Standard Sensitivity Analysis Research Articles

Prognostic value of liver biochemical parameters for COVID-19 mortality

IntroductionCoronavirus disease 2019 (COVID-19) has brought great challenges to global public health. However, a comprehensive analysis of the relationship between liver biochemical parameters and COVID-19 mortality is quite limited. MethodsWe searched the following electronic databases: PubMed, Embase, Cochrane Library, Web of Science, Scopus, Wanfang and China National Knowledge Infrastructure database until May 5, 2020. STATA software was used for the statistical analyses. ResultsA total of 25 studies involving 5971 COVID-19 patients were included in our analysis. Compared with non-survivors, survivors had lower levels of aspartate aminotransferase (AST) (weighted mean difference [WMD]=−16.71U/L, 95%CI=[−21.03,−12.40], P<0.001), alanine transaminase (ALT) (WMD=−5.20U/L, 95%CI=[−8.00,−2.41], P<0.001), total bilirubin (TBIL) (WMD=4.40μmol/L, 95%CI=[−5.11,−3.70], P<0.001) and lactic dehydrogenase (LDH) (WMD=−252.44U/L, 95%CI=[−289.57,−215.30], P<0.001), and higher albumin (ALB) level (WMD=4.47g/L, 95%CI=[3.47,5.47], P<0.001). Besides, survivors had lower proportions of these abnormally increased parameters (AST: OR=0.25, 95%CI=[0.15,0.41], P<0.001; ALT: OR=0.49, 95%CI=[0.37,0.64], P<0.001; TBIL: (OR=0.20, 95%CI=[0.12,0.34], P<0.001; LDH, OR=0.09, 95%CI=[0.06,0.14], P<0.001), and lower proportion of abnormally decreased ALB (OR=0.16, 95%CI=[0.07,0.38], P<0.001). Meta-analysis based on standard mean difference and sensitivity analysis did not change the conclusions. Egger test did not detect the presence of publication bias. ConclusionsLiver biochemical parameters were strongly correlated with COVID-19 mortality. Measurement of these liver biochemical parameters might assist clinicians to evaluate the prognosis of COVID-19.

A probabilistic-based method to evaluate hygrothermal performance of an internally insulated brick wall

Uncertainty exists in many aspects of building simulation. A deterministic hygrothermal analysis may not sufficiently give a reliable guidance if a number of input variables are subject to uncertainty. In this paper, a probabilistic-based method was developed to evaluate the hygrothermal performance of building components. The approach accounts for the uncertainties from model inputs and propagates them to the outputs through the simulation model, thus it provides a likelihood of performance risk. Latin hypercube sampling technique, incorporated with correlation structure among the inputs, was applied to generate the random samples that follows the intrinsic relations. The performance of an internally insulated masonry wall was evaluated by applying the proposed approach against different criteria. Thermal performance, condensation and mould growth potential of the renovated wall can overall satisfy the requirements stipulated in multifold standards. The most influential inputs were identified by the standardized regression sensitivity analysis and partial correlation technique. Both methods deliver the same key parameters for the single and time-dependent output variables in the case study. The probabilistic method can provide a comprehensive risk analysis and support the decision-maker and engineer in the design and optimization of building components.

A global sensitivity analysis approach for marine biogeochemical modeling

This paper introduces the Sobol’ indices approach for global sensitivity analysis (SA), in the context of marine biogeochemistry. Such an approach is particularly well suited for ocean biogeochemical models, which make use of numerous parameters within large sets of differential equations with complex dependencies. This SA allows for a detailed study of the relative influence of a large number of input parameters on output quantities of interest to be chosen. It is able to distinguish between direct effects of these parameters and effects due to interaction between two or more parameters. Although demanding in terms of computation, such a tool is now becoming affordable, thanks to the development of distributed computing environments. An applicative example is presented with the MODECOGeL biogeochemical model, and illustrates the advantages of this approach over standard local SA.

Standardized Sensitivity Analysis in BCA: An Education Case Study.

