Principal Surrogates in Context of High Vaccine Efficacy

2019 ◽  
Author(s):  
Andrea Callegaro ◽  
Tibaldi Fabian ◽  
Dean Follmann
Keyword(s):  
Statistical Methods ◽  
Simulation Study ◽  
Vaccine Efficacy ◽  
Principal Stratification ◽  
Clinical Endpoint ◽  
Correlates Of Protection ◽  
The Impact ◽  
Very High

Abstract Background: The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. Vaccines with very high vaccine efficacy (VE) are documented in the literature (95% or above). Callegaro and Tibaldi, (2019) showed that the rare infections observed in thevaccinated groups of these trials poses challenges when applying conventionally-used statistical methods for CoP assessment such as the Prentice criteria and meta-analysis.Methods: In this paper, we extended Callegaro and Tibaldi, (2019) simulation study by evaluating the impact of high VE on the Principal stratification approach.Results: Similarly to the Prentice framework, we showed that the power decreases when the VE grows. It follows that it can be challenging to validate a principal surrogate (and a statistical surrogate) when rare infections are observed in the vaccinated groups.

scholarly journals Principal surrogates in context of high vaccine efficacy

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrea Callegaro ◽  
Fabian Tibaldi ◽  
Dean Follmann
Keyword(s):  
Vaccine Efficacy ◽  
Meta Analysis ◽  
Principal Stratification ◽  
Simulation Experiments ◽  
Correlates Of Protection ◽  
Vaccine Trials ◽  
Simulation Results ◽  
The Impact ◽  
Statistical Solutions ◽  
Very High

Abstract Objectives The use of correlates of protection (CoPs) in vaccination trials offers significant advantages as useful clinical endpoint substitutes. Vaccines with very high vaccine efficacy (VE) are documented in the literature (95% or above). Callegaro, A., and F. Tibaldi. 2019. “Assessing Correlates of Protection in Vaccine Trials: Statistical Solutions in the Context of High Vaccine Efficacy.” BMC Medical Research Methodology 19: 47 showed that the rare infections observed in the vaccinated groups of these trials poses challenges when applying conventionally-used statistical methods for CoP assessment such as the Prentice criteria and meta-analysis. The objective of this work is to investigate the impact of this problem on another statistical method for the assessment of CoPs called Principal stratification. Methods We perform simulation experiments to investigate the effect of high vaccine efficacy on the performance of the Principal Stratification approach. Results Similarly to the Prentice framework, simulation results show that the power of the Principal Stratification approach decreases when the VE grows. Conclusions It can be challenging to validate principal surrogates (and statistical surrogates) for vaccines with very high vaccine efficacy.

scholarly journals Quantifying the Impact of Natural Immunity on Rotavirus Vaccine Efficacy Estimates: A Clinical Trial in Dhaka, Bangladesh (PROVIDE) and a Simulation Study

2018 ◽  
Vol 217 (6) ◽  
pp. 861-868 ◽  
Author(s):  
Elizabeth T Rogawski ◽  
James A Platts-Mills ◽  
E Ross Colgate ◽  
Rashidul Haque ◽  
K Zaman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Simulation Study ◽  
Vaccine Efficacy ◽  
Natural Immunity ◽  
Rotavirus Vaccine ◽  
The Impact

scholarly journals Continuous Numerical Analysis of Slug Rivet Installation Process Using Parameterized Modeling Method

Coatings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 189
Author(s):  
Changyi Lei ◽  
Yunbo Bi ◽  
Jiangxiong Li
Keyword(s):  
Data Processing ◽  
Simulation Study ◽  
Modeling Method ◽  
Manual Intervention ◽  
Modeling Process ◽  
Parameterized Modeling ◽  
Parameterized Model ◽  
The Impact ◽  
Slug Rivet ◽  
Very High

The slug rivet installation process is complex. A lot of parameters are included during the riveting deformation process. The workload and time cost of a traditional simulation study is very high since a traditional numerical model should be modified manually time by time when riveting parameters change. The data processing after simulation is another complex work. To improve the situation, this paper presents a parameterized modeling method. The modeling process and data processing algorithm can be developed using Python script. The parameterized model can automatically and continuously re-build without any manual intervention according to the riveting parameter auto-update condition. The post-processing analysis can be automatically conducted and saved as well. Then this paper conducts continuous analysis to illustrate the impact of riveting parameters on riveting quality. The parameterized model keeps running 41 times until the riveting parameter is out of range. The parameterized modeling method is a useful method for a simulation study. The study will pave the way for further investigations.

scholarly journals Do Prospective Teachers Care About PISA Studies? A Pilot Study among University Students in Germany

2017 ◽  
Vol 10 (5) ◽  
pp. 26 ◽  
Author(s):  
Olga Ioannidou ◽  
Despoina Georgiou ◽  
Andreas Obersteiner ◽  
Nilufer Deniz Bas ◽  
Christine Mieslinger
Keyword(s):  
Teacher Education ◽  
University Students ◽  
Prospective Teachers ◽  
International Student ◽  
Teacher Education Program ◽  
Student Assessment ◽  
International Student Assessment ◽  
The Impact ◽  
Very High ◽  
Expected Outcomes

