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.

scholarly journals Approaches for Structural Investigations of Binary Data Using Confirmatory Factor Models

2018 ◽  
Vol 7 (6) ◽  
pp. 68
Author(s):  
Karl Schweizer ◽  
Siegbert Reiß ◽  
Stefan Troche
Keyword(s):  
Sample Size ◽  
Binary Data ◽  
Factor Models ◽  
The Other ◽  
Simulation Studies ◽  
Sample Sizes ◽  
Structural Investigations ◽  
Ml Estimation ◽  
Confirmatory Factor ◽  
The Relationship

An investigation of the suitability of threshold-based and threshold-free approaches for structural investigations of binary data is reported. Both approaches implicitly establish a relationship between binary data following the binomial distribution on one hand and continuous random variables assuming a normal distribution on the other hand. In two simulation studies we investigated: whether the fit results confirm the establishment of such a relationship, whether the differences between correct and incorrect models are retained and to what degree the sample size influences the results. Both approaches proved to establish the relationship. Using the threshold-free approach it was achieved by customary ML estimation whereas robust ML estimation was necessary in the threshold-based approach. Discrimination between correct and incorrect models was observed for both approaches. Larger CFI differences were found for the threshold-free approach than for the threshold-based approach. Dependency on sample size characterized the threshold-based approach but not the threshold-free approach. The threshold-based approach tended to perform better in large sample sizes, while the threshold-free approach performed better in smaller sample sizes.

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 Testing for baseline differences in clinical trials

2020 ◽  
Vol 7 (2) ◽  
pp. 150
Author(s):  
Henian Chen ◽  
Yuanyuan Lu ◽  
Nicole Slye
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sofa Score ◽  
Statistical Tests ◽  
Statistical Significance ◽  
P Value ◽  
Large Trial ◽  
P Values ◽  
Failure Assessment ◽  
The Relationship

<p class="abstract">Reporting statistical tests for baseline measures of clinical trials does not make sense since the statistical significance is dependent on sample size, as a large trial can find significance in the same difference that a small trial did not find to be statistically significant. We use 3 published trials using the same baseline measures to provide the relationship between trial sample size and p value. For trial 1 sequential organ failure assessment (SOFA) score, p=0.01, 10.4±3.4 vs. 9.6±3.2, difference=0.8; p=0.007 for vasopressors, 83.0% vs. 72.6%. Trial 2 has SOFA score 11±3 vs. 12±3, difference=1, p=0.42. Trial 3 has vasopressors 73% vs. 83%, p=0.21. Based on trial 2, supine group has a mean of 12 and an SD of 3 for SOFA score, while prone group has a mean of 11 and an SD of 3 for SOFA score. The p values are 0.29850, 0.09877, 0.01940, 0.00094, 0.00005, and &lt;0.00001 when n (per arm) is 20, 50, 100, 200, 300 and 400, respectively. Based on trial 3 information, the vasopressors percentages are 73.0% in the supine group vs. 83.0% in the prone group. The p values are 0.4452, 0.2274, 0.0878, 0.0158, 0.0031, and 0.0006 when n (per arm) is 20, 50, 100, 200, 300 and 400, respectively. Small trials provide larger p values than big trials for the same baseline differences. We cannot define the imbalance in baseline measures only based on these p values. There is no statistical basis for advocating the baseline difference tests</p>

Randomized Clinical Trials in Surgery

1989 ◽  
Vol 5 (3) ◽  
pp. 317-332 ◽  
Author(s):  
Bucknam McPeek ◽  
Frederick Mosteller ◽  
Martin McKneally
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Comparative Effectiveness ◽  
Randomized Clinical Trials ◽  
Controlled Trial ◽  
Causal Link ◽  
Clinical Settings ◽  
Two Generations ◽  
The Relationship

When it is well conducted, a randomized clinical provides the strongest evidence available for evaluating the comparative effectiveness of the interventions tested. Over the last two generations, we have learned much about various devices for strengthening them and about methods of avoiding between in their design, execution, analysis, and reporting. In a trial, we seek evidence for a causal link between treatment and observed outcomes. Becaues the controlled trial depends on an argument based on exculsion (i.e., no other causes or differences affected the experimental groups), we strengthen its inference by taking steps to exclude any such differences.This article discusses a number of issues that deserve consideration: problems of multiplicity and generalizability, devices for strengthening trials, issues of power and sample size, the relationship between study design and reported gains, when to undertake a trial, the role of collaborative trials, and ways to make trials more feasible in clinical settings.

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.