Benefit-cost analyses of education policies in low- and middle-income countries have historically used the effect of education on future wages to estimate benefits. Strong evidence also points to female education reducing both the under-five mortality rates of their children and adult mortality rates. A more complete analysis would thus add the value of mortality risk reduction to wage increases. This paper estimates how net benefits and benefit-cost ratios respond to the values used to estimate education’s mortality-reducing impact including variation in these estimates. We utilize a ‘standardized sensitivity analysis’ to generate a range of valuations of education’s impact on mortality risks. We include alternative ways of adjusting these values for income and age differences. Our analysis is for one additional year of schooling in lower-middle-income countries, incremental to the current mean. Our analysis shows a range of benefit-cost ratios ranging from 3.2 to 6.7, and net benefits ranging from $2,800 to $7,300 per student. Benefits from mortality risk reductions account for 40% to 70% of the overall benefits depending on the scenario. Thus, accounting for changes in mortality risks in addition to wage increases noticeably enhances the value of already attractive education investments.

Valuing Mortality Risk Reductions in Global Benefit-Cost Analysis.

The estimates used to value mortality risk reductions are a major determinant of the benefits of many public health and environmental policies. These estimates (typically expressed as the value per statistical life, VSL) describe the willingness of those affected by a policy to exchange their own income for the risk reductions they experience. While these values are relatively well studied in high-income countries, less is known about the values held by lower-income populations. We identify 26 studies conducted in the 172 countries considered low- or middle-income in any of the past 20 years; several have significant limitations. Thus there are few or no direct estimates of VSL for most such countries. Instead, analysts typically extrapolate values from wealthier countries, adjusting only for income differences. This extrapolation requires selecting a base value and an income elasticity that summarizes the rate at which VSL changes with income. Because any such approach depends on assumptions of uncertain validity, we recommend that analysts conduct a standardized sensitivity analysis to assess the extent to which their conclusions change depending on these estimates. In the longer term, more research on the value of mortality risk reductions in low- and middle-income countries is essential.

Topology optimization for linear thermo‐mechanical transient problems: Modal reduction and adjoint sensitivities

SummaryThis paper focuses on topology optimization for linear transient thermo‐mechanical problems. The latter are, for example, encountered for extreme precision tools and instrumentation. Due to the transient nature, a standard adjoint sensitivity analysis will result in a backward transient analysis for the adjoint variables, leading to both storage and computational inefficiencies. A method is proposed that rigorously eliminates the backward transient integration for the adjoint sensitivity analysis. At the basis is a model‐order reduction technique, which relies on a reduced thermal modal basis combined with static correction. The modal amplitudes can be readily obtained semi‐analytically using simple convolutions. This accurate but reduced‐order model is the starting point for an adjoint sensitivity analysis. Via a tactic selection of adjoint variables, the backward transient analysis for the adjoints is completely eliminated, whereas computational efficiency and consistency are maintained. The effectiveness of the resulting adjoint sensitivities and their application in topology optimization are demonstrated on the basis of several test examples.

The HTA Risk Analysis Chart: Visualising the Need for and Potential Value of Managed Entry Agreements in Health Technology Assessment.

BackgroundRecent changes to the regulatory landscape of pharmaceuticals may sometimes require reimbursement authorities to issue guidance on technologies that have a less mature evidence base. Decision makers need to be aware of risks associated with such health technology assessment (HTA) decisions and the potential to manage this risk through managed entry agreements (MEAs).ObjectiveThis work develops methods for quantifying risk associated with specific MEAs and for clearly communicating this to decision makers.MethodsWe develop the ‘HTA risk analysis chart’, in which we present the payer strategy and uncertainty burden (P-SUB) as a measure of overall risk. The P-SUB consists of the payer uncertainty burden (PUB), the risk stemming from decision uncertainty as to which is the truly optimal technology from the relevant set of technologies, and the payer strategy burden (PSB), the additional risk of approving a technology that is not expected to be optimal. We demonstrate the approach using three recent technology appraisals from the UK National Institute for Health and Clinical Excellence (NICE), each of which considered a price-based MEA.ResultsThe HTA risk analysis chart was calculated using results from standard probabilistic sensitivity analyses. In all three HTAs, the new interventions were associated with substantial risk as measured by the P-SUB. For one of these technologies, the P-SUB was reduced to zero with the proposed price reduction, making this intervention cost effective with near complete certainty. For the other two, the risk reduced substantially with a much reduced PSB and a slightly increased PUB.ConclusionsThe HTA risk analysis chart shows the risk that the healthcare payer incurs under unresolved decision uncertainty and when considering recommending a technology that is not expected to be optimal given current evidence. This allows the simultaneous consideration of financial and data-collection MEA schemes in an easily understood format. The use of HTA risk analysis charts will help to ensure that MEAs are considered within a standard utility-maximising health economic decision-making framework.

