FINDOUT: Using Either SPSS Commands or Graphical User Interface to Identify Influential Cases in Structural Equation Modeling in AMOS

The results in a structural equation modeling (SEM) analysis can be influenced by just a few observations, called influential cases. Tools have been developed for users of R to identify them. However, similar tools are not available for AMOS, which is also a popular SEM software package. We introduce the FINDOUT toolset, a group of SPSS extension commands, and an AMOS plugin, to identify influential cases and examine how these cases influence the results. The SPSS commands can be used either as syntax commands or as custom dialogs from pull-down menus, and the AMOS plugin can be run from AMOS pull-down menu. We believe these tools can help researchers to examine the robustness of their findings to influential cases.

Testing weak nulls in matched observational studies

We develop sensitivity analyses for the sample average treatment effect in matched observational studies while allowing unit-level treatment effects to vary. The methods may be applied to studies using any optimal without-replacement matching algorithm. In contrast to randomized experiments and to paired observational studies, we show for general matched designs that over a large class of test statistics, any procedure bounding the worst-case expectation while allowing for arbitrary effect heterogeneity must be unnecessarily conservative if treatment effects are actually constant across individuals. We present a sensitivity analysis which bounds the worst-case expectation while allowing for effect heterogeneity, and illustrate why it is generally conservative if effects are constant. An alternative procedure is presented that is asymptotically sharp if treatment effects are constant, and that is valid for testing the sample average effect under additional restrictions which may be deemed benign by practitioners. Simulations demonstrate that this alternative procedure results in a valid sensitivity analysis for the weak null hypothesis under a host of reasonable data-generating processes. The procedures allow practitioners to assess robustness of estimated sample average treatment effects to hidden bias while allowing for effect heterogeneity in matched observational studies.

Adjusting for publication bias in meta-analysis via inverse probability weighting using clinical trial registries

Publication bias is a major concern in conducting systematic reviews and meta-analyses. Various sensitivity analysis or bias-correction methods have been developed based on selection models, and they have some advantages over the widely used trim-and-fill bias-correction method. However, likelihood methods based on selection models may have difficulty in obtaining precise estimates and reasonable confidence intervals, or require a rather complicated sensitivity analysis process. Herein, we develop a simple publication bias adjustment method by utilizing the information on conducted but still unpublished trials from clinical trial registries. We introduce an estimating equation for parameter estimation in the selection function by regarding the publication bias issue as a missing data problem under the missing not at random assumption. With the estimated selection function, we introduce the inverse probability weighting (IPW) method to estimate the overall mean across studies. Furthermore, the IPW versions of heterogeneity measures such as the between-study variance and the I2 measure are proposed. We propose methods to construct confidence intervals based on asymptotic normal approximation as well as on parametric bootstrap. Through numerical experiments, we observed that the estimators successfully eliminated bias, and the confidence intervals had empirical coverage probabilities close to the nominal level. On the other hand, the confidence interval based on asymptotic normal approximation is much wider in some scenarios than the bootstrap confidence interval. Therefore, the latter is recommended for practical use.

Sensitivity Analyses for Means or Proportions with Missing Outcome Data

We describe an approach to sensitivity analysis introduced by Robins et al (1999), for the setting where the outcome is missing for some observations. This flexible approach focuses on the relationship between the outcomes and missingness, where data can be missing completely at random, missing at random given observed data, or missing not at random. We provide examples from HIV that include the sensitivity of the estimation of a mean and proportion under different missingness mechanisms. The approach illustrated provides a method for examining how the results of epidemiologic studies might shift as a function of bias due to missing data.

Sensitivity Analysis in Economic Evaluations of Immuno-Oncology Drugs: A Systematic Literature Review

OBJECTIVES

This study aimed to review, assess, and report the characteristics and strategies of sensitivity analyses (SAs) that were performed in the context of published economic evaluations of immuno-oncology drugs.

