Form Of Sensitivity Analysis Research Articles

A multi-criteria decision-making framework and analysis of vegetable oils to produce bio-based plastics

In the present paper, the suitability of the selected oilseeds, their corresponding vegetable oils, and few other raw materials to produce bio-based plastics was evaluated by constructing a novel criteria-based framework for Multi-Criteria Decision-Making (MCDM) analysis with a focus on the criteria of chemical functionality, sustainability, production quantity, cost, and market availability. Qualitative, semi-quantitative, and quantitative data was utilized as a base for the criteria, for which a 1–5–9 scaling technique was developed to convert the hybrid starting data into the quantitative form, when required. Additionally, two varying sets of starting data, four scenarios with differing weights of importance, as well as two different MCDM techniques, namely Technique for Order Preference and Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW), were used as a form of sensitivity analysis. The MCDM results were influenced by the dissimilar algorithm of TOPSIS and SAW techniques, resulting in different level of accuracy and a phenomenon of rank reversal, together with the developed MCDM framework in terms of the utilized data types, scaling technique, assumptions to treat data uncertainty as well as the selected criteria and scenarios. Regardless of the different starting data sets, scenarios, and MCDM techniques utilized for the MCDM analysis, tall, linseed, soybean, and palm oil were identified as the most suitable and palm kernel, coconut, and sunflower oil as the least suitable raw materials with their feature trade-offs to produce bio-based plastics. The MCDM results of the present paper can be treated as a guidepost targeted for diverse actors in the early stage of the bio-based plastics’ value chain with varying point-of-views. Further, in the future, the novel MCDM framework can be of relevant significance in analysing the features of various raw materials to produce bio-based plastics.

The impact of biparental hepatitis B virus infection on pregnancy outcomes in patients undergoing assisted reproductive technology treatment: a systematic review and meta-analysis.

To evaluate the association between biparental hepatitis B virus (HBV) infection (hepatitis B surface antigen [HBsAg] seropositivity) and pregnancy outcomes in patients undergoing assisted reproductive technology (ART) treatment, including clinical pregnancy rate (CPR) and live birth rate (LBR). We searched various literature databases from the earliest date available until May 20, 2021, including PubMed, Embase, Medline, Web of Science, Scopus, CNKI, and Wangfang. Four patterns of biparental HBV infection for pregnancy outcomes following ART were synthesized separately by using random-effects model. We also performed subgroup analyses (matched or unmatched) and two forms of sensitivity analysis. A total of 25 retrospective cohort studies, involving more than 19,269 couples, were included. Maternal and paternal HBV co-infection (crude relative risk [cRR]: 0.58, 95% confidence interval: 0.28-1.23), either maternal or paternal infection (cRR: 1.05, 0.88-1.24), maternal HBV infection alone (cRR: 0.96, 0.79-1.16), or paternal HBV infection alone (cRR: 1.02, 0.91-1.13) were not associated with significant reductions in CPR at per woman level. These patterns of infection were also not significantly associated with decreased LBR at per woman level. However, paternal HBV infection alone reduced CPR (adjusted odds ratio: 0.54, 0.33-0.86) and LBR (cRR: 0.52, 0.28-0.99) at per cycle level. Subgroup analyses and sensitivity analyses were mostly consistent with the primary results. Paternal HBV infection alone probably decrease CPR after ART at per cycle level. However, maternal HBV infection was not associated with reductions in CPR and LBR in women undergoing ART at per woman level.

Association between Klebsiella pneumoniae and ankylosing spondylitis: A systematic review and meta-analysis.

The aim of this study is to evaluate the association between Klebsiella pneumoniae infection and ankylosing spondylitis (AS). Five electronic databases, PubMed, Embase, Medline, Web of Science, and Scopes, were searched until September 29, 2021. Cohort and case-control studies that assessed the association between K. pneumoniae infection and AS were included. Pooled odds ratio (OR) was selected to show the effect size. Subgroup analysis (active or inactive AS) and 2 forms of sensitivity analysis were conducted. All statistical analyses were conducted by using STATA 12.0. There were 25 case-control studies finally included, including 8 studies concerning presence of K. pneumoniae in feces, and 17 studies concerning serum antibody (immunoglobulin [Ig]G, IgM, IgA) against K. pneumoniae. The results suggested that when compared with healthy people, presence of K. pneumoniae in feces was associated with AS (OR: 5.65; 95% CI: 1.68-19.00). Similarly, when compared with healthy people, higher positive rates of IgA (OR: 6.28; 95% CI: 3.32-11.91) and IgG (OR: 5.22; 95% CI: 1.36-19.99) were observed. Subgroup analyses suggested that association between K. pneumoniae and AS appears stronger in active AS. When compared with healthy people, a significantly higher positive rate of K. pneumoniae in feces, serum IgA and IgG were observed in patients with AS, suggesting that K. pneumoniae probably plays a crucial role in the occurrence of AS. The findings in this study need further prospective investigations for confirmation.