Sensitivity Analysis of the Integral Quality Monitoring System® Using Monte Carlo Simulation.

The Integral Quality Monitoring (IQM) System is a real-time beam output verifying system that validates the integrity and accuracy of patient treatment plan (TP) data during radiation treatment. The purpose of this study was to evaluate the sensitivity of the IQM to errors in segment using EGSnrc/BEAMnrc Monte Carlo (MC) codes. Sensitivity analysis (SA) techniques were applied to study the significance of small alterations of field sizes (segments) on the IQM signal response. One hundred and eighty multileaf segments were analyzed with methods that include scatter plots (SP), brute force, variance-based (VAR), and standard regression coefficient SA. The segments were altered randomly within ±1, ±2, and ±3 mm leaf steps for 10 MV photon beams. SP analysis gradient and VAR maximum index are 1.045 and 0.556 for the smallest segment while the largest segment has the value of 0.018 and 0.504, respectively. The brute force and standard regression displayed maximum sensitivity indices around the unaltered segments. These tests conclusively indicated that the IQM was more sensitive to alterations of small segments compared to larger segments. This is important since small segment variation will cause a higher dose output variation that should be picked up during online beam monitoring.

Optimal wavy surface to suppress vortex shedding using second-order sensitivity to shape changes

A method to find optimal 2nd-order perturbations is presented, and applied to find the optimal spanwise-wavy surface for the suppression of cylinder wake instability. As shown in recent studies (Hwang et al., 2013, Tammisola et al., 2014, Del Guercio et al., 2014), 2nd-order perturbations are required to capture the stabilizing effect of spanwise waviness, which is ignored by standard adjoint-based sensitivity analyses. Here, previous methods are extended so that (i) 2nd-order sensitivity is formulated for base flow changes satisfying the linearized Navier–Stokes, and (ii) the resulting method is applicable to a 2D global instability problem. This makes it possible to formulate the 2nd-order sensitivity to shape modifications. This formulation is used to find the optimal shape to suppress the cylinder wake instability. The optimal shape is then perturbed by random distributions in full 3D stability analysis to confirm that it is a local optimal at the given amplitude and wavelength. At Re=100, surface waviness of maximum height 1% of the cylinder diameter is sufficient to stabilize the flow. The optimal surface creates streaks passively by extracting energy from the base flow derivatives and altering the tangential velocity component at the wall. This paper extends previous techniques to a fully two-dimensional method to find boundary perturbations which optimize the 2nd-order drift. The method should be applicable to generic flow instability problems, and to different types of control, such as boundary forcing, shape modulation or suction.

Commute duration and health: Empirical evidence from Brazil

There have been many empirical studies associating commuting time and health outcomes in the last few decades. Their general conclusion is that commuting and health are negatively related. The validity of their findings, however, is questionable, given their lack of good identification strategies to correctly account for omitted variables. In this paper, we analyze this relationship using a large and unique nationally representative sample of Brazilian individuals, coupled with the use of propensity score matching techniques, and the application of an exhaustive set of standard falsification tests and sensitivity analysis that may prevent one from claiming a causal link between the two variables. Our results indicate that individuals with more than one hour of commuting appear to have statistically higher probability, ranging from 1.9 to 4.6 percentage points, of reporting bad health status when compared to a person whose commuting time is less than one hour.

A Single-Center Multidisciplinary Initiative to Reduce Catheter-Associated Urinary Tract Infection Rates: Quality and Financial Implications.

