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Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

INTRODUCTION: The ICH E9 addendum outlining the estimand framework for clinical trials was published in 2019 but provides limited guidance around how to handle intercurrent events for non-inferiority studies. Once an estimand is defined, it is also unclear how to deal with missing values using principled analyses for non-inferiority studies. METHODS: Using a tuberculosis clinical trial as a case study, we propose a primary estimand, and an additional estimand suitable for non-inferiority studies. For estimation, multiple imputation methods that align with the estimands for both primary and sensitivity analysis are proposed. We demonstrate estimation methods using the twofold fully conditional specification multiple imputation algorithm and then extend and use reference-based multiple imputation for a binary outcome to target the relevant estimands, proposing sensitivity analyses under each. We compare the results from using these multiple imputation methods with those from the original study. RESULTS: Consistent with the ICH E9 addendum, estimands can be constructed for a non-inferiority trial which improves on the per-protocol/intention-to-treat-type analysis population previously advocated, involving respectively a hypothetical or treatment policy strategy to handle relevant intercurrent events. Results from using the twofold multiple imputation approach to estimate the primary hypothetical estimand, and using reference-based methods for an additional treatment policy estimand, including sensitivity analyses to handle the missing data, were consistent with the original studys reported per-protocol and intention-to-treat analysis in failing to demonstrate non-inferiority. CONCLUSIONS: Using carefully constructed estimands and appropriate primary and sensitivity estimators, using all the information available, results in a more principled and statistically rigorous approach to analysis. Doing so provides an accurate interpretation of the estimand.

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained. [ABSTRACT FROM AUTHOR]

Objective: To investigate the causal effect of immune cells on endometriosis (EMS), we performed a Mendelian randomization analysis. Methods: Mendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effects of exposures on outcomes in observational data. In this study, we conducted a thorough two-sample MR analysis to investigate the causal relationship between 731 immune cells and endometriosis. We used complementary Mendelian randomization (MR) methods, including weighted median estimator (WME) and inverse variance weighted (IVW), and performed sensitivity analyses to assess the robustness of our results. Results: Four immune phenotypes have been found to be significantly associated with the risk of developing EMS: B cell %lymphocyte (WME: OR: 1.074, p = 0.027 and IVW: OR: 1.058, p = 0.008), CD14 on Mo MDSC (WME: OR: 1.056, p =0.021 and IVW: OR: 1.047, p = 0.021), CD14+ CD16− monocyte %monocyte (WME: OR: 0.947, p = 0.024 and IVW: OR: 0.958, p = 0.011), CD25 on unsw mem (WME: OR: 1.055, p = 0.030 and IVW: OR: 1.048, p = 0.003). Sensitivity analyses confirmed the main findings, demonstrating consistency across analyses. Conclusions: Our MR analysis provides compelling evidence for a direct causal link between immune cells and EMS, thereby advancing our understanding of the disease. It also provides new avenues and opportunities for the development of immunomodulatory therapeutic strategies in the future. [ABSTRACT FROM AUTHOR]

In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation. [ABSTRACT FROM AUTHOR]

In a previous study, we assessed the efficiency of reducing either traffic or industrial emissions on various ozone metrics for several cities in Europe, based on the Air Control Toolbox surrogate model. Here, we perform various model parametrisation sensitivity analyses in order to assess the robustness of our results. We find that increasing the model resolution has a limited impact on the ozone response to emission changes when focusing on concentration peaks but strongly changes the response of the ozone daily mean with a switch to a titration regime for all zones with significant nitrogen oxide (NOx) emissions. The impact of pollution imported from outside the simulation domain was also studied and we show that if the first lever for action on ozone peaks remains as the reduction of local and regional emissions, in order to achieve higher levels of reduction, it is necessary to act at a European level. We also explore more up-to-date temporal profiles and sectoral emission speciation and find a shift towards a more NOx-limited regime in a number of cities. Overall, these sensitivity tests show that most of the differences are simulated in cities with high NOx emissions and little solar radiation but do not change the overall conclusions that were previously obtained. [ABSTRACT FROM AUTHOR]

Shale wettability determines shale gas productivities and gas (H 2 , CH 4 and CO 2) geo-storage efficiencies. However, shale wettability is a complex parameter which depends on multiple influencing factors, thus very time-consuming and costly to measure experimentally. Herein, we combined the eXtreme gradient boosting (XGBoost) and Shapley additive explanation (SHAP) machine learning methods to accurately predict brine advancing (θ A) and receding (θ R) contact angles and estimate shale wettability. The XGBoost model demonstrated much higher prediction accuracies than the commonly-used multiple linear regression and partial least squares regression models, e.g., R 2 was 0.946–0.999, 0.794–0.821, and 0.635–0.674, respectively for these three models. The SHAP sensitivity analyses showed that total organic carbon content and gas molecular weight (M G) were the two most significant factors influencing shale wettability. In addition, shale hydrophobicity positively correlated with M G , calcite content, pressure and brine ionic strength, while negatively correlated with temperature and quartz content. This work provides an efficient approach for shale wettability estimation, thus aiding in the implementation of improved gas recovery and gas geo-storage processes, to further guarantee energy security and mitigate climate change. • Shale wettability was accurately predicted via the eXtreme gradient boosting (XGBoost) method. • The two most dominant factors influencing shale wettability were total organic carbon content and gas molecular weight (M G). • Shale hydrophobicity increased with M G , calcite content and pressure, while decreased with temperature and quartz content. [ABSTRACT FROM AUTHOR]