Integrating Alternative Social Value Judgments Into Cost-Effectiveness Analysis of Vaccines: An Application to Varicella-Zoster Virus Vaccination

ObjectivesCost-effectiveness analyses (CEA) are based on the value judgment that health outcomes (eg, quantified in quality-adjusted life-years; QALYs) are all equally valuable irrespective of their context. Whereas most published CEAs perform extensive sensitivity analysis on various parameters and assumptions, only rarely is the influence of the QALY-equivalence assumption on cost-effectiveness results investigated. We illustrate how the integration of alternative social value judgments in CEA can be a useful form of sensitivity analysis. MethodsBecause varicella-zoster virus (VZV) vaccination affects 2 distinct diseases (varicella zoster and herpes zoster) and likely redistributes infections across different age groups, the program has an important equity dimension. We used a cost-effectiveness model and disentangled the share of direct protection and herd immunity within the total projected QALYs resulting from a 50-year childhood VZV program in the UK. We use the UK population’s preferences for QALYs in the vaccine context to revalue QALYs accordingly. ResultsRevaluing different types of QALYs for different age groups in line with public preferences leads to a 98% change in the projected net impact of the program. The QALYs gained among children through direct varicella protection become more important, whereas the QALYs lost indirectly through zoster in adults diminish in value. Weighting of vaccine-related side effects made a large difference. ConclusionsOur study shows that a sensitivity analysis in which alternative social value judgments about the value of health outcomes are integrated into CEA of vaccines is relatively straightforward and provides important additional information for decision makers to interpret cost-effectiveness results.

A multi-criteria multi-commodity flow model for analysing transportation networks

This article proposes a novel multi-criteria multi-commodity network flow (MCMCNF) model to help transport planners and other analysts holistically assess different types of transportation systems (TS). This model provides a tool to autonomously analyse the effect of expansions, tolls, different levels of congestion and accidents leading to potential insights into a network's resilience and vulnerability, emissions distribution and risk. Unlike the mono criterion network flow models used for some time, we propose the application of multiple objectives. In this article we investigate the application of two objectives. The first maximises the flow of commodities and the second minimises travel related costs. The travel cost is modelled generically and may include the distance travelled, travel time and access charges. The considered cost function is non-linear, so different linearization strategies are suggested. These permit the model to be solved efficiently using Separable Programming techniques and the ɛ-constraint method (ECM). We have applied the proposed model to a variety of case studies and demonstrate how different forms of sensitivity analysis can be performed. The numerical investigations have highlighted the specific features of the Pareto frontiers and the resilience and flexibility of the networks considered.

Practical Experience of Sensitivity Analysis: Comparing Six Methods, on Three Hydrological Models, with Three Performance Criteria

Currently, practically no modeling study is expected to be carried out without some form of Sensitivity Analysis (SA). At the same time, there is a large number of various methods and it is not always easy for practitioners to choose one. The aim of this paper is to briefly review main classes of SA methods, and to present the results of the practical comparative analysis of applying them. Six different global SA methods: Sobol, eFAST (extended Fourier Amplitude Sensitivity Test), Morris, LH-OAT, RSA (Regionalized Sensitivity Analysis), and PAWN are tested on three conceptual rainfall-runoff models with varying complexity: (GR4J, Hymod, and HBV) applied to the case study of Bagmati basin (Nepal). The methods are compared with respect to effectiveness, efficiency, and convergence. A practical framework of selecting and using the SA methods is presented. The result shows that, first of all, all the six SA methods are effective. Morris and LH-OAT methods are the most efficient methods in computing SI and ranking. eFAST performs better than Sobol, and thus it can be seen as its viable alternative for Sobol. PAWN and RSA methods have issues of instability, which we think are due to the ways Cumulative Distribution Functions (CDFs) are built, and using Kolmogorov–Smirnov statistics to compute Sensitivity Indices. All the methods require sufficient number of runs to reach convergence. Difference in efficiency of different methods is an inevitable consequence of the differences in the underlying principles. For SA of hydrological models, it is recommended to apply the presented practical framework assuming the use of several methods, and to explicitly take into account the constraints of effectiveness, efficiency (including convergence), ease of use, and availability of software.