Catheter-associated urinary tract infection (CAUTI) is an important patient safety issue that is responsible for an estimated 449334 annual infections, with an average direct cost of $790-$1200 per infection. In total, the cost associated with CAUTI is estimated to be $115 million to $1.82 billion annually. We conducted an internal revenue analysis with a standard sensitivity analysis to assess the impact of a low-cost CAUTI reduction program on direct costs to the hospital over four years. The interventions included the formation of a multidisciplinary CAUTI reduction task force, formal data collection in all ICUs, staff education, and new electronic order sets with decision support. During the initial intervention period, the infection rate per 1000 catheter days decreased from 5.4 to 1.5. In the second year of the program, the infection rate increased to 4.6. After additional interventions were launched, infection rates decreased to 2.2. Cost savings per 1000 catheter days (±20%) during the initial intervention were $4501 ($3600-$5401). Our intervention demonstrated that provider education and electronic documentation prompts were followed by a significant decrease in catheter utilization, that in turn was followed by lower infection rates. Decreased emphasis on intervention goals were followed by an increase in CAUTI rates. Our subsequent interventions suggest that upward trends may be reversible.

Second-order sensitivity analysis for a photonic crystal waveguide bend

Sensitivity analysis provides important information on the tolerance to fabrication imperfections for any designed devices. Standard sensitivity analysis relies on the first-order partial derivatives of a response function of the device with respect to the design parameters. These derivatives are also useful in the device optimization process when a gradient based optimization method is used. The first-order sensitivity analysis becomes inadequate if the first-order derivatives are small or zero. In this paper an efficient method for computing second-order partial derivatives is developed for idealized two-dimensional photonic crystal (PhC) devices with circular cylinders. The response function is the normalized power-transmission coefficient in an output waveguide and the design parameters are the radii of the cylinders. Based on that, a second-order sensitivity analysis is performed for a 90&#xB0; PhC waveguide bend at the frequency where the transmission coefficient is close to 1.

What is the Clinical and Cost Effectiveness of Erythropoietin-Stimulating Agents for the Treatment of Patients with Cancer-Treatment Induced Anaemia? Insights from Cumulative Meta-Analyses (Cma) and Lessons for Cost-Effectiveness Analyses

ABSTRACT Aim: A health technology assessment (HTA) informing the recent NICE guidance use of erythropoiesis-stimulating agents (ESA) in cancer-treatment induced anaemia (CIA) identified uncertainty around precision and the direction of overall survival hazard ratio (OSHR). It also found that cost-effectiveness results were extremely sensitive to changes in OSHR. We investigate how the understanding of OS in CIA patients treated with ESAs has shaped over time and the effects of accumulating OS evidence on cost-effectiveness. Methods: CMA was applied to the HTA review OS data to identify patterns in results; study results were accumulated by the year of publication. As with the HTA review a random effects model was assumed. The annual OSHR results from the CMA were applied to an economic model developed in the HTA to calculate long term quality-adjusted life year (QALY) gains and the corresponding incremental cost-effectiveness ratios. Results: Although the precision of the OSHR estimate appeared to be improving with additional evidence, the true location of the estimate remained uncertain and the best estimate varied over time; no conclusive patterns were identified. Using the CMA, results from 2001 and 2002 suggested survival benefits to using ESAs (0.77, 95% CI 0.60–0.98 and 0.78, 95% CI 0.65–0.93 respectively), with ESAs being cost-effective at a willingness to pay threshold of £30,000 per QALY, even at the upper limit of the OSHR 95% confidence interval. Apart from the 2001 and 2002 results, the CMA for other years, including the most recent estimates and including the overall estimate, did not suggest any significant effects of ESAs on OS. Conclusions: CMA results confirm the lack of stability of OSHR. The possibility that there is no difference in OS for patients receiving ESAs compared to those who do not cannot be rejected, nor is there sufficient evidence to support this conclusion. This analysis also highlights the additional uncertainty of the current evidence base on cost-effectiveness analyses, which cannot be captured in standard sensitivity analyses. Disclosure: All authors have declared no conflicts of interest.