Surging glaciers are glaciers that experience rapidly accelerated glacier flow over a comparatively short period of time. Though relatively rare worldwide, Alaska is home to the largest number of surge‐type glaciers globally. However, their impact on the broader socioecological system in the state is both poorly understood and under‐researched, which poses a challenge in developing appropriate sustainability decisions in Alaska. We investigated how the surge patterns of the Bering Glacier in Alaska have potentially devastating effects on the local ecological biodiversity of its watershed via a structured decision‐making analysis of the different possible consequences. Specifically, this analysis was conducted to explore the various outcomes of a Bering Glacier surge particularly if humans have an increased presence near the glacier due to the area potentially becoming a state park. This work explored the benefits of applying a risk and decision analytical framework in a cryosphere context, to better understand the socioeconomic impact of glacier surges. This is a novel approach in which a decision analysis tool was used to better understand an environmental sustainability challenge, offering an innovative method to support the achievement of the United Nations Sustainability Development Goals in Alaska. We therefore emphasise the need for integrated biophysical and socioeconomic analyses when it comes to understanding glacier hazards. Our research highlights the importance of understanding and researching biophysical changes as well as using a structured decision‐making process for complicated hazard planning scenarios, exemplified via glaciated regions in Alaska, in order to create adaptation strategies that are sustainable and encompass the range of possible outcomes. [ABSTRACT FROM AUTHOR]

This study presents the development of a decision support system that focuses on predicting the endpoint temperature of molten steel to manage the process of an electric arc furnace more systematically. The decision support system leverages a data‐driven approach that consists of the following modules: 1) a data preprocessing module that specifically includes raw data filtering, feature engineering, and outlier detection; 2) a feature selection module based on domain knowledge; 3) regression modeling module that employs a supervised learning algorithm to forecast an endpoint temperature; and 4) sensitivity analysis module to identify the correlation between input and output metric. The applicability of the system is demonstrated through a validation study using real‐world operational data from Hyundai Steel located in Pohang, South Korea. The validation results show that the endpoint temperatures predicted by the system are evenly scattered to a perfect‐fit line within 5% errors of the actual temperatures. The results also indicate that CaO, power, and melting score have the most significant impact on the endpoint temperature, in which temperature decreases as CaO increases and increases as the power and melting score increase. [ABSTRACT FROM AUTHOR]

BackgroundIn the N-MOmentum trial, the risk of an adjudicated neuromyelitis optica spectrum disorder (NMOSD) attack was significantly reduced with inebilizumab compared with placebo.ObjectiveTo demonstrate the robustness of this finding, using pre-specified sensitivity and subgroup analyses.MethodsN-MOmentum is a prospective, randomized, placebo-controlled, double-masked trial of inebilizumab, an anti-CD19 monoclonal B-cell-depleting antibody, in patients with NMOSD. Pre-planned and post hoc analyses were performed to evaluate the primary endpoint across a range of attack definitions and demographic groups, as well as key secondary endpoints.ResultsIn the N-MOmentum trial (ClinicalTrials.gov: NCT02200770), 174 participants received inebilizumab and 56 received placebo. Attack risk for inebilizumab versus placebo was consistently and significantly reduced, regardless of attack definition, type of attack, baseline disability, ethnicity, treatment history, or disease course (all with hazard ratios < 0.4 favoring inebilizumab, p < 0.05). Analyses of secondary endpoints showed similar trends.ConclusionN-MOmentum demonstrated that inebilizumab provides a robust reduction in the risk of NMOSD attacks regardless of attack evaluation method, attack type, patient demographics, or previous therapy.The N-MOmentum study is registered at ClinicalTrials.gov: NCT2200770.

Long-term transmission expansion planning has to face the energy transition in a restructured electricity market environment. Increased transmission capacity within and between Member States is likely to play an essential role in maintaining the secure and economic operation of the whole European power system and ensuring the integration of growing renewable generation. This paper proposes a novel iterative methodology aimed at assessing an optimal level of interconnection between relevant bidding zones, simultaneously investigating different potential alternatives. Starting from a reference grid, a multi-criteria analysis is adopted to select the additional transmission capacities to be tested in each iteration via network and market simulations in order to confirm that transmission expansion benefits outweigh the estimated realization costs. The proposed approach is applied to the Italian case in two contrasting energy scenarios for the mid-term 2030 and very-long-term 2040 horizons: different development strategies are derived, and the least regret criterion is applied to define the most cost-effective as the target development strategy for the Transmission System Operator (TSO). Furthermore, sensitivity analyses on relevant input data variation are performed to test the robustness of the results obtained.

In a previous study, we assessed the efficiency of reducing either traffic or industrial emissions on various ozone metrics for several cities in Europe, based on the Air Control Toolbox surrogate model. Here, we perform various model parametrisation sensitivity analyses in order to assess the robustness of our results. We find that increasing the model resolution has a limited impact on the ozone response to emission changes when focusing on concentration peaks but strongly changes the response of the ozone daily mean with a switch to a titration regime for all zones with significant nitrogen oxide (NOx) emissions. The impact of pollution imported from outside the simulation domain was also studied and we show that if the first lever for action on ozone peaks remains as the reduction of local and regional emissions, in order to achieve higher levels of reduction, it is necessary to act at a European level. We also explore more up-to-date temporal profiles and sectoral emission speciation and find a shift towards a more NOx-limited regime in a number of cities. Overall, these sensitivity tests show that most of the differences are simulated in cities with high NOx emissions and little solar radiation but do not change the overall conclusions that were previously obtained.