METHODS

The systematic literature search was conducted in Scopus and MEDLINE for articles published from 2005 to 2021. Study selection, based on a predefined set of criteria, was performed by 2 reviewers independently. We included economic evaluations of Food and Drug Administration-approved immuno-oncology drugs that were published in English and assessed the accompanying SAs on a set of items, including the range justification of the baseline parameters within the deterministic SA, the provisions for the correlation/overlay between parameters, and the justification of the chosen parameter distribution for the probabilistic SA, among others.

RESULTS

A total of 98 of 295 publications met the inclusion criteria. A total of 90 studies included a one-way and probabilistic SA and 16 of 98 studies had one-way and scenario analysis, alone or together with probabilistic analysis. Most studies provide explicit references as to the choice of parameters and values; nevertheless, there is a lack of a reference of correlation/overlay between parameters in most of the evaluations. In 26 of 98 studies, the most influential parameter for the incremental cost-effectiveness ratio was the under-evaluation drug cost.

CONCLUSIONS

Most of included articles contained an SA that was implemented according to commonly accepted published guidance. The under-evaluation drug cost, the estimates of progression-free survival, the hazard ratio for overall survival, and the time horizon of the analysis seem to play an important part in the robustness of the outcomes.

Uncertainty-aware automated assessment of the arm impedance with upper-limb exoskeletons

Providing high degree of personalization to a specific need of each patient is invaluable to improve the utility of robot-driven neurorehabilitation. For the desired customization of treatment strategies, precise and reliable estimation of the patient's state becomes important, as it can be used to continuously monitor the patient during training and to document the rehabilitation progress. Wearable robotics have emerged as a valuable tool for this quantitative assessment as the actuation and sensing are performed on the joint level. However, upper-limb exoskeletons introduce various sources of uncertainty, which primarily result from the complex interaction dynamics at the physical interface between the patient and the robotic device. These sources of uncertainty must be considered to ensure the correctness of estimation results when performing the clinical assessment of the patient state. In this work, we analyze these sources of uncertainty and quantify their influence on the estimation of the human arm impedance. We argue that this mitigates the risk of relying on overconfident estimates and promotes more precise computational approaches in robot-based neurorehabilitation.

Sensitivity analysis of point neuron model simulations implemented on neuromorphic hardware

With the ongoing growth in the field of neuro-inspired computing, newly arriving computational architectures demand extensive validation and testing against existing benchmarks to establish their competence and value. In our work, we break down the validation step into two parts-(1) establishing a methodological and numerical groundwork to establish a comparison between neuromorphic and conventional platforms and, (2) performing a sensitivity analysis on the obtained model regime to assess its robustness. We study the neuronal dynamics based on the Leaky Integrate and Fire (LIF) model, which is built upon data from the mouse visual cortex spanning a set of anatomical and physiological constraints. Intel Corp.'s first neuromorphic chip "Loihi" serves as our neuromorphic platform and results on it are validated against the classical simulations. After setting up a model that allows a seamless mapping between the Loihi and the classical simulations, we find that Loihi replicates classical simulations very efficiently with high precision. This model is then subjected to the second phase of validation, through sensitivity analysis, by assessing the impact on the cost function as values of the significant model parameters are varied. The work is done in two steps-(1) assessing the impact while changing one parameter at a time, (2) assessing the impact while changing two parameters at a time. We observe that the model is quite robust for majority of the parameters with slight change in the cost function. We also identify a subset of the model parameters changes which make the model more sensitive and thus, need to be defined more precisely.

Sensitivity Analysis of Factors Influencing Blast-like Loading on Reinforced Concrete Slabs Based on Grey Correlation Degree

Blast simulators are capable of applying blast-like loading to components in a safe and controlled laboratory environment, overcoming the inherent shortcomings of blast testing in terms of data acquisition, test cycle time, and cost. In this paper, reasonable assumptions and refinements are made to the components and shape of the impact module, a key component of the blast simulator, to achieve diversity in simulated blast loading. By designing four rubber shapes, the importance of ellipsoid rubber as an elastic cushion for simulating blast loading was determined. In order to assess the effectiveness of this optimization, numerical calculations based on a calibrated finite element model were performed around four factors: flat rubber thickness, ellipsoid rubber thickness, impact velocity, and impact modulus mass. Additionally, a grey correlation sensitivity analysis was carried out to evaluate the effect of these factors on the impact loading on the reinforced concrete (RC) slab. The results indicate that peak pressure and impulse had opposite sensitivities to velocity and mass. Changes in ellipsoid rubber thickness had a more positive effect on the impact loading than flat rubber thickness. An in-depth study of the role of these influencing factors is important for the design and improvement of impact modules.