A Simple Adjustment for Bandwidth Snooping

Kernel-based estimators such as local polynomial estimators in regression discontinuity designs are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals (CIs) may not have correct coverage, even if the estimator is unbiased. This article proposes a simple adjustment that gives correct coverage in such situations: replace the normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the loss in coverage for conventional CIs. For a range of relevant cases, a conventional 95% CI has coverage between 70% and 90%, and our adjustment amounts to replacing the conventional critical value 1.96 with a number between 2.2 and 2.8. Our results also apply to other settings involving trimmed data, such as trimming to ensure overlap in treatment effect estimation. We illustrate our approach with three empirical applications.

A Simple Adjustment for Bandwidth Snooping

Kernel-based estimators are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is unbiased. This paper proposes a simple adjustment that gives correct coverage in such situations: replace the Normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the loss in coverage for conventional confidence intervals. For a range of relevant cases, a conventional 95% confidence interval has coverage between 70% and 90%, and our adjustment amounts to replacing the conventional critical value 1.96 with a number between 2.2 and 2.8. A Monte Carlo study confirms that our approach gives accurate coverage in finite samples. We illustrate our approach with two empirical applications.

A Simple Adjustment for Bandwidth Snooping

Kernel-based estimators are often evaluated at multiple bandwidths as a form of sensitivity analysis. However, if in the reported results, a researcher selects the bandwidth based on this analysis, the associated confidence intervals may not have correct coverage, even if the estimator is unbiased. This paper proposes a simple adjustment that gives correct coverage in such situations: replace the Normal quantile with a critical value that depends only on the kernel and ratio of the maximum and minimum bandwidths the researcher has entertained. We tabulate these critical values and quantify the loss in coverage for conventional confidence intervals. For a range of relevant cases, a conventional 95% confidence interval has coverage between 70% and 90%, and our adjustment amounts to replacing the conventional critical value 1.96 with a number between 2.2 and 2.8. A Monte Carlo study confirms that our approach gives accurate coverage in finite samples. We illustrate our approach with two empirical applications.

Using spatial gradient analysis to clarify species distributions with application to South African protea

Typical ecological gradient analyses consider variation in the response of plants along a gradient of covariate values, but generally constrain themselves to predetermined response curves and ignore spatial autocorrelation. In this paper, we develop a formal spatial gradient analysis. We adopt the mathematical definition of gradients as directional rates of change with regard to a spatial surface. We view both the response and the covariate as spatial surfaces over a region of interest with respective gradient behavior. The gradient analysis we propose enables local comparison of these gradients. At any spatial location, we compare the behavior of the response surface with the behavior of the covariate surface to provide a novel form of sensitivity analysis. More precisely, we first fit a joint hierarchical Bayesian spatial model for a response variable and an environmental covariate. Then, after model fitting, at a given location, for each variable, we can obtain the posterior distribution of the derivative in any direction. We use these distributions to compute spatial sensitivities and angular discrepancies enabling a more detailed picture of the spatial nature of the response–covariate relationship. This methodology is illustrated using species presence probability as a response to elevation for two species of South African protea. We also offer a comparison with sensitivity analysis using geographically weighted regression. We show that the spatial gradient analysis allows for more extensive inference and provides a much richer description of the spatially varying relationships.

Accurately decoding visual information from fMRI data obtained in a realistic virtual environment.

Three-dimensional interactive virtual environments (VEs) are a powerful tool for brain-imaging based cognitive neuroscience that are presently under-utilized. This paper presents machine-learning based methods for identifying brain states induced by realistic VEs with improved accuracy as well as the capability for mapping their spatial topography on the neocortex. VEs provide the ability to study the brain under conditions closer to the environment in which humans evolved, and thus to probe deeper into the complexities of human cognition. As a test case, we designed a stimulus to reflect a military combat situation in the Middle East, motivated by the potential of using real-time functional magnetic resonance imaging (fMRI) in the treatment of post-traumatic stress disorder. Each subject experienced moving through the virtual town where they encountered 1–6 animated combatants at different locations, while fMRI data was collected. To analyze the data from what is, compared to most studies, more complex and less controlled stimuli, we employed statistical machine learning in the form of Multi-Voxel Pattern Analysis (MVPA) with special attention given to artificial Neural Networks (NN). Extensions to NN that exploit the block structure of the stimulus were developed to improve the accuracy of the classification, achieving performances from 58 to 93% (chance was 16.7%) with six subjects. This demonstrates that MVPA can decode a complex cognitive state, viewing a number of characters, in a dynamic virtual environment. To better understand the source of this information in the brain, a novel form of sensitivity analysis was developed to use NN to quantify the degree to which each voxel contributed to classification. Compared with maps produced by general linear models and the searchlight approach, these sensitivity maps revealed a more diverse pattern of information relevant to the classification of cognitive state.