To study the lane-change interaction characteristics of intelligent connected vehicles (ICVs) and reduce the risk of vehicle lane-changing decisions, a decision model based on the lane-changing game characteristics of the ICV is proposed in this paper. In the modeling process, the characteristics of vehicle lane-changing interaction behavior are analyzed based on evolutionary game theory and the vehicle game lane-changing payoff functions are quantified. The stability of the game equilibrium points is analyzed by using a dynamic evolution equation, and sensitivity analysis of the main factors affecting vehicle lane changes and the time to the collision of vehicles is conducted. The SUMO software is used to simulate and verify the vehicle game decision model, and the results show that the game decision system converges to different optimal strategy combinations under different traffic conditions, and this model can effectively reduce the decision-making conflict and the collision risk of vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Civil Engineering & Architecture is the property of Journal of Beijing University of Civil Engineering & Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Classification trees are a popular machine learning tool for studying a variety of problems, including prediction, inference, risk factors identification, and risk groups classification. Classification trees are basically developed under the assumption that the response and covariate variables are accurately measured. This condition, however, is often violated in practice. Ignoring this feature commonly yields invalid analysis results. In this paper, we study the impact of mismeasured responses on the performance of standard classification trees and propose a novel classification trees algorithm for mismeasured responses. Our study is directed to settings with binary responses which are subject to mismeasurement. To address the effects of mismeasured responses, we modify the decision rules which are valid for tree building in the mismeasurement-free settings by introducing new measures for the node impurity and misclassification cost. To characterize the magnitude of mismeasurement in responses, we consider two data scenarios. In the first scenario, the mismeasurement rates are known, either from previous studies of the same nature or being set by researchers who are interested in conducting sensitivity analyses to assess the impact of mismeasured responses. In the second scenario, the mismeasurement rates are unknown and are estimated from a validation dataset which contains both accurate measurements and mismeasurements for responses. We conduct a variety of simulation studies to assess the performance of the proposed classification trees algorithm, in comparison to the usual classification trees algorithms which ignore response mismeasurement. It is demonstrated that ignoring response mismeasurement can yield seriously erroneous results and that our proposed method provides superior performance with the mismeasurement effects accommodated. To illustrate the usage of the proposed method, we analyze the data arising from the National Health and Nutrition Examination Surveys (NHANES) by conducting sensitivity analyses to assess how classification results may be affected by different misclassification costs. [ABSTRACT FROM AUTHOR]

This article examines pathways among farmers' extension participation, the uptake of recommended farm management practices and economic and environmental sustainability. We explore the 'win-win', efficiency-based focus of the Irish hybrid extension programme using an unbalanced panel dataset of dairy farms from 2010 to 2019. We apply two-way fixed effects regression models and sensitivity analyses to ensure the robustness of our results to effect heterogeneity and omitted variable bias. Our findings reveal that extension participation has a limited association with the adoption of recommended practices. These practices might be associated with economic benefits, while their environmental effects are not evident. Additionally, extension participation is not found to have a direct association with sustainability outcomes. These findings have important implications for extension programmes that focus on economic and environmental outcomes. [ABSTRACT FROM AUTHOR]

Floods have received global significance in contemporary times due to their destructive behavior, which may wreak tremendous ruin on infrastructure and civilization. The present research employed an integration of the Geographic information system (GIS) and Analytical Hierarchy Process (AHP) method for identifying the flood susceptibility zonation (FSZ), flood vulnerability zonation (FVZ), and flood risk zonation (FRZ) of the humid subtropical Uttar Dinajpur district in India. The study combined a large number of thematic layers (N = 12 for FSZ and N = 9 for FVZ) to achieve reliable accuracy and included the multicollinearity analysis of these variables to overcome the issues related to highly correlated variables. According to the findings, 27.04, 15.62, and 4.59% of the area were classified as medium, high, and very high FRZ, respectively. The ROC-AUC, MAE, MSE, and RMSE of the model exhibited a good prediction accuracy of 0.73, 0.15, 0.16, and 0.21, respectively. The performance of the AHP model has been evaluated using sensitivity analyses. It also highly recommends that persistent improvement in this subject, such as sensitivity studies on modifying criteria thresholds, changing the relative significance of criteria, and changing the desired matrix, will permit GIS and MCDA to be progressively adapted to real hazard-management issues.

Ignition delay time measurements in high-temperature propane-air mixtures have been performed behind reflected shock wave at temperatures T = 1043 ÷ 1778 K, pressures p = 17.7 ÷ 42.8 atm, and equivalence ratios φ = 0.5, 1.0, and 2.0. Simultaneously, the emission characteristics of the flammable mixture associated with the emission of electronically excited OH* radical were recorded. The available detailed kinetic mechanisms of propane ignition were used to describe the obtained experimental data. Sensitivity analyses of the propane-air mixtures with various equivalence ratios was carried out to single out the key reactions proceeded in these mixtures. The data obtained make it possible to specify the available information on the ignition of combustible mixtures and thereby to improve the understanding of the processes occurring in the combustion chambers. • New experimental data on the ignition delay times in propane-air mixture. • Different detailed kinetic mechanisms of propane ignition. • Understanding of the processes occurring in the combustion chambers of jet engines. [ABSTRACT FROM AUTHOR]

M-X have emanated as a potential material for numerous mechanical uses ascribed to their exceptional physicochemical features, multilayered architectures, superior strength, and electrical conductivity along with their flexibility. The mechanical features of M-X polymeric nanoarchitectures are critical to their application in advanced engineering structures and include Young's modulus, bending rigidity, elasticity, sliding resistance, adhesion, ideal strengths, theoretical and experimental affiliations. Furthermore, the deformation along with failure mechanism of materials are essential in engineering applications. Engineering finite elemental models as well as prediction of M-X nanosheets mechanical behavior are imperative in understanding the mechanical behavior of M-X/polymeric nanoarchitectures. Therefore, this paper elucidates the mechanical, deformation, and failure mechanism of M-X and M-X polymeric nanoarchitectures with special emphasis on models utilized in predicting the mechanical behaviors of these materials. [ABSTRACT FROM AUTHOR]

This work aims to study the parameters affecting a global slope failure in an open pit mine. The event was a complex failure, formed by an upper layer of a weathered rock, which behaved like a soil mass, above two faults, which intersect to form a wedge. This upper soil layer has pressured the wedge leading to its failure. It occurred in the northern sector of the pit in 2013, causing the interruption of mining activity for about 1 year. The geomechanical parameters were defined from an extensive field survey database; lab tests and Schmidt hammer tests were performed in the region of the failure. The Hoek-Brown criterion was adopted for the rock mass; Barton-Bandis for the discontinuities and Mohr-Coulomb for the weathered rocks. Three-dimensional limit equilibrium methods were applied using the average strength parameters. The factor of safety (F.S.) was 1.33, a situation which indicated relative stability. Thus, sensitivity analyses were performed to define the parameters that conditioned the failure, by progressively reducing the discontinuity shear strength via percentage factors, until F.S reached 1.0. Failure surface geometry obtained in the analysis was close to the failure observed at the field. The susceptibility condition of the wedge failure was evaluated through kinematic analyses in two scenarios; F.S = 1.33 and F.S. = 1. The results indicated a significant increase in the probability of kinematic feasibility when F.S. was 1. The reduction of the slope dip should ensure a stable condition. Moreover, the water level should be kept far from the slope face. [ABSTRACT FROM AUTHOR]

