Sensitivity Analysis Strategy Research Articles

Remote and Unsupervised Exercise Strategies for Improving the Physical Activity of Colorectal Cancer Patients: A Meta-Analysis.

Colorectal cancer (CRC) burden across the world is expected to increase by ~2.2 million new cases and ~1.1 million deaths by 2030. Regular physical exercise is recommended to prevent CRC, but the myriad of protocols preclude further discussion on how to manage its variables for this population. Home-based exercise guided by remote monitoring provides an alternative to surpass the barriers of supervised exercise. However, no meta-analysis was conducted to verify the effectiveness of this intervention for improving physical activity (PA). We performed a systematic review of remote and unsupervised strategies imposed on CRC patients for improving PA and compared, via a meta-analysis, their effectiveness against CRC patients submitted to usual care or no intervention. The databases PubMed, Scopus, and Web of Science were searched on 20 September 2022. Eleven studies attained the criteria for eligibility in the qualitative approach, and seven were included in the meta-analysis. No significant effect (p = 0.06) of remote and unsupervised exercise intervention was observed. However, a sensitivity analysis including three studies that only considered CRC patients was performed, demonstrating a significant effect in favor of exercise (p = 0.008). Based on our sensitivity analysis, remote and unsupervised exercise strategies were effective to improve the PA of CRC patients.

Matched design for marginal causal effect on restricted mean survival time in observational studies

Abstract Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using hazard ratio (HR) as the marginal causal effect measure due to noncollapsibility. In this article, we advocate using restricted mean survival time (RMST) difference as a marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is generally more robust than outcome modeling due to potential misspecifications. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased, and the corresponding variance estimator is calculated by accounting for the correlation due to matching. Simulation studies also demonstrate that our method has adequate empirical performance and outperforms several competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival.

Life cycle assessment-driven collaborative optimization model of power dry cutting for face-hobbing hypoid gear production

Efficient, high-precision, energy-saving, green manufacturing and low-cost design is an increasingly important target to life cycle assessment (LCA) for high-performance hypoid gear production. With development of numerical controlled (NC) technology of hypoid generator, power dry cutting is enabling ever-higher levels of production efficiency and sustainability. It also has the potential to dramatically influence sustainable development of hypoid gear product. Sufficient guidance, in this respect, is lacking in the scholarly or practitioner literature. In this work, focusing on LCA for hypoid gear production, we further examine power dry cutting in terms of application and sustainability implications. We introduce collaborative optimization considering geometric accuracy and loaded contact performances into the power dry cutting. Here, LCA-driven power dry cutting collaborative optimization model is proposed for face-hobbing hypoid gear production. At first, complex machine kinematics between cutting tool and work blank are developed for power dry cutting simulation. Then, tooth flank mathematical modeling is performed to output discretization points in a closed-form of the whole tooth flank including concave flank and convex flank. Moreover, in order to improve LCA, an accurate model of material removal collaborative optimization considering both geometric accuracy and loaded contact pattern is established for face-hobbing hypoid gears. Where, time-varying material removal path planning is developed. Then, sensitivity analysis strategy and genetic algorithm (GA) are proposed for the proposed model solution, respectively. Finally, numerical instances can demonstrate that the proposed model can get a very important access to power dry cutting having high LCA for face-hobbing hypoid gear production.

Rapid and robust analysis of aristolochic acid I in Chinese medicinal herbal preparations by surface-enhanced Raman spectroscopy

The use of Chinese herbs containing aristolochic acid can induce the exchange of adenine and thymine in gene mutations and even cause liver cancer. To eliminate the harm of aristolochic acids (AAs) to humans, a rapid and robust method of AAs screening is a prerequisite. In this work, a facile and robust Surface-enhanced Raman spectroscopy (SERS) method was used for the qualitative and quantitative detection of AAs in Chinese medicinal herbal preparations based on the mandelic acid modified Ag nanoparticles SERS substrate. Qualitative and quantitative SERS detection of Aristolochic acid I (AAI) was achieved with a good linear relationship ranging from 0.2 − 120.0 μM and a limit of detection (LOD) of 0.06 μM. The proposed method demonstrates a refined strategy for sensitivity analysis of AAs with the advantages of easy operation, time-saving, high sensitivity, and molecular specificity, making it a preferred platform for the screening of AAI in regular inspections of herbal products and regulatory supervision of the supply chain.