The results of international comparison studies such as the Program for International Student Assessment (PISA) have initiated intense discussions about educational reforms in Germany. Although in-service and pre-service teachers are an essential part of such reforms, little is known about their attitudes towards PISA studies. The present study aims to fill this gap through the investigation of pre-service teachers’ awareness, interest, perception, and attitudes towards PISA. A questionnaire was used to survey a sample of 107 university students who were participating in a teacher education program. The results reveal that 100% of the participants are aware of PISA. Nearly 69% of the participants think that the impact of PISA is rather high or very high, while 41% of them believe that PISA results are reliable. Accordingly, half of the participants seem to be interested in PISA results for their country. The present study discusses these findings in the light of the expected outcomes as proposed in standards for teacher education.

scholarly journals Psychological and Behavioral Predictors of Vaccine Efficacy: Considerations for COVID-19

2021 ◽  
pp. 174569162198924
Author(s):  
Annelise A. Madison ◽  
M. Rosie Shrout ◽  
Megan E. Renna ◽  
Janice K. Kiecolt-Glaser
Keyword(s):  
Risk Factors ◽  
Side Effects ◽  
Vaccine Efficacy ◽  
Psychological Factors ◽  
Behavioral Interventions ◽  
Vulnerable Groups ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Vaccine Response ◽  
The Impact

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine candidates are being evaluated, with the goal of conferring immunity on the highest percentage of people who receive the vaccine as possible. It is noteworthy that vaccine efficacy depends not only on the vaccine but also on characteristics of the vaccinated. Over the past 30 years, a series of studies has documented the impact of psychological factors on the immune system’s vaccine response. Robust evidence has demonstrated that stress, depression, loneliness, and poor health behaviors can impair the immune system’s response to vaccines, and this effect may be greatest in vulnerable groups such as the elderly. Psychological factors are also implicated in the prevalence and severity of vaccine-related side effects. These findings have generalized across many vaccine types and therefore may be relevant to the SARS-CoV-2 vaccine. In this review, we discuss these psychological and behavioral risk factors for poor vaccine responses, their relevance to the COVID-19 pandemic, as well as targeted psychological and behavioral interventions to boost vaccine efficacy and reduce side effects. Recent data suggest these psychological and behavioral risk factors are highly prevalent during the COVID-19 pandemic, but intervention research suggests that psychological and behavioral interventions can increase vaccine efficacy.

Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility

2021 ◽  
Vol 106 (1) ◽  
pp. 881-912
Author(s):  
Jingbo Sun ◽  
Shengwu Qin ◽  
Shuangshuang Qiao ◽  
Yang Chen ◽  
Gang Su ◽  
...  
Keyword(s):  
Debris Flow ◽  
Physical Model ◽  
Statistical Methods ◽  
Regional Scale ◽  
The Impact ◽  
Debris Flow Susceptibility

Statistical methods for spatial cluster detection in childhood cancer incidence: A simulation study

2021 ◽  
Vol 70 ◽  
pp. 101873
Author(s):  
Michael M. Schündeln ◽  
Toni Lange ◽  
Maximilian Knoll ◽  
Claudia Spix ◽  
Hermann Brenner ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Statistical Methods ◽  
Cancer Incidence ◽  
Simulation Study ◽  
Spatial Cluster ◽  
Cluster Detection

scholarly journals The impact of excluding or including Death Certificate Initiated (DCI) cases on estimated cancer survival: A simulation study

2021 ◽  
Vol 71 ◽  
pp. 101881
Author(s):  
Therese M.-L. Andersson ◽  
Tor Åge Myklebust ◽  
Mark J. Rutherford ◽  
Bjørn Møller ◽  
Isabelle Soerjomataram ◽  
...  
Keyword(s):  
Simulation Study ◽  
Cancer Survival ◽  
Death Certificate ◽  
The Impact

scholarly journals Using multiple imputation to classify potential outcomes subgroups

2021 ◽  
pp. 096228022110028
Author(s):  
Yun Li ◽  
Irina Bondarenko ◽  
Michael R Elliott ◽  
Timothy P Hofer ◽  
Jeremy MG Taylor
Keyword(s):  
Multiple Imputation ◽  
Statistical Methods ◽  
The United States ◽  
Treatment Selection ◽  
Potential Outcomes ◽  
Model Framework ◽  
Medical Tests ◽  
Gene Assay ◽  
The Impact ◽  
Test Result

With medical tests becoming increasingly available, concerns about over-testing, over-treatment and health care cost dramatically increase. Hence, it is important to understand the influence of testing on treatment selection in general practice. Most statistical methods focus on average effects of testing on treatment decisions. However, this may be ill-advised, particularly for patient subgroups that tend not to benefit from such tests. Furthermore, missing data are common, representing large and often unaddressed threats to the validity of most statistical methods. Finally, it is often desirable to conduct analyses that can be interpreted causally. Using the Rubin Causal Model framework, we propose to classify patients into four potential outcomes subgroups, defined by whether or not a patient’s treatment selection is changed by the test result and by the direction of how the test result changes treatment selection. This subgroup classification naturally captures the differential influence of medical testing on treatment selections for different patients, which can suggest targets to improve the utilization of medical tests. We can then examine patient characteristics associated with patient potential outcomes subgroup memberships. We used multiple imputation methods to simultaneously impute the missing potential outcomes as well as regular missing values. This approach can also provide estimates of many traditional causal quantities of interest. We find that explicitly incorporating causal inference assumptions into the multiple imputation process can improve the precision for some causal estimates of interest. We also find that bias can occur when the potential outcomes conditional independence assumption is violated; sensitivity analyses are proposed to assess the impact of this violation. We applied the proposed methods to examine the influence of 21-gene assay, the most commonly used genomic test in the United States, on chemotherapy selection among breast cancer patients.

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