The simple shear response of air-fall volcanic (pyroclastic) soils under both saturated and unsaturated conditions is interpreted through an elastoplastic constitutive model with hydraulic-hardening and porosity-dependent critical state. Extensive experimental data collected under various testing protocols (i.e., simple shear, direct shear, and triaxial tests) enable the theoretical identification of the variability of the fundamental physical properties (i.e., frictional resistance, dilatancy, and water retention behavior) and the identification of a band of admissible values for prescribed confidence levels. In this paper, a constitutive model specifically developed to account for the simple shear loading is adopted and the calibration of the model parameters is performed accounting for such data scatter. The calibration procedure led to a single set of constitutive parameters through 3 main steps. The identification of range of variation for each parameter is carried out using the data derived from laboratory tests. Then, sensitivity analyses are performed on the main physical and constitutive parameters. Mathematical indicators quantifying the difference between the measurements and the predictions are proposed to investigate the role of the material properties and evaluate the model performance. Furthermore, an optimization algorithm is adopted to identify the optimal set of model parameters which best fits all the considered tests (i.e., simple shear tests under saturated and unsaturated conditions and wetting tests). The results imply that the variability of the hydro-mechanical properties must be considered to satisfactorily simulate the constitutive behaviors of the volcanic soils under a variety of simple shear testing regimes. [ABSTRACT FROM AUTHOR]

Rice is a strategic commodity in the food chain for the people and governments. It is a fundamental food for many societies. Moreover, producing rice can provide a reliable source of revenue if proper supply chain management is coordinated by farmer countries. The rice supply chain includes diverse elements such as farms, rice mills, distribution centers, and markets. This study examines the important factors that play a significant role in the rice supply chain. A bi-objective mathematical model is formulated to minimize total costs as an economic goal and minimize soil erosion and its destruction due to the consumed water for rice cultivation as an environmental goal. To verify the viability of the proposed model, a case study of the rice supply chain with limited producer farms has been investigated. Moreover, some parameters such as annual precipitation in production areas along with other factors are presented under several scenarios. Furthermore, an extended goal programming approach and stochastic programming are utilized to solve the proposed model. Finally, the sensitivity analyses of the important parameters have been performed. [ABSTRACT FROM AUTHOR]

This article focuses on the recent epidemic caused by COVID-19 and takes into account several measures that have been taken by governments, including complete closure, media coverage, and attention to public hygiene. It is well known that mathematical models in epidemiology have helped determine the best strategies for disease control. This motivates us to construct a fractional mathematical model that includes quarantine categories as well as government sanctions. In this article, we prove the existence and uniqueness of positive bounded solutions for the suggested model. Also, we investigate the stability of the disease-free and endemic equilibriums by using the basic reproduction number (BRN). Moreover, we investigate the stability of the considering model in the sense of Ulam–Hyers criteria. To underpin and demonstrate this study, we provide a numerical simulation, whose results are consistent with the analysis presented in this article.

Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses (CAMSA) App implements sensitivity analysis for LGCMM models to evaluate if a mediating path (mechanism) is robust to potential confounding variables. The CAMSA approach is described and applied to simulated data, and data from a research study exploring a mechanism of change in the treatment of substance use disorder. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a regionally downscaled economic model developed to assess the impacts of the management of dredged sediments on Gross Domestic Product (GDP) and jobs created; the model is validated and applied using real project data from sediment management projects in Ireland and Scotland. The model provides significant insight into and allows impact analysis for the economic aspect of sediment management projects with the potential to facilitate and inform stakeholders across the sediment management sector. Methods: The economic model facilitates regional analysis of the impacts of sediment management projects on GDP and job creation for direct, indirect and induced effects. Methods for estimating the economic induced impacts are based on industry-specific type I and type II economic multipliers and coefficients, derived for the EU Interreg SURICATES partner countries (Ireland, Scotland, France and the Netherlands) using symmetric input–output tables and application of the open Leontief model and based on available economic data for the identified countries. The model is applied to sediment management projects in Ireland (a harbour development project at Castletownbere) and in Scotland (a bioremediation project at Falkirk). Model results are compared to project data for direct contribution to GDP and direct jobs created, and the model also estimates the indirect and induced economic project impacts. The model has been applied to undertake sensitivity analyses and compare different sediment management options. Results: Model results provide a satisfactory comparison to real project data for direct cost and jobs created. Indirect economic benefits for GDP and employment created were estimated from 47 to 53% of direct impacts. The model has been applied to undertake sensitivity analyses and assess a range of different site-specific sediment management options with indirect economic impacts ranging from 42 to 53% of direct impacts. Conclusions: The economic model results are compared to real project economic data, the validation exercise proving satisfactory with promising results. Sensitivity analyses and site-specific sediment management options have been assessed. The positive economic impacts of the Castletownbere Harbour project in particular are evident. These results highlight the potentially different economic impacts of the implementation of different sediment management options and in different regions and countries. The model allows the quantification of the economic benefits of sediment management projects. The model provides significant insight into and allows impact analysis for the economic aspect of sediment management projects and has the potential to facilitate and inform stakeholders and decision-makers across the sector. [ABSTRACT FROM AUTHOR]

Mitigating climate change requires a variety of energy technologies and energy simulation approaches to evaluate the best possible system structures. Screening whether novel technologies are a viable solution for a particular country within a cost-optimised system setup is usually simulation- and time-intensive. This study introduces the novel add-on optimisation tool EP-ALISON-LUT for use in combination with EnergyPLAN applied to the test case of wave power in the case of Seychelles in 2030 and 2050 within a structured sensitivity analysis. The tool enables a high number of possible system setups and scenarios, including the import and domestic production of electricity-based fuels, to be modelled, allowing for an in-depth view of the system impacts of integrating wave power. The results indicate a limited role for wave power due to its relatively low yield, especially in 2030. However, in 2050, up to 500 MW of wave power capacity is possible with a lower or similar levelised cost of final energy compared to the reference scenario in 2019, which can benefit the diversification of the power generation portfolio. Thus, this novel tool is fast and effective in technology screening studies requiring a fast optimisation algorithm. • Viability screening for novel renewable energy technologies is needed. • Novel optimisation tool based on and in combination with EnergyPLAN is presented. • Structured sensitivity analysis of wave power on the case of Seychelles is made. • Wave power can hardly challenge low-cost solar photovoltaics in the Sunbelt region. • Structured sensitivity analysis showed options for portfolio diversification. [ABSTRACT FROM AUTHOR]