Stormwater Infrastructure Resilience Assessment against Seismic Hazard Using Bayesian Belief Network

Resilient stormwater infrastructure is one of the fundamental components of resilient and sustainable cities. For this, the resilience assessment of stormwater infrastructure against earthquake hazards is crucial for municipal authorities. The objective of this study is to develop a resilience assessment framework for stormwater pipe infrastructure against seismic hazards. A Bayesian belief network (BBN)-based stormwater infrastructure resilience model is constructed based on the published literature and expert knowledge. The developed framework is implemented in the city of Regina, Canada, to assess the city's stormwater pipe infrastructure resilience. The outcome of the model indicates that proposed BBN-based stormwater infrastructure resilience model can effectively quantify uncertainties and handle the nonlinear relationships between several reliability and recovery factors. The model is also capable of identifying the most sensitive and vulnerable stormwater pipes within the network.

Quantitative analysis of the optogenetic excitability of CA1 neurons

Introduction

Optogenetics has emerged as a promising technique for modulating neuronal activity and holds potential for the treatment of neurological disorders such as temporal lobe epilepsy (TLE). However, clinical translation still faces many challenges. This in-silico study aims to enhance the understanding of optogenetic excitability in CA1 cells and to identify strategies for improving stimulation protocols.

Methods

Employing state-of-the-art computational models coupled with Monte Carlo simulated light propagation, the optogenetic excitability of four CA1 cells, two pyramidal and two interneurons, expressing ChR2(H134R) is investigated.

Results and discussion

The results demonstrate that confining the opsin to specific neuronal membrane compartments significantly improves excitability. An improvement is also achieved by focusing the light beam on the most excitable cell region. Moreover, the perpendicular orientation of the optical fiber relative to the somato-dendritic axis yields superior results. Inter-cell variability is observed, highlighting the importance of considering neuron degeneracy when designing optogenetic tools. Opsin confinement to the basal dendrites of the pyramidal cells renders the neuron the most excitable. A global sensitivity analysis identified opsin location and expression level as having the greatest impact on simulation outcomes. The error reduction of simulation outcome due to coupling of neuron modeling with light propagation is shown. The results promote spatial confinement and increased opsin expression levels as important improvement strategies. On the other hand, uncertainties in these parameters limit precise determination of the irradiance thresholds. This study provides valuable insights on optogenetic excitability of CA1 cells useful for the development of improved optogenetic stimulation protocols for, for instance, TLE treatment.

Quantifying Parameter Interdependence in Stochastic Discrete Models of Biochemical Systems

Stochastic modeling of biochemical processes at the cellular level has been the subject of intense research in recent years. The Chemical Master Equation is a broadly utilized stochastic discrete model of such processes. Numerous important biochemical systems consist of many species subject to many reactions. As a result, their mathematical models depend on many parameters. In applications, some of the model parameters may be unknown, so their values need to be estimated from the experimental data. However, the problem of parameter value inference can be quite challenging, especially in the stochastic setting. To estimate accurately the values of a subset of parameters, the system should be sensitive with respect to variations in each of these parameters and they should not be correlated. In this paper, we propose a technique for detecting collinearity among models' parameters and we apply this method for selecting subsets of parameters that can be estimated from the available data. The analysis relies on finite-difference sensitivity estimations and the singular value decomposition of the sensitivity matrix. We illustrated the advantages of the proposed method by successfully testing it on several models of biochemical systems of practical interest.