Ozone Sensitivity Analysis and Control Strategy in Shijiazhuang City in July 2019

The pollution characteristics of surface ozone and its response to meteorological factors were studied based on monitoring in July 2019 in Shijiazhuang City, China. Furthermore, the WRF-CMAQ model coupled with O3 isopleths (EKMA curves) were applied to explore the non-linear response relationship of O3 to precursors VOCs and NOx, aiming to identify a suitable precursor control strategy. The results showed that the days with the maximum daily 8-hour average ozone concentration (MDA8 O3) exceeded the standard by 70.9%. The nonattainment days were usually accompanied by higher temperature, lower relative humidity, and low winds, and the south and southeast winds occurred frequently. The O3 formation was in the strong VOC-limited regime in the urban area of Shijiazhuang, whereas it was in the NOx and VOCs transition regime in suburban areas. As for the urban area, under the condition of single NOx emission reduction, O3 pollution improved when continuous NOx emission reduction was higher than 50% during the nonattainment days. By contrast, during the non-polluted days, O3 concentrations would not rebound when the reduction ratio of NOx and VOCs was higher than 1. In conclusion, VOCs reduction should be the priority for emission reduction plans in urban areas, whereas all different NOx and VOCs ratios led to a decline in O3 concentration in the suburban areas, and a VOCs:NOx of 1:2 was recommended.

Input parameter and thermal load representation uncertainties effects on the buckling and dynamic characteristics of cylindrical tubes

This research considers different methods in sensitivity analysis and uncertainty quantification applied to cylindrical tubes subject to three types of thermal load representation, namely, uniform, linear, and nonlinear in the radial direction. Sensitivity analysis techniques are used to quantify and rank the parameters that are most influential in altering the critical buckling temperatures, the post-buckled configuration amplitudes, and the natural frequencies in the pre- and post-buckling regimes. The uncertainty quantification and sensitivity analysis strategies show a great potential and usefulness in terms of determining the most influential input parameters for cylindrical tubes subject to thermal loads. Based on the set of nominal parameters in this study, it is shown that the length is the most sensitive parameter in altering the critical thermal buckling load. Additionally, it is demonstrated how uncertainty in the thermal load representation can lead to over or underestimation in both sensitivity analysis and uncertainty quantification findings. The outcome of this analysis can be utilized by other researchers in the design and optimization of cylindrical tubes in thermal environment.

Posttreatment confounding in causal mediation studies: A cutting-edge problem and a novel solution via sensitivity analysis.

In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of posttreatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a posttreatment confounder of the mediator-outcome relationship due to incomplete information: for any given individual, a posttreatment confounder is observed under the actual treatment condition while missing under the counterfactual treatment condition. This paper proposes a new sensitivity analysis strategy for handling posttreatment confounding and incorporates it into weighting-based causal mediation analysis. The key is to obtain the conditional distribution of the posttreatment confounder under the counterfactual treatment as a function of not only pretreatment covariates but also its counterpart under the actual treatment. The sensitivity analysis then generates a bound for the natural indirect effect and that for the natural direct effect over a plausible range of the conditional correlation between the posttreatment confounder under the actual and that under the counterfactual conditions. Implemented through either imputation or integration, the strategy is suitable for binary as well as continuous measures of posttreatment confounders. Simulation results demonstrate major strengths and potential limitations of this new solution. A reanalysis of the National Evaluation of Welfare-to-Work Strategies (NEWWS) Riverside data reveals that the initial analytic results are sensitive to omitted posttreatment confounding.

Multivariate statistical sensitivity analysis of the completed structural state for Long-span Cable-Stayed bridges

Conventional statistical sensitivity analysis approaches can be prohibitively expensive or impracticable computationally for the long-span cable-stayed bridge involving many parameters. In this study, a novel statistical sensitivity analysis strategy is proposed to enhance the feasibility for the construction process and the completed structural state of long-span cable-stayed bridges. A uniform test design is first incorporated into the equivalent marginal distribution problem in statistics to characterise the parameter variations, and parameter significance testing is employed to identify the significant structural parameters. Moreover, the parameter substructure is introduced to the determination of random variables during the test design process to balance the computational efficiency and structural parameter variation. Thus, the structural components fabricated or constructed in the same batches or under similar construction conditions are grouped into a single parameter substructure, and each structural parameter in the substructure is defined as one random variable. When the number of structural parameters becomes so large that the uniform test design is difficult to perform effectively, the structural parameters are then grouped to ensure the uniform test design is implementable. After the significant parameters and surrogate models of the sub-group test designs are determined, the integrated surrogate model including all of the significant parameters is obtained based on the property of the normalised regression coefficients in multivariate stepwise regression. Furthermore, t-tests are used to determine the significant structural parameters for the structural responses. A long-span cable-stayed bridge with a composite/hybrid girder is considered as a case study to verify the proposed multivariate statistical sensitivity analysis. The discussion reveals that the impacts of structural parameter variations on the structural responses are not only related to the variation ranges of structural parameters but also to the relative position between the section to which the structural parameter belongs and the section to which the structural response belongs.