The development of demountable shear connectors for composite beams is receiving increasing attention, aiming at facilitating the disassembly and reuse of structural components to promote sustainability. The lack of classification methods and standardized assessment criteria to assess both the constructability and mechanical performance of various types of connectors would lead to the impediment of practical applications. In this study, a comprehensive literature review was conducted, the mathematical method of multi-attribute weight was employed to quantitatively assess the mechanical, constructional, manufacturing, and economic performance of various demountable shear connectors and finally, the evaluation method was proposed. The sensitivity analyses were carried out to further evaluate the applicability and reliability of the proposed evaluation method for various engineering scenarios. The results demonstrated that the proposed evaluation method could provide a reliable evaluation, and the method is expected to enable decision-makers to identify the advantages and limitations of each connector type, offering a valuable tool for structural design in practice. • The comprehensive literature review of demountable shear connectors was conducted. • A multi-attribute weight method of demountable shear connectors was adopted. • The sensitivity analyses for demountable shear connectors were performed. [ABSTRACT FROM AUTHOR]

• Patient-specific fluid dynamics models of drug transport can predict tumor dose. • Since sensitivity analysis of these models are costly, surrogate models are used. • Systolic duration is an important waveform shape parameter influencing tumor dose. • Our algorithm allows feasibly cheap sensitivity analysis with surrogate models. • Our algorithm is ideally fit to use on novel, complex problems. Patient-specific 3D computational fluid dynamics (CFD) models are increasingly being used to understand and predict transarterial radioembolization procedures used for hepatocellular carcinoma treatment. While sensitivity analyses of these CFD models can help to determine the most impactful input parameters, such analyses are computationally costly. Therefore, we aim to use surrogate modelling to allow relatively cheap sensitivity analysis. As an example, we compute Sobol's sensitivity indices for three input waveform shape parameters. We extracted three characteristic shape parameters from our input mass flow rate waveform (peak systolic mass flow rate, heart rate, systolic duration) and defined our 3D input parameter space by varying these parameters within 75 %-125 % of their nominal values. To fit our surrogate model with a minimal number of costly CFD simulations, we developed an adaptive design of experiments (ADOE) algorithm. The ADOE uses 100 Latin hypercube sampled points in 3D input space to define the initial design of experiments (DOE). Subsequently, we re-sample input space with 10,000 Latin Hypercube sampled points and cheaply estimate the outputs using the surrogate model. In each of 27 equivolume bins which divide our input space, we determine the most uncertain prediction of the 10,000 points, compute the true outputs using CFD, and add these points to the DOE. For each ADOE iteration, we calculate Sobol's sensitivity indices, and we continue to add batches of 27 samples to the DOE until the Sobol indices have stabilized. We tested our ADOE algorithm on the Ishigami function and showed that we can reliably obtain Sobol's indices with an absolute error <0.1. Applying ADOE to our waveform sensitivity problem, we found that the first-order sensitivity indices were 0.0550, 0.0191 and 0.407 for the peak systolic mass flow rate, heart rate, and the systolic duration, respectively. Although the current study was an illustrative case, the ADOE allows reliable sensitivity analysis with a limited number of complex model evaluations, and performs well even when the optimal DOE size is a priori unknown. This enables us to identify the highest-impact input parameters of our model, and other novel, costly models in the future. [ABSTRACT FROM AUTHOR]

To study the lane-change interaction characteristics of intelligent connected vehicles (ICVs) and reduce the risk of vehicle lane-changing decisions, a decision model based on the lane-changing game characteristics of the ICV is proposed in this paper. In the modeling process, the characteristics of vehicle lane-changing interaction behavior are analyzed based on evolutionary game theory and the vehicle game lane-changing payoff functions are quantified. The stability of the game equilibrium points is analyzed by using a dynamic evolution equation, and sensitivity analysis of the main factors affecting vehicle lane changes and the time to the collision of vehicles is conducted. The SUMO software is used to simulate and verify the vehicle game decision model, and the results show that the game decision system converges to different optimal strategy combinations under different traffic conditions, and this model can effectively reduce the decision-making conflict and the collision risk of vehicles.

Subsurface hydrogen storage in depleted hydrocarbon reservoirs and saline formations is a potential option for storing hydrogen at large scales. These subsurface formations need to store sufficient hydrogen efficiently and securely, and the hydrogen must be withdrawn in adequate quantities on demand. In this study, we investigate the reservoir, geological, and operational controls that enable large-scale hydrogen storage and maximize hydrogen injection and withdrawal from depleted natural gas reservoirs. Hydrogen injection, storage, and withdrawal scenarios were computed using a reservoir simulator. Sensitivity analyses exposed the crucial parameters to achieve the goal of optimum storage and withdrawal of hydrogen. We determined that reservoirs with smaller pressures at the start of storage operations are suitable for hydrogen storage if wellhead pressure constraints permit. Steeply dipping reservoirs enable better hydrogen withdrawal if the reservoirs have good permeability (greater than 100 mD) and the injection/withdrawal well is placed updip within the reservoir. Permeable reservoirs and reservoirs with sufficient thickness increase hydrogen withdrawal rates. These findings and the results of the sensitivity analyses are used to propose site selection criteria for underground storage of hydrogen in depleted gas reservoirs. • Reservoir and operational parameters that enable large-scale hydrogen storage in depleted gas reservoirs are investigated. • Reservoir simulation and sensitivity analyses provide insights into hydrogen dynamics in depleted gas reservoirs. • The top factors impacting hydrogen withdrawal are reservoir depth, dip, current pressure, and flow capacity. • Hydrogen recovery per cycle depends on reservoir structure, properties, and management practices. • Novel screening and scoring criteria are developed to select potential porous media underground hydrogen storage sites. [ABSTRACT FROM AUTHOR]