Assumptions and analysis planning in studies with missing data in multiple variables: moving beyond the MCAR/MAR/MNAR classification

Researchers faced with incomplete data are encouraged to consider whether their data are 'missing completely at random' (MCAR), 'missing at random' (MAR) or 'missing not at random' (MNAR) when planning their analysis. However, there are two major problems with this classification as originally defined by Rubin in the 1970s. First, when there are missing data in multiple variables, the plausibility of the MAR assumption is difficult to assess using substantive knowledge and is more stringent than is generally appreciated. Second, although MCAR and MAR are sufficient conditions for consistent estimation with specific methods, they are not necessary conditions and therefore this categorization does not directly determine the best approach for handling the missing data in an analysis. How best to handle missing data depends on the assumed causal relationships between variables and their missingness, and what these relationships imply in terms of the 'recoverability' of the target estimand (the population parameter that encodes the answer to the underlying research question). Recoverability is defined as whether the estimand can be consistently estimated from the patterns and associations in the observed data without needing to invoke external information on the extent to which the distribution of missing values might differ from that of observed values. In this manuscript we outline an approach for deciding which method to use to handle multivariable missing data in an analysis, using directed acyclic graphs to depict missingness assumptions and determining the implications in terms of recoverability of the target estimand.

Thoracoabdominal asynchrony in a virtual preterm infant: computational modeling and analysis

Thoracoabdominal asynchrony (TAA), the asynchronous volume changes between the rib cage and abdomen during breathing, is associated with respiratory distress, progressive lung volume loss, and chronic lung disease in the newborn infant. Preterm infants are prone to TAA risk factors such as weak intercostal muscles, surfactant deficiency, and a flaccid chest wall. The causes of TAA in this fragile population are not fully understood and, to date, the assessment of TAA has not included a mechanistic modeling framework to explore the role these risk factors play in breathing dynamics and how TAA can be resolved. We present a dynamic compartmental model of pulmonary mechanics that simulates TAA in the preterm infant under various adverse clinical conditions, including high chest wall compliance, applied inspiratory resistive loads, bronchopulmonary dysplasia, anesthesia-induced intercostal muscle deactivation, weakened costal diaphragm, impaired lung compliance, and upper airway obstruction. Sensitivity analyses performed to screen and rank model parameter influence on model TAA and respiratory volume outputs show that risk factors are additive so that maximal TAA occurs in a virtual preterm infant with multiple adverse conditions, and addressing risk factors individually causes incremental changes in TAA. An abruptly obstructed upper airway caused immediate nearly paradoxical breathing and tidal volume reduction despite greater effort. In most simulations, increased TAA occurred together with decreased tidal volume. Simulated indices of TAA are consistent with published experimental studies and clinically observed pathophysiology, motivating further investigation into the use of computational modeling for assessing and managing TAA.NEW & NOTEWORTHY A novel model of thoracoabdominal asynchrony incorporates literature-derived mechanics and simulates the impact of risk factors on a virtual preterm infant. Sensitivity analyses were performed to determine the influence of model parameters on TAA and respiratory volume. Predicted phase angles are consistent with prior experimental and clinical results, and influential parameters are associated with clinical scenarios that significantly alter phase angle, motivating further investigation into the use of computational modeling for assessing and managing thoracoabdominal asynchrony.

Small-scale medical oxygen production unit using PSA technology: modeling and sensitivity analysis

This study presents a solid approach for small-scale medical oxygen production unit using pressure swing adsorption (PSA) technology. The objective of this research is to develop a mathematical model and conduct a sensitivity analysis to optimise the design and operating parameters of the PSA system. Based on the simulation results, an optimal set of operational parameter values has been obtained for the PSA beds. The result shows that the binary system produced oxygen with a purity of 94%, at the adsorption pressure 1 bar and temperature of 308K. The findings demonstrate the effectiveness of the proposed small-scale PSA system for medical oxygen production, highlighting the impact of key parameters and emphasising the need for careful optimisation. The findings serve as a guide for the design and operation of small-scale PSA systems, enabling healthcare facilities to produce their own medical oxygen, thereby improving accessibility and addressing critical shortages during emergencies. Future research may explore the integration of large scale PSA units in hospitals in Morocco.