Second-order perturbation of global modes and implications for spanwise wavy actuation

AbstractSensitivity analysis has successfully located the most efficient regions in which to apply passive control in many globally unstable flows. As is shown here and in previous studies, the standard sensitivity analysis, which is linear (first order) with respect to the actuation amplitude, predicts that steady spanwise wavy alternating actuation/modification has no effect on the stability of planar flows, because the eigenvalue change integrates to zero in the spanwise direction. In experiments, however, spanwise wavy modification has been shown to stabilize the flow behind a cylinder quite efficiently. In this paper, we generalize sensitivity analysis by examining the eigenvalue drift (including stabilization/destabilization) up to second order in the perturbation, and show how the second-order eigenvalue changes can be computed numerically by overlapping the adjoint eigenfunction with the first-order global eigenmode correction, shown here for the first time. We confirm the prediction against a direct computation, showing that the eigenvalue drift due to a spanwise wavy base flow modification is of second order. Further analysis reveals that the second-order change in the eigenvalue arises through a resonance of the original (2-D) eigenmode with other unperturbed eigenmodes that have the same spanwise wavelength as the base flow modification. The eigenvalue drift due to each mode interaction is inversely proportional to the distance between the eigenvalues of the modes (which is similar to resonance), but also depends on mutual overlap of direct and adjoint eigenfunctions (which is similar to pseudoresonance). By this argument, and by calculating the most sensitive regions identified by our analysis, we explain why an in-phase actuation/modification is better than an out-of-phase actuation for control of wake flows by spanwise wavy suction and blowing. We also explain why wavelengths several times longer than the wake thickness are more efficient than short wavelengths.

Comprehensive Approach for the Sensitivity Analysis of High-Dimensional and Computationally Expensive Traffic Simulation Models

The reliability of traffic model results is strictly connected to the quality of its calibration. A challenge arising in this context concerns the selection of the most influential input parameters. A model sensitivity analysis should be used with this aim. However, because of the limitations of time and computational resources, a proper sensitivity analysis is rarely performed in common practice. A recent study introduced a methodology based on Gaussian process metamodels for the sensitivity analysis of computationally expensive traffic simulation models. The main limitation was a dependence on model dimensionality. When the model has more than about 15 to 20 parameters, estimation of a Gaussian process metamodel (also known as a Kriging metamodel) may become problematic. In this paper, the Kriging-based approach is coupled with a recently developed approach, quasi-optimized trajectory-based elementary effects (quasi-OTEE), for the sensitivity analysis of computationally expensive models. The quasi-OTEE sensitivity analysis can be used to identify the whole subset of sensitive parameters of a high-dimensional model, and the Kriging-based sensitivity analysis can then be used to refine the analysis and to rank the different parameters of the subset in a more reliable way. Application of this new sequential sensitivity analysis method is illustrated with the Wiedemann-74 car-following model. Results show that the new method requires 40 times fewer model evaluations than a standard variance-based sensitivity analysis to identify the influential parameters and their ranks.

Using finite mixture models in thermal-hydraulics system code uncertainty analysis

Nuclear Power Plant safety analysis is mainly based on the use of best estimate (BE) codes that predict the plant behavior under normal or accidental conditions. As the BE codes introduce uncertainties due to uncertainty in input parameters and modeling, it is necessary to perform uncertainty assessment (UA), and eventually sensitivity analysis (SA), of the results obtained. These analyses are part of the appropriate treatment of uncertainties imposed by current regulation based on the adoption of the best estimate plus uncertainty (BEPU) approach. The most popular approach for uncertainty assessment, based on Wilks’ method, obtains a tolerance/confidence interval, but it does not completely characterize the output variable behavior, which is required for an extended UA and SA. However, the development of standard UA and SA impose high computational cost due to the large number of simulations needed. In order to obtain more information about the output variable and, at the same time, to keep computational cost as low as possible, there has been a recent shift toward developing metamodels (model of model), or surrogate models, that approximate or emulate complex computer codes. In this way, there exist different techniques to reconstruct the probability distribution using the information provided by a sample of values as, for example, the finite mixture models. In this paper, the Expectation Maximization and the k-means algorithms are used to obtain a finite mixture model that reconstructs the output variable probability distribution from data obtained with RELAP-5 simulations. Both methodologies have been applied to a separated effects experiment, and to an integral effects simulation.