Mendelian randomisation (MR) is an increasingly popular method for strengthening causal inference in epidemiological studies. cis-MR in particular uses genetic variants in the gene region of a drug target protein as an instrumental variable to provide quasi-experimental evidence for on-target drug effects. A limitation of this framework is when the genetic variant is correlated to another variant that also effects the outcome of interest (confounding through linkage disequilibrium). Methods for correcting this bias, such as multivariable MR, struggle in a cis setting because of the high correlation among genetic variants. Here, through simulation experiments and an applied example considering the effect of interleukin 6 receptor signaling on coronary artery disease risk, we present an alternative method for attenuating bias that does not suffer from this problem. As our method uses both MR and the product and difference method for mediation analysis, our proposal inherits all assumptions of these methods. We have additionally developed an R package, TwoStepCisMR, to facilitate the implementation of the method. [ABSTRACT FROM AUTHOR]

The ICH E9(R1) addendum on Estimands and Sensitivity Analyses in Clinical Trials has introduced a new estimand framework for the design, conduct, analysis, and interpretation of clinical trials. We share Pharmaceutical Industry experiences of implementing the estimand framework in the first two years since the final guidance became available with key lessons learned and highlight what else needs to be done to continue the journey in embedding the estimand framework in clinical trials. Emerging best practices and points to consider on strategies for implementing a new estimand thinking process are provided. Whilst much of the focus of implementing ICH E9(R1) to date has been on defining estimands, we highlight some of the important aspects relating to the choice of statistical analysis methods and sensitivity analyses to ensure estimands can be estimated robustly with minimal bias. In particular, we discuss the implications if complete follow-up is not possible when the treatment policy strategy is being used to handle intercurrent events. ICH E9(R1) was introduced just before the start of the COVID-19 pandemic, but a positive outcome from the pandemic has been an acceleration in the adoption of the estimand framework, including differentiating intercurrent events related or not related to the pandemic. In summary, much has been learned on the estimand journey and continued sharing of case studies will help to further advance the understanding and increase awareness across all clinical researchers of the estimand framework. [ABSTRACT FROM AUTHOR]

Due to the uncertainties of determining stable, efficient, and safe operating conditions of methane-air gas turbines and the complexity of involving multivariable parameters, the task of controlling and analyzing methane-gas turbines remains a challenge despite the existing methods of control and analyses in the literature. In fact, as far as the methane-air gas turbines are concerned, there is still much room for improvement within the criterion of (computation effort, settle time, its capacity to adjust with a variable range of set-points, as well as its stable, steady, and dynamic outputs can all be enhanced). Furthermore, an accurate system-identification phase has been adopted in this paper to initiate the process of advancing an efficient gas turbine controller by determining the system's sensitivities towards the involved multivariable parameters.Therefore, this paper presents a control and analysis tool for methane-air gas turbines. This tool is a ‘light code (the time to reach the desired set-point (i.e., settle period reaches 1 s)) to serve as a methane-air gas turbine controller that can be used for experimentation or applied on an industrial scale. Moreover, the code is designed with a user interface that enables the simulations and system identification for the methane-fueled gas turbine, thus; ensuring that the governing parameters are calibrated precisely. The code was created using LabVIEW and GASEQ and is driven by conventional gas turbine cycle assessments. A simulation case study has been utilized as a system identification phase to determine the system's sensitivity of temperatures and works at the three primary stages of a simple cycle methane-air gas turbine (compressor, combustion chamber, and turbine) toward the multivariable input parameters of the compression stage (compressor pressure ratio, efficiency, inlet pressure, and temperature), the combustion phase (methane, airflow rates) and the expansion phase (turbine ratio of pressures and isentropic efficiency). Based on the sensitivity analysis performed through the developed code, the turbine output temperature is shown to be very sensitive to the turbine's parameters (P04/P03 and εt). This essentially emphasizes the possibility of including waste heat utilization mechanisms (i.e., heat exchangers, combined cycles) for turbines with high P04/P03 or/and low εt, resulting in a high turbine outlet temperature.Based on the performed system identification phase and sensitivity analyses, the intervals of the Proportional controller gain (Kc), Integral time (ti), and Derivative time (td) have been identified to guarantee a settling period (Ts) within [1 s-60 s] to achieve the desired temperature set-point which eliminates turbine overheating.

A methanol–steam reformer (MSR) can safely provide hydrogen-rich fuel for a fuel cell system. Since the operating temperature of an MSR is relatively low, convective heat transfer is typically used to provide thermal energy to the endothermic reactions in the MSR. In this study, the use of phase change heat transfer to provide thermal energy to the endothermic reactions was investigated, which enhanced the temperature uniformity longitudinally along the MSR. ANSYS Fluent® software was used to investigate the performance of the reforming reactions. A comparative analysis using sensible heat and latent heat as the heat supply sources was performed. Using latent heat as a heat source achieved a lesser temperature drop than sensible heat that was under 5.29 K in the outer pipe. Moreover, a sensitivity analysis of methanol–steam-reforming reactions that use phase change heat transfer in terms of the carbon ratio, gas hourly velocity (for the inner and outer pipes of the MSR), inlet temperature (inner and outer pipes), reactor length, and operating pressure (inner pipe) was performed. When the phase change energy of water vapor is used, the wall temperature of the MSR is conveniently controlled and is uniformly distributed along the channel (standard deviation: 0.81 K). Accordingly, the methanol conversion rate of an MSR that uses phase change energy is ~4% higher than that of an MSR that employs convective heat transfer. [ABSTRACT FROM AUTHOR]