Association of uncontrolled blood pressure in apparent treatment-resistant hypertension with increased risk of major adverse cardiovascular events plus

Patients with apparent treatment-resistant hypertension (aTRH) are at increased risk of end-organ damage and cardiovascular events. Little is known about the effects of blood pressure (BP) control in this population. Using a national claims database integrated with electronic medical records, the authors evaluated the relationships between uncontrolled BP (UBP; ≥130/80 mmHg) or controlled BP (CBP; <130/80 mmHg) and risk of major adverse cardiovascular events plus (MACE+; stroke, myocardial infarction, heart failure requiring hospitalization) and end-stage renal disease (ESRD) in adult patients with aTRH (taking ≥3 antihypertensive medication classes concurrently within 30 days between January 1, 2015 and June 30, 2021). MACE+ components were also evaluated separately. Multivariable regression models were used to adjust for baseline differences in demographic and clinical characteristics, and sensitivity analyses using CBP <140/90 mmHg were conducted. Patients with UBP (n = 22 333) were younger and had fewer comorbidities at baseline than those with CBP (n = 11 427). In the primary analysis, which adjusted for these baseline differences, UBP versus CBP patients were at an 8% increased risk of MACE+ (driven by a 31% increased risk of stroke) and a 53% increased risk of ESRD after 2.7 years of follow-up. Greater MACE+ (22%) and ESRD (98%) risk increases with UBP versus CBP were seen in the sensitivity analysis. These real-world data showed an association between suboptimal BP control in patients with aTRH and higher incidence of MACE+ and ESRD linked with UBP despite the use of multidrug regimens. Thus, there remains a need for improved aTRH management.

Are single global warming potential impact assessments adequate for carbon footprints of agri-food systems?

The vast majority of agri-food climate-based sustainability analyses use global warming potential (GWP100) as an impact assessment, usually in isolation; however, in recent years, discussions have criticised the 'across-the-board' application of GWP100 in Life Cycle Assessments (LCAs), particularly of food systems which generate large amounts of methane (CH4) and considered whether reporting additional and/or alternative metrics may be more applicable to certain circumstances or research questions (e.g. Global Temperature Change Potential (GTP)). This paper reports a largescale sensitivity analysis using a pasture-based beef production system (a high producer of CH4 emissions) as an exemplar to compare various climatatic impact assessments: CO2-equivalents using GWP100 and GTP100, and 'CO2-warming-equivalents' using 'GWP Star', or GWP*. The inventory for this system was compiled using data from the UK Research and Innovation National Capability, the North Wyke Farm Platform, in Devon, SW England. LCAs can have an important bearing on: (i) policymakers' decisions; (ii) farmer management decisions; (iii) consumers' purchasing habits; and (iv) wider perceptions of whether certain activities can be considered 'sustainable' or not; it is, therefore, the responsibility of LCA practitioners and scientists to ensure that subjective decisions are tested as robustly as possible through appropriate sensitivity and uncertainty analyses. We demonstrate herein that the choice of climate impact assessment has dramatic effects on interpretation, with GWP100 and GTP100 producing substantially different results due to their different treatments of CH4 in the context of carbon dioxide (CO2) equivalents. Given its dynamic nature and previously proven strong correspondence with climate models, out of the three assessments covered, GWP* provides the most complete coverage of the temporal evolution of temperature change for different greenhouse gas emissions. We extend previous discussions on the limitations of static emission metrics and encourage LCA practitioners to consider due care and attention where additional information or dynamic approaches may prove superior, scientifically speaking, particularly in cases of decision support.

Association between the triglyceride to high-density lipoprotein cholesterol ratio and the risk of gestational diabetes mellitus: a second analysis based on data from a prospective cohort study

Background

Although there is strong evidence linking triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio to insulin resistance and diabetes mellitus, its clinical importance in pregnant women has not been well determined. This study sought to determine the connection between the TG/HDL-C ratio in the first trimester and the eventual onset of gestational diabetes mellitus (GDM).

Methods

We performed a secondary analysis of open-access data from a prospective cohort study. This present study included 590 singleton pregnant women at 10-14 weeks who visited the outpatient clinics for prenatal checks and were recorded at Incheon Seoul Women's Hospital and Seoul Metropolitan Government Seoul National University Boramae Medical Center in Korea. A binary logistic regression model, a series of sensitivity analyses, and subgroup analysis were used to examine the relationship between TG/HDL-C ratio and incident GDM. A receiver operating characteristic (ROC) analysis was also conducted to assess the ability of the TG/HDL-C ratio to predict GDM.