On Covariate Importance for Regression Models with Multivariate Response

We address the question of identifying the relative importance of covariates for model response, a form of sensitivity analysis. Relative importance is typically implemented as part of the model building procedure, e.g., forward variable selection or backward elimination. Here, we take a different perspective. We assume a model with multiple covariates and multivariate response has been selected and formulate criteria to assess covariate importance. Hence, with regard to covariates, our approach is joint, post model fitting, rather than conditional or sequential model creation. The noteworthy challenge we accommodate is the handling of multivariate response where individual regressions may give differing, perhaps conflicting, relative importances. In addition, we recognize that, according to the model specification, importance/sensitivity to covariates may be a global or a local issue. For models with multivariate response, we provide a criterion that (i) produces one sensitivity coefficient for each covariate, (ii) takes into account the model specification of uncertainty, and (iii) is based only on the model parameters but does not require a distribution on the covariates. However, with a prior on the covariates, in special cases, we show that comparison of covariates using this criterion gives the same results as comparison of marginal variances of the inverse predictive distributions of the covariates. We illustrate with an application examining sensitivity of tree abundance to climate.

Comparison of Traditional and Bayesian Calibration Techniques for Gray-Box Modeling

Bayesian and nonlinear least-squares methods of calibration were evaluated and compared for gray-box modeling of a retail building. Gray-box model calibration is one form of system identification and is examined here with perturbations to the simple yet popular European Committee for Standardization (CEN)-ISO thermal network model. The primary objective was to understand whether the computational expense of probabilistic Bayesian techniques is required to provide robustness to signal noise, specifically with regard to lower dimensional problems (physical or semiphysical), where model calibration is preferred over uncertainty quantification. The Bayesian approach allows parameter interactions and trade-offs to be revealed, one form of sensitivity analysis, but its full power for uncertainty quantification cannot be harnessed with gray-box or other simplified models. Surrogate data from a detailed building energy simulation program were used to ensure command over latent variables, whereas a range of signal-to-noise and noise colors were considered in the experimental study. The fidelity to the building zone temperature and thermal load was the basis for comparing results. Utilization of uniform priors showed that both methods performed similarly well. Bayesian calibration outperformed traditional methods on noisy data sets; however, traditional methods were adequate up to an approximately 25% noise level. The thermal gray-box model calibration has the intended application of embedded model predictive control, where speed, accuracy, and robustness are crucial. Traditional methods required approximately 100 times less CPU time and are recommended given the model simplicity, application, and expected system noise levels.

Data Mining using Artificial Neural Network POS_NEG Rules

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

Do political determinants affect the size and composition of public expenditure? A study of the Indian states

This study examines whether there exists a systematic link between the size and composition of public expenditure and the political determinants across the Indian states. Three types of political determinants—forms of governments, government’s ideology, and electoral cycle—are identified and three sets of hypotheses are designed linking these characteristics with three different measures of public expenditure. The hypotheses are tested using a panel dataset of 14 Indian states spread over 27 fiscal years, from 1980–1981 to 2006–2007. The overall findings of the study suggest that the relationship between expenditure measures and political determinants across the Indian states validates the proposed hypotheses even after controlling for the traditional and other unobservable determinants. These findings are robust to various forms of sensitivity analysis.

Estimating the Health Impact of Climate Change With Calibrated Climate Model Output

Studies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multi-model ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output for examining to the risks of future temperature on adverse health outcomes. Specifically, we first estimate the spatial quantile process for climate model output using nonlinear monotonic regression during a historical period. The quantile process is then calibrated using the quantile functions estimated from the observed monitoring data. Our model also down-scales the gridded climate model output to the point-level for projecting future exposure over a specific geographical region. The quantile regression approach is motivated by the need to better characterize the tails of future temperature distribution where the greatest health impacts are likely to occur. We applied the methodology to calibrate temperature projections from a regional climate model for the period 2041 to 2050. Accounting for calibration uncertainty, we calculated the number of of excess deaths attributed to future temperature for three cities in the US state of Alabama.

The economics of Rawlsian justice: can it be neoclassical?

