):  
Sara Hanaei ◽  
Khashayar Afshari ◽  
Armin Hirbod-Mobarakeh ◽  
Bahram Mohajer ◽  
Delara Amir Dastmalchi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Overall Survival ◽  
Clinical Trials ◽  
Specific Immunotherapy ◽  
Data Extraction ◽  
Meta Analysis ◽  
Progression Free Survival ◽  
Control Group ◽  
Free Survival ◽  
Median Difference

Abstract Although different immunotherapeutic approaches have been developed for the treatment of glioma, there is a discrepancy between clinical trials limiting their approval as common treatment. So, the current systematic review and meta-analysis were conducted to assess survival and clinical response of specific immunotherapy in patients with glioma. Generally, seven databases were searched to find eligible studies. Controlled clinical trials investigating the efficacy of specific immunotherapy in glioma were found eligible. After data extraction and risk of bias assessment, the data were analyzed based on the level of heterogeneity. Overall, 25 articles with 2964 patients were included. Generally, mean overall survival did not statistically improve in immunotherapy [median difference=1.51; 95% confidence interval (CI)=−0.16–3.17; p=0.08]; however, it was 11.16 months higher in passive immunotherapy (95% CI=5.69–16.64; p<0.0001). One-year overall survival was significantly higher in immunotherapy groups [hazard ratio (HR)=0.69; 95% CI=0.52–0.92; p=0.01]. As the hazard rate in the immunotherapy approach was 0.83 of the control group, 2-year overall survival was significantly higher in immunotherapy (HR=0.83; 95% CI=0.69–0.99; p=0.04). Three-year overall survival was significantly higher in immunotherapy as well (HR=0.67; 95% CI=0.48–0.92; p=0.01). Overall, median progression-free survival was significantly higher in immunotherapy (standard median difference=0.323; 95% CI=0.110–0.536; p=0.003). However, 1-year progression-free survival was not remarkably different between immunotherapy and control groups (HR=0.94; 95% CI=0.74–1.18; p=0.59). Specific immunotherapy demonstrated remarkable improvement in survival of patients with glioma and could be a considerable choice of treatment in the future. Despite the current promising results, further high-quality randomized controlled trials are required to approve immunotherapeutic approaches as the standard of care and the front-line treatment for glioma.

scholarly journals Meta-analysis of randomized clinical trials in the era of individual patient data sharing

2018 ◽  
Vol 23 (3) ◽  
pp. 403-409 ◽  
Author(s):  
Takuya Kawahara ◽  
Musashi Fukuda ◽  
Koji Oba ◽  
Junichi Sakamoto ◽  
Marc Buyse
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Randomized Clinical Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data

scholarly journals Meta-analysis of effect of renin–angiotensin–aldosterone system blockers on contrast-induced nephropathy

2020 ◽  
Vol 21 (2) ◽  
pp. 147032032091958
Author(s):  
Weidong Wang ◽  
Wei Qu ◽  
Dan Sun ◽  
Xiaodan Liu
Keyword(s):  
Older People ◽  
Sample Size ◽  
Meta Analysis ◽  
Control Group ◽  
Inclusion Criteria ◽  
Contrast Induced Nephropathy ◽  
Renin Angiotensin Aldosterone System ◽  
Renin Angiotensin ◽  
Increased Risk ◽  
Larger Sample

Background: The purpose of this study was to systematically evaluate the effect of renin–angiotensin–aldosterone system blockers on the incidence of contrast-induced nephropathy in patients undergoing coronary angiography or percutaneous coronary intervention. Methods: A systematic literature search of several databases was conducted to identify studies that met the inclusion criteria. A total of 12 studies with 14 trials that performed studies on a total of 4864 patients (2484 treated with renin–angiotensin–aldosterone system blockers and 2380 in the control group) were included. The primary endpoint was the overall incidence of contrast-induced nephropathy. Analyses were performed with STATA version 12.0. Results: The overall contrast-induced nephropathy incidence in renin–angiotensin–aldosterone system blocker and control groups was 10.43% and 6.81%, respectively. The pooled relative risk of contrast-induced nephropathy incidence was 1.22 (95% confidence interval: 0.81–1.84) in the renin–angiotensin–aldosterone system blocker group. An increased risk of developing contrast-induced nephropathy in the renin–angiotensin–aldosterone system blocker group was observed among older people, non-Asians, chronic users, and studies with larger sample size, and the pooled RRs and 95% confidence intervals were 2.02 (1.21–3.36), 2.30 (1.41–3.76), 1.69 (1.10–2.59) and 1.83 (1.28–2.63), respectively. Conclusions: Intervention with renin–angiotensin–aldosterone system blockers was associated with an increased risk of contrast-induced nephropathy among non-Asians, chronic users, older people, and studies with larger sample size. Large clinical trials with strict inclusion criteria are needed to confirm our results and to evaluate the effect further.

