Importance Of Sensitivity Analysis Research Articles

On Importance of Sensitivity Analysis on an Example of a k-out-of-n System

Reliability and sensitivity issues are very close and important problems in any technical system. The system’s sensitivity is understood as the dependence of its behavior on changes in some internal parameters. To perform sensitivity analysis, a general procedure based on a theoretical and numerical study is proposed and applied to a repairable k-out-of-n model. The results show the asymptotic insensitivity of the non-stationary and stationary characteristics of the system reliability to the shape of the repair-time distribution, as well as to the value of its coefficient of variation at a fixed mean. The proposed methodology can be useful to researchers and engineers at the designing stage of real systems, as well as applied to other stochastic reliability models.

Proper Use of Multiple Imputation and Dealing with Missing Covariate Data

Missing data is a typical problem in clinical studies, where the value of variables of interest is not measured or collected for some patients. This article aimed to review imputation approaches for missing values and their application in neurosurgery. We reviewed current practices on detecting missingness patterns and applications of multiple imputation approaches under different scenarios. Statistical considerations and importance of sensitivity analysis were explained. Various imputation methods were applied to a retrospective cohort. For illustration purposes, a retrospective cohort of 609 patients harboring both ruptured and unruptured intracranial aneurysms and undergoing microsurgical clip reconstruction at Erasmus MC University Medical Center, Rotterdam, The Netherlands, between 2000 and 2019 was used. modified Rankin Scale score at 6 months was the clinical outcome, and potential predictors were age, sex, size of aneurysm, hypertension, smoking, World Federation of Neurosurgical Societies grade, and aneurysm location. Associations were investigated using different imputation approaches, and the results were compared and discussed. Missing values should be treated carefully. Advantages and disadvantages of multiple imputation methods along with imputation in small and big data should be considered depending on the research question and specifics of the study.

The Influence of Sensitivity Analysis on ohe Process of Price Decision Making and Pricing of the Company

Research background:An integral part of the existence of every company is the constant making of various decisions that affect the company and its surroundings on a global scale. These decisions include decisions about price, which is a key element of the company’s interaction with its surroundings. In the conditions of globalization, price is a key factor in the competition, and price decisions must be made by the company very carefully and their selection is not easy. Sensitivity analysis helps companies choose the best options and steps in the price decision making, which will ultimately affect the process of pricing.Purpose of the article:The purpose of this work is to point out the importance of sensitivity analysis as a tool for simplifying price decision making and to perform this analysis on a model example of a company.Methods:Methods of analysis and research of the issue were used in the paper. Induction and deduction methods were also used to help provide a comprehensive picture of sensitivity analysis.Findings & Value added:The result of this work is, in addition to a general overview of the sensitivity analysis in price decision making, also the specific procedure and the results of the sensitivity analysis and recommendations for the company resulting from these results. Based on the created instructions, companies can make clear pricing decisions that will be of the greatest benefit to them.

The Importance of Prior Sensitivity Analysis in Bayesian Statistics: Demonstrations Using an Interactive Shiny App.

The current paper highlights a new, interactive Shiny App that can be used to aid in understanding and teaching the important task of conducting a prior sensitivity analysis when implementing Bayesian estimation methods. In this paper, we discuss the importance of examining prior distributions through a sensitivity analysis. We argue that conducting a prior sensitivity analysis is equally important when so-called diffuse priors are implemented as it is with subjective priors. As a proof of concept, we conducted a small simulation study, which illustrates the impact of priors on final model estimates. The findings from the simulation study highlight the importance of conducting a sensitivity analysis of priors. This concept is further extended through an interactive Shiny App that we developed. The Shiny App allows users to explore the impact of various forms of priors using empirical data. We introduce this Shiny App and thoroughly detail an example using a simple multiple regression model that users at all levels can understand. In this paper, we highlight how to determine the different settings for a prior sensitivity analysis, how to visually and statistically compare results obtained in the sensitivity analysis, and how to display findings and write up disparate results obtained across the sensitivity analysis. The goal is that novice users can follow the process outlined here and work within the interactive Shiny App to gain a deeper understanding of the role of prior distributions and the importance of a sensitivity analysis when implementing Bayesian methods. The intended audience is broad (e.g., undergraduate or graduate students, faculty, and other researchers) and can include those with limited exposure to Bayesian methods or the specific model presented here.