Moment-independent regional sensitivity analysis: Application to an environmental model

Through several decades of development, global sensitivity analysis has been developed as a very useful guide tool for assessing scientific models and has gained pronounced attention in environmental science. However, standard global sensitivity analysis aims at measuring the contribution of input variables to model output uncertainty on average by investigating their full distribution ranges, but does not investigate the contribution of specific ranges. To deal with this problem, researchers have developed several regional sensitivity analysis techniques such as the contribution to sample mean and variance (CSM and CSV) plots. In this paper, a moment-independent regional sensitivity analysis technique called contribution to delta indices (CDI) plot is developed for assessing the effect of a specific range of an individual input to the uncertainty of model output. The CDI plot can be obtained with the same set of samples used for computing the CSM and CSV. Compared with the CSM and CSV, the CDI plot uses the probability density function shift of model output to describe the uncertainty instead of the mean and variance, thus it is moment-independent. An analytical linear model, the Ishigami function and an environmental model are employed to test the proposed RSA technique.

캘리포니아 표준테스트 방법을 사용한 전기냉방기기의 가스냉방기기 대체에 따른 편익비용분석

There are some efforts to improve the performance of electrical heat pump(EHP) and replace it with an alternative cooling equipment such as gas engine-driven heat pump(GHP), a gas cooling equipment, in order to solve the problem of summer electricity supply through reducing the summer electricity peak. This paper analyzes cost-benefit in accordance with replacement of electrical cooling system by gas cooling system using california standard test and sensitivity analysis of some scenarios.

Combining a weather generator and a standard sensitivity analysis method to quantify the relevance of weather variables on agrometeorological models outputs

Sensitivity analysis (SA) is increasingly used to explain models behaviour in response to inputs variation. Agrometeorologists are used to apply standard SA methods only on model parameters because of the difficulty of applying standard sampling techniques to derive series of weather data where each value cannot be sampled independently from those of the neighbouring days and from other variables in the same day. The impact of weather variability on a crop model was here analysed by coupling the Morris SA method to a weather generator. Spring barley in northern Italy was simulated and different outputs considered. Under the explored conditions, parameters involved with temperature generation resulted the most relevant in determining yield and maturity date. Radiation-related parameters were high-ranked for cumulated drainage and actual evapotranspiration. According to the author, this is the first time the sensitivity of a cropping system model to weather variables is quantified using standard SA techniques.

Transient sensitivity analysis for nonlinear population models

Summary1. Performing sensitivity analyses for deterministic multi‐stage, multi‐parameter population models that include nonlinearity presents a significant computational challenge.2. We implement a standard forward sensitivity analysis to evaluate partial derivatives of population state variables with respect to parameters at all times for which the solution is known. We present a hybrid Matlab Maple software package, sensai, which automates the steps required to calculate sensitivities and elasticities. We focus on sensitivities of transient states of populations. Our analysis therefore differs from the more common analysis of linear systems where sensitivities are computed for the asymptotic stable‐stage distribution assuming unbounded population growth. The method generalizes the matrix calculus methods for nonlinear, stage‐structured matrix models previously developed by Caswell (2009, Journal of Difference Equations and Applications, 15, 349–369).3. We present an example of a nonlinear, discrete‐time population model in the form of a stage‐structured Lefkovitch matrix, and another multi‐stage, continuous‐time SIR disease model in the form of a system of ordinary differential equations. In addition, we extend the framework for composite nonlinear maps of population demography to include single‐locus genetics with natural selection discriminating among genotypes.4. SENSAI allows for the analysis of a quantity of interest (an arbitrary function of variables and parameters, e.g. the proportion of infected individuals in a disease model), and sensitivity to combinations of model parameters. For example, we can compute sensitivities with respect to parameters and initial conditions at all times during an outbreak in a disease model, before a steady state of disease prevalence is attained. Similarly, in population genetic models, we can compute sensitivity of a response to natural selection (e.g. the change in allele frequencies) in relation to fitness differences among genotypes.5. We illustrate the capabilities of sensai through a series of canonical models with relatively few variables and parameters. However, sensai has the capability to analyse significantly more complex models.