Results

The mean age of the included individuals was 32.06 ± 3.80 years old. The mean TG/HDL-C ratio was 1.96 ± 1.09. The incidence rate of GDM was 6.27%. After adjustment for potentially confounding variables, TG/HDL-C ratio was positively associated with incident GDM (OR=1.77, 95%CI: 1.32-2.38, P=0.0001). Sensitivity analyses and subgroup analysis demonstrated the validity of the relationship between the TG/HDL-C ratio and GDM. The TG/HDL-C ratio was a good predictor of GDM, with an area under the ROC curve of 0.7863 (95% CI: 0.7090-0.8637). The optimal TG/HDL-C ratio cut-off value for detecting GDM was 2.2684, with a sensitivity of 72.97% and specificity of 75.05%.

Conclusion

Our results demonstrate that the elevated TG/HDL-C ratio is related to incident GDM. The TG/HDL-C ratio at 10-14 weeks could help identify pregnant women at risk for GDM and may make it possible for them to receive early and effective treatment to improve their prognosis.

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.

[Characteristics of VOCs and Assessment of Emission Reduction Effect During the Epidemic Lockdown Period in Shenzhen Urban Area]

To prevent disease spreading during the COVID-19 epidemic, Shenzhen adopted lockdown measures in March of 2022. This provided an opportunity to study the response of changes in anthropogenic volatile organic compounds (AVOCs) in Shenzhen to emission reduction and to evaluate the effectiveness of current emission reduction measures. This study analyzed the variety of AVOCs before, during, and after the epidemic lockdown based on the online observation data of pollutants at Lianhua Station in Shenzhen from March 7, 2022 to March 27, 2022. Additionally, the sensitivity of ozone formation and the assessment of the reduction effect of precursors was conducted by an observation based model(OBM). The results showed that:affected by regional influences and the interference of meteorological conditions, the average value of AVOCs in Shenzhen urban areas did not drop significantly during the lockdown period compared to that before the lockdown. However, the peak of AVOCs at the morning peak time under the influence of "sea and land wind" during the epidemic lockdown period dropped by 46% on average compared with that during the non-lockdown period, and the aromatic hydrocarbon component dropped the most by 59%. Additionally, under the influence of continuous easterly wind, or during the accumulation and increase of AVOCs affected by regional transmission, aromatic components also decreased by an average of 25% and 21%, respectively. During the lockdown period of the epidemic in Shenzhen, the O₃ formation in urban areas was still AVOCs-limited. Increasing the emission reduction ratio of AVOCs was the most effective measure to reduce O₃ in the short term. In order to ensure the effectiveness of emission reduction, it was recommended that the coordinated emission reduction ratio of AVOCs and NO_x should be greater than 1:2. It was only possible to enter the downward channel of O₃ if the deep emission reduction was more than 60%. This study revealed that the emission reduction of AVOCs during the morning traffic peak during the epidemic lockdown period was conducive to inhibiting the formation of O₃, whereas the control of NO_x would promote it. Strengthening the control of local aromatic hydrocarbon components during the regional impact process could also significantly reduce O₃ production. At this stage, Shenzhen should strengthen the management and control of industrial solvents, especially to reduce the aromatic hydrocarbon components in the solvent source that have a greater impact on the generation of O₃. Further, Shenzhen should continue to promote the reform of the energy structure of motor vehicles to reduce the emission of VOCs in fuel combustion.