Sensitive data-driven tooth surface collaborative grinding model for aerospace spiral bevel gears

To distinguish with the conventional grinding requiring geometric accuracy and loaded contact pattern (LCP), an innovative data-driven tooth surface collaborative grinding model is established by fine-modifying sensitive hypoid generator parameters. Especially, in sensitivity analysis-based adaptive prediction, robust control, an innovative adaptive material removal optimization considering loaded contact deformation of both normal and tangential directions is developed. It shows local constraint for LCP in tangential direction, and global constraint for geometric performance in normal direction. Moreover, in normal direction, the loaded contact deformation is also taken into account for an accurate prediction of tooth surface deformation. Then, the nonlinear adaptive prediction is performed by using universal motion concept (UMC) hypoid generator parameter modification. A sensitivity analysis strategy of hypoid generator parameters is used to perform adaptive robust control. Finally, Levenberg-Marquardt (L-M) method is adopted to solve the established control model for accurate optimal hypoid generator parameters with modification variations. The given test instance can verify the proposed method.

Sensitivity analysis for calibrated inverse probability-of-censoring weighted estimators under non-ignorable dropout.

Inverse probability of censoring weighting is a popular approach to handling dropout in longitudinal studies. However, inverse probability-of-censoring weighted estimators (IPCWEs) can be inefficient and unstable if the weights are estimated by maximum likelihood. To alleviate these problems, calibrated IPCWEs have been proposed, which use calibrated weights that directly optimize covariate balance in finite samples rather than the weights from maximum likelihood. However, the existing calibrated IPCWEs are all based on the unverifiable assumption of sequential ignorability and sensitivity analysis strategies under non-ignorable dropout are lacking. In this paper, we fill this gap by developing an approach to sensitivity analysis for calibrated IPCWEs under non-ignorable dropout. A simple technique is proposed to speed up the computation of bootstrap and jackknife confidence intervals and thus facilitate sensitivity analyses. We evaluate the finite-sample performance of the proposed methods using simulations and apply our methods to data from an international inception cohort study of systemic lupus erythematosus. An R Markdown tutorial to demonstrate the implementation of the proposed methods is provided.

Sensitive misalignment-based dynamic loaded meshing impact diagnosis mechanism for aviation spiral bevel gear transmission

Focusing on dynamic loaded meshing impact under complex operation condition, its innovative diagnosis mechanism including judgment, prediction, sensitivity analysis and optimization is developed for aviation spiral bevel gear transmission by modifying the misalignment which is of strong sensitivity. Firstly, data-driven finite element modeling relating to misalignments for aviation gear transmission structure is developed. Then, dynamic loaded meshing impact judgement for the initial misalignments is investigated by using dynamic loaded tooth contact analysis (DLTCA). Where, loaded contact pattern is used for impact position evaluation, and dynamic loaded contact pressure is used for impact force evaluation considering the action area. A semi-finite element method (semi-FEM) solution is used for prediction of dynamic loaded meshing impact evaluations including torque distribution, impact force, angular velocity and angular acceleration. Moreover, a new sensitive misalignment-based optimization is developed to get an improved dynamic meshing impact. Sensitivity analysis strategy is applied to select a small amount of misalignments as the optimal design variables for improving diagnosis efficiency. The singular value decomposition (SVD) algorithm is used for solve the established optimization model for accurate misalignments with modification variations. Finally, the numerical and test instances can verify the proposed method.

Como interpretar uma metanálise?

There is an enormous and ever-growing quantity of healthcare information available and practitioners must transform it into knowledge to be able to use it in their clinical practice. Even readers who do not conduct scientific studies themselves need to understand the scientific method in detail to be able to critically evaluate scientific articles. Evidence-based healthcare (EBH) can be defined as the link between good scientific research and clinical practice and systematic reviews constitute one of the forms of research excellence proposed within EBH. Systematic reviews employ rigorous methods that reduce the occurrence of bias. Systematic reviews with meta-analyses generally optimize the results found, because quantitative analysis of the studies included in the review yields additional information. In this paper, we will discuss how to interpret a meta-analysis and how to apply subset and sensitivity analysis strategies and we will also describe possible sources of heterogeneity and common errors that can affect a meta-analysis.

How to interpret a meta-analysis?