Economic values for annual milk yield (MY, kg), annual fat yield (FY, kg), annual protein yield (PY, kg), age at first calving (AFC, days), number of services per conception (NSC), calving interval (CI, days) and mastitis episodes (MS) were derived for temperate dairy cattle breeds in tropical Sri Lanka using a bio‐economic model. Economic values were calculated on a per cow per year basis. Derived economic values in rupees (LKR) for MY, FY and PY were 107, −162 and −15, while for AFC, NSC, CI and MS, economic values were −59, −270, −84 and −8,303. Economic values for FY and PY further decreased with higher feed prices, and a less negative economic value for FY was obtained with increased price for fat. Negative economic values for FY and PY show that genetic improvement for these traits is not economical due to the high feed costs and/or the insufficient payment for fat and protein. Therefore, revision of milk fat and protein payments is recommended. Furthermore, the breeding objective developed in this study was dominated by milk production and fertility traits. Adaptability and functional traits that are important in a temperate dairy cattle breeding programme in tropical Sri Lanka, such as longevity, feed efficiency, disease resistance and heat tolerance should be recorded to incorporate them in the breeding objective. Continued trait recording of all traits is recommended to ensure dairy cows can be selected more effectively in a tropical environment based on a breeding objective that also includes adaptability and functional traits. [ABSTRACT FROM AUTHOR]

Outcome values in randomized controlled trials (RCTs) may be missing not at random (MNAR), if patients with extreme outcome values are more likely to drop out (eg, due to perceived ineffectiveness of treatment, or adverse effects). In such scenarios, estimates from complete case analysis (CCA) and multiple imputation (MI) will be biased. We investigate the use of the trimmed means (TM) estimator for the case of univariable missingness in one continuous outcome. The TM estimator operates by setting missing values to the most extreme value, and then "trimming" away equal fractions of both groups, estimating the treatment effect using the remaining data. The TM estimator relies on two assumptions, which we term the "strong MNAR" and "location shift" assumptions. We derive formulae for the TM estimator bias resulting from the violation of these assumptions for normally distributed outcomes. We propose an adjusted TM estimator, which relaxes the location shift assumption and detail how our bias formulae can be used to establish the direction of bias of CCA and TM estimates, to inform sensitivity analyses. The TM approach is illustrated in a sensitivity analysis of the CoBalT RCT of cognitive behavioral therapy (CBT) in 469 individuals with 46 months follow‐up. Results were consistent with a beneficial CBT treatment effect, with MI estimates closer to the null and TM estimates further from the null than the CCA estimate. We propose using the TM estimator as a sensitivity analysis for data where extreme outcome value dropout is plausible. [ABSTRACT FROM AUTHOR]

In today's competitive environments, companies need to be cost-effective, environmental-friendly, and social-friendly to deal with several challenges that exist in markets. In this context, reconfigurable manufacturing systems (RMSs) have emerged to fulfil these requirements. RMS is one of the attractive manufacturing paradigms. Machine components, software, or material handling units can be added, removed, modified, or interchanged as needed and imposed by the necessity to react rapidly and cost-effectively to changing. A multi-objective multi-product process and production planning problem in a sustainable reconfigurable manufacturing environment (SRMS) is considered in this paper. Three pillars of sustainability, respectively social, environmental, and economic are formulated and optimised. First, a linear mixed-integer model is proposed. Second, a Lagrangian relaxation-based approach is developed to solve the problem on the large scales, where an exact method is used to solve the problem in small and medium cases with GAMS software. To illustrate the applicability of the proposed approaches, some numerical examples and analyses are presented. Finally, a sensibility study of the problem according to some parameters is performed. [ABSTRACT FROM AUTHOR]

To reasonably investigate the influence of pile-soil interaction on the dynamic reliability of monopile supported offshore wind turbines (OWTs), a framework with high efficiency and good accuracy is proposed for reliability and sensitivity analyses of monopile supported OWTs. This proposed framework combines the grey relational analysis (GRA), the probability density evolution method (PDEM), and the interval mathematical analysis method (IM). Firstly, the GRA is applied to identify the main controlling parameters influencing the stochastic dynamic response of a monopile supported OWT, considering various factors such as pile elasticity modulus, soil internal friction angle and soil unit weight. Secondly, combining the probability density evolution theory, a complete sample probability set is established to obtain the dynamic reliability of the monopile supported OWT under wind and wave loads. Finally, the effect of each controlling parameter on the dynamic reliability of the monopile supported OWT is systematically analyzed by combining the IM and the change of probability measure. The proposed GRA-PDEM-IM-based framework for reliability analyses of monopile supported OWTs can effectively reduce the number of samples for probability density evolution calculations and provide reasonable and accurate reliability analyses results which can serve as a reference for design and construction of monopile supported OWTs. • A framework for reliability sensitivity analysis based on pre-screening of controlling parameters is proposed. • A complete set of sample probabilities is established to obtain the reliability of the offshore wind turbine. • The accuracy of the framework is validated through a comparative analysis of two case conditions. • The effects of dominant parameters on the dynamic reliability of offshore wind turbines are investigated. [ABSTRACT FROM AUTHOR]

Blending ammonia with hydrogen has the potential of replacing conventional hydrocarbon fuels to reduce carbon emissions. However, the uncertainties of determining safe, stable, and efficient operating conditions remain a great challenge. Furthermore, although control technologies have been in the scope of interest for gas turbine manufacturers, those are not yet specialized for NH 3 –H 2 /air gas turbines. Therefore, this paper identifies the cycle performance of an NH 3 –H 2 /air gas turbine in comparison to a CH 4 /air gas turbine, thus highlighting the operating conditions in which the NH 3 –H 2 /air gas turbines show higher performance compared to the CH 4 /air gas turbines. The results have been obtained by developing a LabVIEW code which has also been utilized to serve as a control code for the NH 3 –H 2 /air and CH 4 /air gas turbines. The system identification stage of calibrating the controller has been achieved for both gas turbines by determining the system's sensitivities, thus providing an accurate calibration of the controlling parameters. • Higher NH3–H2/air gas turbines performance compared to CH4/air gas turbines. • System identification, cycle, and sensitivity analyses of NH3–H2/air gas turbines. • Developing and calibrating an NH3–H2/air gas turbines controller. [ABSTRACT FROM AUTHOR]