Health Economic Evaluation of an Influenza Vaccination Program to Prevent Sick Leave in Employees: A Prospective Cohort Study.

To evaluate the cost-effectiveness of an influenza vaccination program (IVP) among employees. This health economic evaluation was embedded in a prospective cohort study of a voluntary IVP. Data on incidence, direct, and indirect costs were collected via questionnaires. Bootstrapping and modeling techniques were used to assess uncertainty of the results. In the base-scenario, the IVP was less effective, more expensive and thus, neither cost-effective nor cost-beneficial. When applying a sensitivity analysis using published estimates of IVP effectiveness on the data, the IVP became cost-effective and cost-beneficial. Like in many evaluations of real-world settings, lack of randomization may have caused selection bias which may explain the surprising results of the main analysis. This indicates the importance of sensitivity analyses and modeling approaches for future studies assessing the cost-effectiveness of IVP in a real-world setting.

Perspectives on Investment Cost Estimates for Gasification-based Biofuel Production Systems

This study presents investment cost estimates for three different gasification based biofuel production systems producing synthetic natural gas (SNG), methanol and Fischer-Tropsch (FT) fuels. A comparison of cost estimates for similar systems presented in the scientific literature and technical reports was also made. The comparison is made on a common basis for a gasifier capacity of 480 MWth LHV biomass input. Results show that for all three fuels most of the compared estimates fall within the similar to 30 % uncertainty range of a study estimate. The Chemical Plant Cost Index (CEPCI) was used for updating cost estimates to the money value in 2012. An analysis of the impact of using alternative cost escalating indices showed that two of the most commonly used cost escalating indices, the CEPCI and CERA DCCI (downstream capital cost index), gave differences in total plant investment of as much as 30 % for long or specific updating periods. For short updating periods the difference was small. These results underline the importance of sensitivity analysis for investment costs in the analysis of profitability for 2nd generation biofuel plants.

The current practice of handling and reporting missing outcome data in eight widely used PROMs in RCT publications: a review of the current literature.

PurposePatient-reported outcome measures (PROMs) are designed to assess patients’ perceived health states or health-related quality of life. However, PROMs are susceptible to missing data, which can affect the validity of conclusions from randomised controlled trials (RCTs). This review aims to assess current practice in the handling, analysis and reporting of missing PROMs outcome data in RCTs compared to contemporary methodology and guidance.MethodsThis structured review of the literature includes RCTs with a minimum of 50 participants per arm. Studies using the EQ-5D-3L, EORTC QLQ-C30, SF-12 and SF-36 were included if published in 2013; those using the less commonly implemented HUI, OHS, OKS and PDQ were included if published between 2009 and 2013.ResultsThe review included 237 records (4–76 per relevant PROM). Complete case analysis and single imputation were commonly used in 33 and 15 % of publications, respectively. Multiple imputation was reported for 9 % of the PROMs reviewed. The majority of publications (93 %) failed to describe the assumed missing data mechanism, while low numbers of papers reported methods to minimise missing data (23 %), performed sensitivity analyses (22 %) or discussed the potential influence of missing data on results (16 %).ConclusionsConsiderable discrepancy exists between approved methodology and current practice in handling, analysis and reporting of missing PROMs outcome data in RCTs. Greater awareness is needed for the potential biases introduced by inappropriate handling of missing data, as well as the importance of sensitivity analysis and clear reporting to enable appropriate assessments of treatment effects and conclusions from RCTs.Electronic supplementary materialThe online version of this article (doi:10.1007/s11136-015-1206-1) contains supplementary material, which is available to authorized users.