PurposeThe purpose of this paper is to explore the compatibility of Rawls's account of justice with neoclassical economic theory, upon which Rawls relies strongly.Design/methodology/approachThis question is approached via a comparison of the implicit account of society and social relations adopted in Rawl's work with that that can be perceived to underly neoclassical economic theory. The purpose of this comparison is to assess how compatible these social visions are.FindingsIt is argued that neoclassical economic theory presupposes a social structure and a social reality that is radically less cooperative than that which underpins Rawls's theory of justice. Rawls presupposes a world in which cooperation is necessary – a specialised world – whereas the equilibrium requirements of neoclassical theory run into severe technical difficulties in such a context, with the result that they are assumed away via a series of theoretical contrivances, along with the role for cooperation that is central to Rawls's theory.Research limitations/implicationsThe paper illustrates clearly the pitfalls associated with uncritical reliance in one discipline on theoretical frameworks imported from another. Where there is debate concerning the fundamental bases of theory, a form of sensitivity analysis must be performed to ensure that the final argument does not demand too much of, or become excessively tied to, the imported framework.Originality/valueThe paper provides the beginnings of such a sensitivity analysis on the Rawlsian project and its relationship to economic theory, and shows that the field is open for a reconstitution of the liberal theory of justice on grounds other than its traditional ally, the exchange paradigm as represented by neoclassical theory.

Quantitative Assessment of Lesion Detection Accuracy, Resolution, and Reconstruction Algorithms in Neutron Stimulated Emission Computed Tomography

We present a quantitative analysis of the image quality obtained using filtered back-projection (FBP) with Ram-Lak filtering and maximum likelihood-expectation maximization (ML-EM)-with no post-reconstruction filtering in either case-in neutron stimulated emission computed tomography (NSECT) imaging using Monte Carlo simulations in the context of clinically relevant models of liver iron overload. The ratios of pixel intensities for several regions of interest and lesion shape detection using an active-contours segmentation algorithm are assessed for accuracy across different scanning configurations and reconstruction algorithms. The modulation transfer functions (MTFs) are also computed for the cases under study and are applied to determine a minimum detectable lesion spacing as a form of sensitivity analysis. The accuracy of NSECT imaging in measuring relative tissue concentration is presented for simulated clinical liver cases. When using the 15th iteration, ML-EM provides at least 25% better resolution than FBP and proves to be highly robust under low-signal high-noise conditions prevalent in NSECT. However, FBP gives more accurate lesion pixel intensity ratios and size estimates in some cases; due to advantages provided by both reconstruction algorithms, it is worth exploring the development of an algorithm that is a hybrid of the two. We also show that NSECT imaging can be used to accurately detect 3-cm lesions in backgrounds that are a significant fraction (one-quarter) of the concentration of the lesion, down to a 4-cm spacing between lesions.

Response to Letter Regarding Article, “Cost-Effectiveness Analysis of Continuous Flow Left Ventricular Assist Devices as Destination Therapy”

We appreciate the comments from Dr Moreno regarding our evaluation of the cost effectiveness of continuous flow left ventricular assist devices (LVADs) implanted as destination therapy. By their nature, health economic models involve a degree of uncertainty. As noted in a review by NICE (National Institute for Health and Clinical Excellence), “all forms of sensitivity analysis, notably both deterministic and probabilistic approaches, have their supporters and their detractors” (http://www.hta.ac.uk/fullmono/mon1329.pdf). We used a decision analytic model adapted from the Blue Cross Blue Shield Technology Evaluation Center (BCBS TEC) assessment, a leading technology assessment organization in the United States. We applied …

Simplified Bayesian Sensitivity Analysis for Mismeasured and Unobserved Confounders

We examine situations where interest lies in the conditional association between outcome and exposure variables, given potential confounding variables. Concern arises that some potential confounders may not be measured accurately, whereas others may not be measured at all. Some form of sensitivity analysis might be employed, to assess how this limitation in available data impacts inference. A Bayesian approach to sensitivity analysis is straightforward in concept: a prior distribution is formed to encapsulate plausible relationships between unobserved and observed variables, and posterior inference about the conditional exposure-disease relationship then follows. In practice, though, it can be challenging to form such a prior distribution in both a realistic and simple manner. Moreover, it can be difficult to develop an attendant Markov chain Monte Carlo (MCMC) algorithm that will work effectively on a posterior distribution arising from a highly nonidentified model. In this article, a simple prior distribution for acknowledging both poorly measured and unmeasured confounding variables is developed. It requires that only a small number of hyperparameters be set by the user. Moreover, a particular computational approach for posterior inference is developed, because application of MCMC in a standard manner is seen to be ineffective in this problem.