There is an enormous and ever-growing quantity of healthcare information available and practitioners must transform it into knowledge to be able to use it in their clinical practice. Even readers who do not conduct scientific studies themselves need to understand the scientific method in detail to be able to critically evaluate scientific articles. Evidence-based healthcare (EBH) can be defined as the link between good scientific research and clinical practice and systematic reviews constitute one of the forms of research excellence proposed within EBH. Systematic reviews employ rigorous methods that reduce the occurrence of bias. Systematic reviews with meta-analyses generally optimize the results found, because quantitative analysis of the studies included in the review yields additional information. In this paper, we will discuss how to interpret a meta-analysis and how to apply subset and sensitivity analysis strategies and we will also describe possible sources of heterogeneity and common errors that can affect a meta-analysis.

Sensitivity Analysis Strategy to Assess the Salting-Out Problem during CO2 Geological Storage Process

The sensitivity analysis of the salting-out effect on well injectivity is a significant work in the research of geological storage of CO2 in deep saline aquifers, which is helpful in the selection of storage sites and the design of the injection strategy. We conduct a detailed sensitivity analysis about the salting-out process using the local sensitivity method and two global sensitivity methods. Sensitivity coefficients showed that brine salinity (XNaCl) has the highest sensitivity and interaction effect, the CO2 injection rate (QCO2) has a greater influence in the early stage of the salting-out process and a smaller influence in the end stage, and the other three parameters (empirical parameters related to the pore distribution m, the liquid residual saturation in the relative permeability function Splr, and the liquid residual saturation in the capillary pressure function Sclr) have a smaller sensitivity. This paper also analyzes the calculation amount of different sensitivity methods and suitable ways of obtaining the sensitivity coefficient and reveals the following. (1) The sensitivity coefficient changes dynamically with time, if only the sensitivity of the final state is taken into account on a long-time physical process, and some sensitive parameters during the process may be neglected. (2) The selection of the sample size should be based on the convergence of multiple calculations, and the results of the empirical calculation are uncertain. (3) The calculation of Sobol sensitivity is complicated, the results calculated by surrogate model depend on whether the sample is representative enough; on the other hand, it is feasible to use Sti-Si approximation to characterize the second-order sensitivity to reduce the computation. The research results not only reveal the sensitivity of the parameters related to the injection well salting-out problem during CO2 storage in deep saline aquifers but also guide the calculation of global sensitivity analysis with a similar physical process.

Childhood Disadvantage and Later-Life Multimorbidity: Demonstrating Robustness Under Imperfect Identification

This study assesses the relationship between indicators of early childhood disadvantage and later-life multimorbidity within a nationally representative sample of Brazilians aged 50 and over (n = 9,412). Data come from the baseline assessment (2015/2016) of the Brazilian Longitudinal Study of Aging (ELSI). We employed survey-weighted Ordinary Least Squares regression to estimate the effects of individual and combined measures of childhood disadvantage on the total number of chronic conditions in later life. Mediation analysis assessed whether adult socioeconomic status (SES) mediated the relationship between childhood disadvantage and chronic conditions. We found that individual and combined measures of childhood disadvantage were associated with the total number of chronic conditions, even after controlling for potential confounders. Mediation analysis suggested that part of the effect of childhood adversity is mediated by higher SES in adulthood (~10%). A formal strategy of sensitivity analysis showed that omitted variable bias is extremely unlikely. To rule out the observed effect, an unobserved hypothetical confounder would need the explanatory power of the residual variance of both the independent and the dependent variables that is at least 30 times larger than that of BMI or 5 times larger than the explanatory power of age. Results should inform efforts to strengthen interventions targeting early childhood development and to improve other key inputs (such as education) to enhance adult SES and lessen the impact of early life stressors on health in older adulthood.

Simulation-based sensitivity analysis for causal mediation studies.

Causal inference regarding a hypothesized mediation mechanism relies on the assumptions that there are no omitted pretreatment confounders (i.e., confounders preceding the treatment) of the treatment-mediator, treatment-outcome, and mediator-outcome relationships, and there are no posttreatment confounders (i.e., confounders affected by the treatment) of the mediator-outcome relationship. It is crucial to conduct a sensitivity analysis to determine if a potential violation of the assumptions would easily change analytic conclusions. This article proposes a simulation-based method to assess the sensitivity to unmeasured pretreatment confounding, assuming no posttreatment confounding. It allows one to (a) quantify the strength of an unmeasured pretreatment confounder through its conditional associations with the treatment, mediator, and outcome; (b) simulate the confounder from its conditional distribution; and (c) finally assess its influence on both the point estimation and estimation efficiency by comparing the results before and after adjusting for the simulated confounder in the analysis. The proposed sensitivity analysis strategy can be implemented for any causal mediation analysis method. It is applicable to both randomized experiments and observational studies and to mediators and outcomes of different scales. A visualization tool is provided for vivid representations of the sensitivity analysis results. An R package mediationsens has been developed for researchers to implement the proposed method easily (https://cran.r-project.org/web/packages/mediationsens/index.html). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Multi-station calibration strategy for evaluation and sensitivity analysis of the snowmelt runoff model using MODIS satellite images