HPV VACCINE AND CERVICAL CANCER

2011 ◽  
pp. 108-115
Author(s):  
Vu Quoc Huy Nguyen
Keyword(s):  
Cervical Cancer ◽  
Relative Risk ◽  
Hpv Vaccine ◽  
Hpv Vaccination ◽  
Preventive Strategy ◽  
Protective Effects ◽  
Papilloma Virus ◽  
Vaccine Trials ◽  
Close Relationship ◽  
The Relationship

Persistent infection with high-risk Human Papilloma Virus (HPV) has been identified as the causal factor of cervical cancer, with relative risk up to 300-400 folds. This very close relationship leads to the preventive strategy of vaccination against HPV infections and HPV-related lesions. The article describes molecular and immunologic characteristics of HPV, currently available HPV vaccines and its protective effects; the relationship between HPV vaccination and cervical cancer screening, and an introduction to therapeutic HPV vaccine trials.

A Biomarker Combining Imaging and Neuropsychological Assessment for Tracking Early Alzheimer's Disease in Clinical Trials

2018 ◽  
Vol 15 (5) ◽  
pp. 429-442 ◽  
Author(s):  
Nishant Verma ◽  
S. Natasha Beretvas ◽  
Belen Pascual ◽  
Joseph C. Masdeu ◽  
Mia K. Markey ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Latent Variable ◽  
Brain Regions ◽  
Disease Assessment ◽  
Latent Variable Analysis ◽  
The Relationship ◽  
Selection Of

Background: Combining optimized cognitive (Alzheimer's Disease Assessment Scale- Cognitive subscale, ADAS-Cog) and atrophy markers of Alzheimer's disease for tracking progression in clinical trials may provide greater sensitivity than currently used methods, which have yielded negative results in multiple recent trials. Furthermore, it is critical to clarify the relationship among the subcomponents yielded by cognitive and imaging testing, to address the symptomatic and anatomical variability of Alzheimer's disease. Method: Using latent variable analysis, we thoroughly investigated the relationship between cognitive impairment, as assessed on the ADAS-Cog, and cerebral atrophy. A biomarker was developed for Alzheimer's clinical trials that combines cognitive and atrophy markers. Results: Atrophy within specific brain regions was found to be closely related with impairment in cognitive domains of memory, language, and praxis. The proposed biomarker showed significantly better sensitivity in tracking progression of cognitive impairment than the ADAS-Cog in simulated trials and a real world problem. The biomarker also improved the selection of MCI patients (78.8±4.9% specificity at 80% sensitivity) that will evolve to Alzheimer's disease for clinical trials. Conclusion: The proposed biomarker provides a boost to the efficacy of clinical trials focused in the mild cognitive impairment (MCI) stage by significantly improving the sensitivity to detect treatment effects and improving the selection of MCI patients that will evolve to Alzheimer’s disease.

scholarly journals Influence of chronotype on daily mood fluctuations: pilot study in patients with depression

BJPsych Open ◽  
2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Konstantin F. Brückmann ◽  
Jürgen Hennig ◽  
Matthias J. Müller ◽  
Stanislava Fockenberg ◽  
Anne-Marthe Schmidt ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Pilot Study ◽  
Negative Affect ◽  
Clinical Setting ◽  
Early Morning ◽  
Healthy Controls ◽  
Patient Subgroup ◽  
Depression Risk ◽  
Gender And Age ◽  
The Relationship

Summary Depression risk is associated with a late chronotype pattern often described as an ‘evening chronotype’. Fluctuations in mood over consecutive days have not yet been measured according to chronotype in in-patients with depression. A total of 30 in-patients with depression and 32 healthy controls matched for gender and age completed a chronotype questionnaire and twice-daily ratings on mood for 10 consecutive days (registered in the German Clinical Trials Register: DRKS00010215). The in-patients had Saturdays and Sundays as hospital-leave days. The relationship between chronotype and daily mood was mediated by the weekday–weekend schedule with higher levels of negative affect in the evening-chronotype patient subgroup at weekends. Results are discussed with respect to a probably advantageous standardised clinical setting with early morning routines, especially for patients with evening chronotypes.

scholarly journals Uric Acid and Hypertension: Prognostic Role and Guide for Treatment

2021 ◽  
Vol 10 (3) ◽  
pp. 448
Author(s):  
Federica Piani ◽  
Arrigo F. G. Cicero ◽  
Claudio Borghi
Keyword(s):  
Clinical Trials ◽  
Uric Acid ◽  
Mendelian Randomization ◽  
Strong Association ◽  
Epidemiological Studies ◽  
Hypertensive Disease ◽  
Genetic Studies ◽  
Lowering Therapy ◽  
The Relationship

The relationship between serum uric acid (SUA) and hypertension has been a subject of increasing interest since the 1870 discovery by Frederick Akbar Mahomed. Several epidemiological studies have shown a strong association between high SUA levels and the presence or the development of hypertension. Genetic analyses have found that xanthine oxidoreductase (XOR) genetic polymorphisms are associated with hypertension. However, genetic studies on urate transporters and Mendelian randomization studies failed to demonstrate a causal relationship between SUA and hypertension. Results from clinical trials on the role of urate-lowering therapy in the management of patients with hypertension are not uniform. Our study sought to analyze the prognostic and therapeutic role of SUA in the hypertensive disease, from uric acid (UA) biology to clinical trials on urate-lowering therapies.

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