COVID-19 transmission dynamics and the impact of vaccination: modelling, analysis and simulations

Despite the lifting of COVID-19 restrictions, the COVID-19 pandemic and its effects remain a global challenge including the sub-Saharan Africa (SSA) region. Knowledge of the COVID-19 dynamics and its potential trends amidst variations in COVID-19 vaccine coverage is therefore crucial for policy makers in the SSA region where vaccine uptake is generally lower than in high-income countries. Using a compartmental epidemiological model, this study aims to forecast the potential COVID-19 trends and determine how long a wave could be, taking into consideration the current vaccination rates. The model is calibrated using South African reported data for the first four waves of COVID-19, and the data for the fifth wave are used to test the validity of the model forecast. The model is qualitatively analysed by determining equilibria and their stability, calculating the basic reproduction number R0 and investigating the local and global sensitivity analysis with respect to R0. The impact of vaccination and control interventions are investigated via a series of numerical simulations. Based on the fitted data and simulations, we observed that massive vaccination would only be beneficial (deaths averting) if a highly effective vaccine is used, particularly in combination with non-pharmaceutical interventions. Furthermore, our forecasts demonstrate that increased vaccination coverage in SSA increases population immunity leading to low daily infection numbers in potential future waves. Our findings could be helpful in guiding policy makers and governments in designing vaccination strategies and the implementation of other COVID-19 mitigation strategies.

A multiple imputation-based sensitivity analysis approach for regression analysis with a missing not at random covariate

Missing covariate problems are common in biomedical and electrical medical record data studies while evaluating the relationship between a biomarker and certain clinical outcome, when biomarker data are not collected for all subjects. However, missingness mechanism is unverifiable based on observed data. If there is a suspicion of missing not at random (MNAR), researchers often perform sensitivity analysis to evaluate the impact of various missingness mechanisms. Under the selection modeling framework, we propose a sensitivity analysis approach with a standardized sensitivity parameter using a nonparametric multiple imputation strategy. The proposed approach requires fitting two working models to derive two predictive scores: one for predicting missing covariate values and the other for predicting missingness probabilities. For each missing covariate observation, the two predictive scores along with the pre-specified sensitivity parameter are used to define an imputing set. The proposed approach is expected to be robust against mis-specifications of the selection model and the sensitivity parameter since the selection model and the sensitivity parameter are not directly used to impute missing covariate values. A simulation study is conducted to study the performance of the proposed approach when MNAR is induced by Heckman's selection model. Simulation results show the proposed approach can produce plausible regression coefficient estimates. The proposed sensitivity analysis approach is also applied to evaluate the impact of MNAR on the relationship between post-operative outcomes and incomplete pre-operative Hemoglobin A1c level for patients who underwent carotid intervetion for advanced atherosclerotic disease.

A Combined Effect of Polygenic Scores and Environmental Factors on Individual Differences in Depression Level

The risk of depression could be evaluated through its multifactorial nature using the polygenic score (PGS) approach. Assuming a "clinical continuum" hypothesis of mental diseases, a preliminary assessment of individuals with elevated risk for developing depression in a non-clinical group is of high relevance. In turn, epidemiological studies suggest including social/lifestyle factors together with PGS to address the "missing heritability" problem. We designed regression models, which included PGS using 27 SNPs and social/lifestyle factors to explain individual differences in depression levels in high-education students from the Volga-Ural region (VUR) of Eurasia. Since issues related to population stratification in PGS scores may lead to imprecise variant effect estimates, we aimed to examine a sensitivity of PGS calculated on summary statistics of depression and neuroticism GWAS from Western Europeans to assess individual proneness to depression levels in the examined sample of Eastern Europeans. A depression score was assessed using the revised version of the Beck Depression Inventory (BDI) in 1065 young adults (age 18-25 years, 79% women, Eastern European ancestry). The models based on weighted PGS demonstrated higher sensitivity to evaluate depression level in the full dataset, explaining up to 2.4% of the variance (p = 3.42 × 10^-7); the addition of social parameters enhanced the strength of the model (adjusted r² = 15%, p < 2.2 × 10^-16). A higher effect was observed in models based on weighted PGS in the women group, explaining up to 3.9% (p = 6.03 × 10^-9) of variance in depression level assuming a combined SNPs effect and 17% (p < 2.2 × 10^-16)-with the addition of social factors in the model. We failed to estimate BDI-measured depression based on summary statistics from Western Europeans GWAS of clinical depression. Although regression models based on PGS from neuroticism (depression-related trait) GWAS in Europeans were associated with a depression level in our sample (adjusted r² = 0.43%, p = 0.019-for unweighted model), the effect was mainly attributed to the inclusion of social/lifestyle factors as predictors in these models (adjusted r² = 15%, p < 2.2 × 10^-16-for unweighted model). In conclusion, constructed PGS models contribute to a proportion of interindividual variability in BDI-measured depression in high-education students, especially women, from the VUR of Eurasia. External factors, including the specificity of rearing in childhood, used as predictors, improve the predictive ability of these models. Implementation of ethnicity-specific effect estimates in such modeling is important for individual risk assessment.

Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study

BACKGROUND

During the COVID-19 pandemic, local health authorities were responsible for managing and reporting current cases in Germany. Since March 2020, employees had to contain the spread of COVID-19 by monitoring and contacting infected persons as well as tracing their contacts. In the EsteR project, we implemented existing and newly developed statistical models as decision support tools to assist in the work of the local health authorities.

OBJECTIVE

The main goal of this study was to validate the EsteR toolkit in two complementary ways: first, investigating the stability of the answers provided by our statistical tools regarding model parameters in the back end and, second, evaluating the usability and applicability of our web application in the front end by test users.

METHODS

For model stability assessment, a sensitivity analysis was carried out for all 5 developed statistical models. The default parameters of our models as well as the test ranges of the model parameters were based on a previous literature review on COVID-19 properties. The obtained answers resulting from different parameters were compared using dissimilarity metrics and visualized using contour plots. In addition, the parameter ranges of general model stability were identified. For the usability evaluation of the web application, cognitive walk-throughs and focus group interviews were conducted with 6 containment scouts located at 2 different local health authorities. They were first asked to complete small tasks with the tools and then express their general impressions of the web application.

RESULTS

The simulation results showed that some statistical models were more sensitive to changes in their parameters than others. For each of the single-person use cases, we determined an area where the respective model could be rated as stable. In contrast, the results of the group use cases highly depended on the user inputs, and thus, no area of parameters with general model stability could be identified. We have also provided a detailed simulation report of the sensitivity analysis. In the user evaluation, the cognitive walk-throughs and focus group interviews revealed that the user interface needed to be simplified and more information was necessary as guidance. In general, the testers rated the web application as helpful, especially for new employees.

CONCLUSIONS

This evaluation study allowed us to refine the EsteR toolkit. Using the sensitivity analysis, we identified suitable model parameters and analyzed how stable the statistical models were in terms of changes in their parameters. Furthermore, the front end of the web application was improved with the results of the conducted cognitive walk-throughs and focus group interviews regarding its user-friendliness.

Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics

The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs.

Multivariate Assessment and Risk Ranking of Pesticide Residues in Citrus Fruits

Pesticides are extensively used in the cultivation and postharvest protection of citrus fruits, therefore continuous monitoring and health risk assessments of their residues are required. This study aimed to investigate the occurrence of pesticide residues on citrus fruits and to evaluate the acute and chronic risk for adults and children. The risk ranking of twenty-three detected pesticides was carried out according to a matrix ranking scheme. Multiple residues were detected in 83% of 76 analyzed samples. In addition, 28% contained pesticides at or above maximum residue levels (MRLs). The most frequently detected pesticides were imazalil, azoxystrobin, and dimethomorph. According to the risk ranking method, imazalil was classified in the high-risk group, followed by prochloraz, chlorpyrifos, azinphos-methyl, tebufenpyrad, and fenpiroximate, which were considered to pose a medium risk. The majority of detected pesticides (74%) posed a low risk. The health risk assessment indicated that imazalil and thiabendazole contribute to acute (HQa) and chronic (HQc) dietary risk, respectively. The HQc was negligible for the general population, while the HQa of imazalil and thiabendazole exceeded the acceptable level in the worst-case scenario. Cumulative chronic/acute risk (HIc/HIa) assessment showed that chronic risk was acceptable in all samples for children and adults, while the acute risk was unacceptable in 5.3% of citrus fruits for adults and 26% of citrus fruits for children. Sensitivity analyses indicated that the ingestion rate and individual body weight were the most influential risk factors.