PIN72 - The Importance of Sensitivity Analysis in Assessing Clinical and Economic Impact of National Immunization Programs: An Example of Slovenia

PIN72 - The Importance of Sensitivity Analysis in Assessing Clinical and Economic Impact of National Immunization Programs: An Example of Slovenia

PRM242 - The Importance of Sensitivity Analyses in Health Technology Assessments

PRM242 - The Importance of Sensitivity Analyses in Health Technology Assessments

AN APPROACH TO MULTI-POLLUTANT ACCOUNTABILITY RESEARCH USING PRINCIPAL STRATIFICATION

Air quality regulations are typically enacted with the goal of improving public health, and large-scale regulations that reduce ambient air pollution are often accompanied by improvements in mortality and morbidity. However, whether observed improvements in health are due to a regulation or to concurrent trends in other risk factors often remains uncertain. Quantifying health effects of actions taken to improve air quality, also known as accountability research, is challenging for large-scale efforts meant to improve air quality and health over a long period of time and over large geographical areas. In this talk we will introduce a potential-outcomes framework for accountability research that uses a Bayesian MCMC computational strategy. The approach stratifies areas based upon the estimated causal effect of an air quality regulation on ambient air pollution and assesses the causal effect of the regulation on health outcomes within these strata. As regulations may affect concentrations of many interrelated pollutants, we extend the use of principal stratification to a multi-pollutant approach that accommodates a multivariate intermediate response vector. Furthermore, we make use of recent advancements in hierarchical modeling for point-referenced spatial data to capitalize on information contained in the geographic locations of the pollution measurement sites. Insofar as these methods rely on unobserved potential outcomes, they rely on assumptions regarding associations not identified based on observed data, indicating the importance of sensitivity analyses. We apply our method to examine whether the 1990 Clean Air Act Amendments causally affected Medicare mortality through affecting ambient concentrations of particulate matter and ozone.

Structure and sensitivity analysis of individual-based predator–prey models

The expensive computational cost of sensitivity analyses has hampered the use of these techniques for analysing individual-based models in ecology. A relatively cheap computational cost, referred to as the Morris method, was chosen to assess the relative effects of all parameters on the model’s outputs and to gain insights into predator–prey systems. Structure and results of the sensitivity analysis of the Sumatran tiger model – the Panthera Population Persistence (PPP) and the Notonecta foraging model (NFM) – were compared. Both models are based on a general predation cycle and designed to understand the mechanisms behind the predator–prey interaction being considered. However, the models differ significantly in their complexity and the details of the processes involved. In the sensitivity analysis, parameters that directly contribute to the number of prey items killed were found to be most influential. These were the growth rate of prey and the hunting radius of tigers in the PPP model as well as attack rate parameters and encounter distance of backswimmers in the NFM model. Analysis of distances in both of the models revealed further similarities in the sensitivity of the two individual-based models. The findings highlight the applicability and importance of sensitivity analyses in general, and screening design methods in particular, during early development of ecological individual-based models. Comparison of model structures and sensitivity analyses provides a first step for the derivation of general rules in the design of predator–prey models for both practical conservation and conceptual understanding.

Sensitivity analysis of the rice model WARM in Europe: Exploring the effects of different locations, climates and methods of analysis on model sensitivity to crop parameters

Sensitivity analysis studies how the variation in model outputs can be due to different sources of variation. This issue is addressed, in this study, as an application of sensitivity analysis techniques to a crop model in the Mediterranean region. In particular, an application of Morris and Sobol' sensitivity analysis methods to the rice model WARM is presented. The output considered is aboveground biomass at maturity, simulated at five rice districts of different countries (France, Greece, Italy, Portugal, and Spain) for years characterized by low, intermediate, and high continentality. The total effect index of Sobol' (that accounts for the total contribution to the output variation due a given parameter) and two Morris indices (mean μ and standard deviation σ of the ratios output changes/parameter variations) were used as sensitivity metrics. Radiation use efficiency (RUE), optimum temperature ( T opt), and leaf area index at emergence (LAI ini) ranked in most of the combinations site × year as first, second and third most relevant parameters. Exceptions were observed, depending on the sensitivity method (e.g. LAI ini resulted not relevant by the Morris method), or site-continentality pattern (e.g. with intermediate continentality in Spain, LAI ini and T opt were second and third ranked; with low continentality in Portugal, RUE was outranked by T opt). Low σ values associated with the most relevant parameters indicated limited parameter interactions. The importance of sensitivity analyses by exploring site × climate combinations is discussed as pre-requisite to evaluate either novel crop-modelling approaches or the application of known modelling solutions to conditions not explored previously. The need of developing tools for sensitivity analysis within the modelling environment is also emphasized.