From regional to continental scales, hydrologic processes are represented by modular modeling frameworks dependent on input datasets and parameter sets representing physical attributes. The hydrologic community needs a common procedure to evaluate model output based on parameter sensitivities and uncertainties compared to performance metrics (i.e., objective functions), especially for large parameter sets. We developed a reproducible workflow for evaluating hydrologic models to objectively analyze model outputs as a function of parameter choice using numerical and visualization techniques. Our workflow was implemented on three separate case studies, each with a different hydrologic model, and the results can be reproduced and visualized from a community github code repository. Model parameter sensitivity was evaluated using several global sensitivity indices and Bayesian theory. Uncertainty in parameter spaces was quantified to highlight the impact of unreliable input data on model output. Model parameter sensitivities and uncertainties were evaluated numerically and visually to provide a comprehensive perspective on their impacts on model output. For each case study, we provided a summary and interpretation of the workflow results. Our workflow can be integrated into hydrologic modeling frameworks for objective modular model and parameter set evaluations based on a data‐driven approach appropriate for model selection. [ABSTRACT FROM AUTHOR]

Piles are widely used to transfer load to the underlying soil layers and reduce settlement. It is difficult to determine the exact bearing capacity (BC) of piles due to the large number of effective parameters. This study combined the PSO and ANN algorithms to provide a polynomial relation for the prediction of bearing capacity in driven piles. Sensitivity analysis examined the effect of the input parameters including flap number (FL), pile length (L) and cross-sectional area (A), internal friction angle (ϕ), soil drained cohesion (C), soil density (γ) and soil–pile interaction friction angle (δ) on the output parameter (BC). This study used the data from 100 static loading tests on the piles. The results of this study showed that the quadratic relation obtained from the PSO–ANN and PSO methods for the prediction of BC yielded the R2 values of 0.912 and 0.957, respectively. The scaling of input data also plays an important role in the ANN performance. The results of sensitivity analyses revealed that γ, A, δ, L, ϕ, C and FL have the greatest effect, respectively. The model presented by PSO–ANN method can be used with a high reliability to predict BC. [ABSTRACT FROM AUTHOR]

Resource scarcity and anthropogenic climate change require the reduction of performance gaps in existing buildings. In addition to unexpected user behavior, performance gaps are primarily caused by the technical gap due to operational errors in building technology. The main objective of this paper is to quantify model input uncertainty incorporating uncertain boundary conditions in terms of operational errors using thermo-dynamic building performance simulations and to identify the most relevant input parameters for the performance gaps in air conditioning systems by means of sensitivity analyses. Model input uncertainty is stochastically determined using Monte-Carlo Simulations to calculate the target values “primary energy demand” as well as “over- and under-temperature degree hours” for an office building. Selected parameters are simulated in a specific uncertainty and sensitivity analyses using the Sobol’ and Jansen estimators, which distinguish between a direct influence on the target variables and interactions between the parameters. The methodology requires a selection process, which is carried out as part of relative uncertainty and relative sensitivity analyses. Furthermore, the operational errors are compared with construction factors as well as building physics inputs and design parameters for building technology systems to show their reciprocal effects as part of a comprehensive investigation. The main findings of this paper are that operational errors in air conditioning systems play an essential role in decreasing energy efficiency and thermal comfort, but do not warrant the significance of certain construction factors as well as setpoints in building technology. Moreover, the impact of operational errors on thermal overheating of the building investigated is minor compared to other targets that cause greater model input uncertainty. [ABSTRACT FROM AUTHOR]

Designing a Static Random-Access Memory (SRAM) cell configuration that copes with conventional complementary metal-oxide-semiconductor (CMOS) constraints on the cell area is desired to satisfy high packing density in integrated digital circuits. This paper analyzes and compares performance parameters of 6T SRAM bit-cells based on the side-contacted field-effect diode (S-FED) and conventional CMOS at 180 nm technology node. Steady-state responses demonstrate that in the worst-case, by applying weak logic 0/1 to the bit-lines, strong logic data is stored in the S-FED-based cell with superior write margin and lower power consumption by about one order of magnitude in comparison with the CMOS-based one. Comparing static noise margin reveals that S-FED-based cells enjoy prominent stability, especially in the read and hold operations with ~3X and 29% improvements, respectively, to the CMOS-based versions. In addition, enhancement of read-stability is attained utilizing S-FED-based cell, as well as decrement of subthreshold current and static power dissipation, compared with the CMOS-based one. Sensitivity analyses extracted from Monte Carlo simulations and butterfly curves indicate that S-FED-based cell successfully tolerates process and supply voltage variations in all operation modes, superior to the CMOS-based counterpart.

Objectives: This study aimed to evaluate the uncertainty related to the use of common collection tools to assess costs in economic evaluations compared with an exhaustive administrative database.Methods: A pragmatic study was performed using preexisting cost-effectiveness studies. Patients were probabilistically matched with themselves in the French National Health Data System (Système National des Données de Santé [SNDS]), and all their reimbursed hospital and ambulatory care data during the study were extracted. Outcomes included the ratio of the number of each type of resources consumed using trial data (case report forms for ambulatory care and local hospital data for hospital care) versus the SNDS and the ratio of corresponding costs. Mean ratios and 95% confidence intervals (CIs) were calculated using bootstrapping. The impact of the collection tool on the result of the economic evaluation was calculated with the difference in costs between the 2 treatment arms with both collection methods.Results: Five cost-effectiveness studies were included in the analysis. A total of 397 patients had the SNDS hospital data, and 321 had ambulatory care data. Common collection tools underestimated hospital admissions by 13% (95% CI 8-20), corresponding costs by 5% (95% CI 2-14), and ambulatory acts by 41% (95% CI 33-51), with large variations in costs depending on the study. There was no change in the economic conclusion in any study.Conclusions: The use of common collection tools underestimates healthcare resource consumption and its associated costs, particularly for ambulatory care. Our results could provide useful evidence-based estimates to inform sensitivity analyses' parameters in future cost-effectiveness analyses. [ABSTRACT FROM AUTHOR]