Abstract In this study, the snowmelt runoff model (SRM) was employed to estimate the effect of snow on the surface flow of Aji-Chay basin, northwest Iran. Two calibration techniques were adopted to enhance the calibration. The multi-station calibration (MSC) and single-station calibration (SSC) strategies applied to investigate their effects on the modeling accuracy. The runoff coefficients (cs and cr) were selected as calibration parameters because of their uncertainty in such an extended basin. To determine the most substantial input of the model which is the snow-covered area (SCA) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor imagery, MOD10A2 images were collected with spatial and temporal resolutions of 500 meters and 8 days, respectively. The results show an average of 15% improvement in the model performance in the MSC strategy from the data period of 2008–2012. Also, an appropriate agreement with physical characteristics of the study area could be seen for the calibration parameters. The contribution of snowmelt in the river flow reaches its peak in April and May, then with increasing temperature, the contribution decreased gradually. Furthermore, analysis of parameters indicates that the SRM is sensitive to recession coefficient and runoff coefficients.

Global sensitivity analysis of mechanical properties in hybrid single lap aluminum-CFRP (plain woven) joints based on uncertainty quantification

Though characterized with extraordinary mechanical performance, the application of hybrid joint (HJ) is restricted due to its numerous parameters and complex nonlinearities in design process. Sensitivity analysis can effectively simplify the HJ design by identifying the dominant parameters in the initial design stage. In this work, uncertainty quantification (UQ) tool was adopted to comprehensively analyze the global sensitivity of the mechanical properties in hybrid (bonded/riveted) single lap aluminum-CFRP joints. A three-dimensional finite element model considering material failure was established and verified by experiments. Besides multiple experimentally quantified uncertainty sources arising from material, geometry and assembly were introduced in the mechanical performance simulation. Meanwhile, the relative contribution of different joint parameters to load sharing, stiffness and peak load was quantified by a high-efficient stochastic sensitivity analysis strategy based on polynomial chaos expansion (PCE) and Sobol decomposition. Then the key parameters affecting the stochastic mechanical properties were identified. Finally, the physical meaning of the sensitivity analysis result was explained. Results show that the proposed method can efficiently and accurately determine the important parameters in the HJ design, which can be applied to improve the design efficiency and quality of HJs.

Eigenvalue sensitivity analysis based on the transfer matrix method

AbstractFor linear mechanical systems, the transfer matrix method is one of the most efficient modeling and analysis methods. However, in contrast to classical modeling strategies, the final eigenvalue problem is based on a matrix which is a highly nonlinear function of the eigenvalues. Therefore, classical strategies for sensitivity analysis of eigenvalues w.r.t. system parameters cannot be applied. The paper develops two specific strategies for this situation, a direct differentiation strategy and an adjoint variable method, where especially the latter is easy to use and applicable to arbitrarily complex chain or branched multibody systems. Like the system analysis itself, it is able to break down the sensitivity analysis of the overall system to analytically determinable derivatives of element transfer matrices and recursive formula which can be applied along the transfer path of the topology figure. Several examples of different complexity validate the proposed approach by comparing results to analytical calculations and numerical differentiation. The obtained procedure may support gradient‐based optimization and robust design by delivering exact sensitivities.

Statistical Models for Composite Endpoints of Death and Nonfatal Events: A Review

The proper analysis of composite endpoints consisting of both death and nonfatal events is an intriguing and sometimes contentious topic. The current practice of analyzing time to the first event often draws criticisms for ignoring the unequal importance between component events and for leaving recurrent-event data unused. Novel methods that address these limitations have recently been proposed. To compare the novel vs. traditional approaches, we review three typical models for composite endpoints based on time to the first event, composite event process, and pairwise hierarchical comparisons. The pros and cons of these models are discussed with reference to the relevant regulatory guidelines, such as the recently released ICH-E9(R1) Addendum “Estimands and Sensitivity Analysis in Clinical Trials.” We also discuss the impact of censoring when the model assumptions are violated and explore sensitivity analysis strategies. Simulation studies are conducted to assess the performance of the reviewed methods under different settings. As demonstration, we use publicly available R-packages to analyze real data from a major cardiovascular trial.