scholarly journals Acupuncture for Chronic Pain-Related Insomnia: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Fushui Liu ◽  
Jianyu You ◽  
Qi Li ◽  
Ting Fang ◽  
Mei Chen ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Sample Size ◽  
Small Sample Size ◽  
Meta Analysis ◽  
Sham Acupuncture ◽  
Small Sample ◽  
Control Group ◽  
Large Sample Size ◽  
Cure Rate ◽  
Significant Difference

Objectives. Acupuncture has been widely used to relieve chronic pain-related insomnia (CPRI). However, the efficacy of acupuncture for CPRI is uncertain. The purpose of this study was to evaluate the efficacy of acupuncture for CPRI. Methods. Seven electronic databases were searched from inception to December 2018. Randomized controlled trials (RCTs) were included if acupuncture was compared to sham acupuncture or conventional drug therapies for treating CPRI. Two reviewers screened each study and extracted data independently. Statistical analyses were conducted by RevMan 5.3 software. Results. A total of nine studies involving 944 patients were enrolled. The pooled analysis indicated that acupuncture treatment was significantly better than control group in improving effective rate (OR = 8.09, 95%CI = [4.75, 13.79], P < 0.00001) and cure rate (OR = 3.17, 95%CI = [2.35, 4.29], P < 0.00001), but subgroup analysis showed that there was no statistically significant difference between acupuncture and sham acupuncture in improving cure rate (OR =10.36, 95% CI [0.53, 201.45], P=0.12) based on one included study. In addition, meta-analysis demonstrated that acupuncture group was superior to control group in debasing PSQI score (MD = -2.65, 95%CI = [-4.00, -1.30], P = 0.0001) and VAS score (MD = -1.44, 95%CI = [-1.58, -1.29], P < 0.00001). And there was no significant difference in adverse events (OR =1.73, 95%CI = [0.92, 3.25], P =0.09) between the two groups. Conclusions. Acupuncture therapy is an effective and safe treatment for CPRI, and this treatment can be recommended for the management of patients with CPRI. Due to the low quality and small sample size of the included studies, more rigorously designed RCTs with high quality and large sample size are recommended in future.

scholarly journals The Effect of Zinc Supplementation on Serum Leptin Levels: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

2019 ◽  
Vol 51 (08) ◽  
pp. 503-510
Author(s):  
Masoud Khorshidi ◽  
Meysam Zarezadeh ◽  
Alireza Sadeghi ◽  
Alireza Teymouri ◽  
Mohammad Reza Emami ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Randomized Controlled Trials ◽  
Zinc Supplementation ◽  
Meta Analysis ◽  
Control Group ◽  
Controlled Trials ◽  
Serum Leptin ◽  
Randomized Controlled ◽  
Human Adults ◽  
The Mean

AbstractRecently, obesity has become a common worldwide concern. Leptin, as an adipocytokine, plays a major role in the etiology of obesity. Prior studies have demonstrated that zinc potentially affects serum leptin levels. However, clinical trials carried out in this regard are not consistent. Therefore, current meta-analysis was conducted to ascertain the actual effect of zinc supplementation on serum leptin levels in adults. Databases of PubMed, SCOPUS, and Google Scholar were methodically searched to identify relevant articles up to April 2018. Clinical trials that examined the effect of zinc supplementation on serum leptin concentrations as outcome variables in human adults were included. The mean difference (SD) of leptin changes in the intervention and placebo groups were used to calculate the overall effect size. Totally, 663 articles were identified, of which 6 studies were eligible randomized controlled trials (RCTs) with 7 treatment arms. The analysis suggested that zinc supplementation exerts no significant effect on overall serum leptin (WMD: 0.74 ng/ml; 95% CI: −1.39 to 2.87, p=0.49). Nevertheless, sex and duration of intervention seemed to impact the extent of zinc’s influence. In trials with female subjects, zinc consumption led to a significant decrease in serum leptin level (WMD: −1.93 ng/ml; 95% CI: −3.72 to −0.14, p=0.03) as well as trials that lasted for more than 6 weeks (WMD: −1.71 ng/ml; 95% CI: −3.07 to −0.35, p=0.01), in comparison to the control group. Zinc supplementation did not significantly improve leptin concentrations, but it may result in a decreased circulating leptin level in studies with a duration of more than 6 weeks especially among females.

Sign in / Sign up

Export Citation Format

Share Document