Aurora Textile Company

This case asks the student to decide whether Aurora Textile Company can create value by upgrading its spinning machine to produce higher-quality yarn that sells for a higher margin. Cost information allows the student to produce cash-flow projections for both the existing spinning machine and the new machine. The cash flows have many different cost components, including depreciation, the number of days of cotton inventory, and the liability costs associated with returns from retailers. The cost of capital is specified in order to simplify the analysis. The analysis has added complexity, however, owing to the troubled financial condition of both the company and the U.S. textile industry, which is in decline as manufacturers migrate to Asia to benefit from lower manufacturing costs. This begs the question whether management should invest in a declining business or harvest the company by paying out all profits as a dividend to the owners. The case is suitable for students just beginning to learn finance principles, but is also rich enough to use with experienced students and executives. The primary learning points are as follows: The basics of incremental-cash-flow analysis: identifying the cash flows relevant to a capital-investment decision The construction of a side-by-side discounted-cash-flow analysis for a replacement decision How to adapt the NPV decision rule to a troubled or dying industry The effect of financial distress on the NPV calculation The importance of sensitivity analysis to a capital-investment decision

Bicycle helmet effectiveness in preventing injury and death

This case—bicycle helmet effectiveness—is one of a series of teaching cases in the Case-Based Series in Population-Oriented Prevention (C-POP). It has been developed for use in medical school and residency prevention curricula. The complete set of cases is presented in this supplement to the American Journal of Preventive Medicine.This case examines the cost-effectiveness of three interventions to increase utilization of bicycle helmets to avert head injuries in individuals aged 18 years and under in Onondaga Count NY. Students are initially presented with data on head injuries, hospitalization, and death related to bicycle use. They then appraise a published study on the effectiveness of bicycle helmets in averting head injury. Finally, students work in groups to determine the cost-effectiveness of each intervention by calculating implementation costs and the specific number of head injuries averted associated with intervention. The three interventions are legislative, school, and community-based campaigns to increase helmet use. Students are provided with budget estimates and assumptions needed to complete the exercise. Cost-effectiveness analysis, cost-benefit analysis, and related concepts are discussed, including provider versus societal perspectives and importance of sensitivity analysis.

Analyzing the Sensitivity of Generalized Linear Models to Incomplete Outcomes via the IDE Algorithm

Incomplete data models typically involve strong untestable assumptions about the missing data distribution. As inference may critically depend on them, the importance of sensitivity analysis is well recognized. Molenberghs, Kenward, and Goetghebeur proposed a formal frequentist approach to sensitivity analysis which distinguishes ignorance due to unintended incompleteness from imprecision due to finite sampling by design. They combine both sources of variation into uncertainty. This article develops estimation tools for ignorance and uncertainty concerning regression coefficients in a complete data model when some of the intended outcome values are missing. Exhaustive enumeration of all possible imputations for the missing data requires enormous computational resources. In contrast, when the boundary of the occupied region is of greatest interest, reasonable computational efficiency may be achieved via the imputation towards directional extremes (IDE) algorithm. This is a special imputation method designed to mark the boundary of the region by maximizing the direction of change of the complete data estimator caused by perturbations to the imputed outcomes. For multi-dimensional parameters, a dimension reduction approach is considered. Additional insights are obtained by considering structures within the region, and by introducing external knowledge to narrow the boundary to useful proportions. Special properties hold for the generalized linear model. Examples from a Kenyan HIV study will illustrate the points.

Application of distributed sensitivity analysis to a model of turbulent open channel flow in a natural river channel

Sensitivity analysis is a widely applied tool used to investigate the predictions of numerical models of environmental processes. This paper illustrates the importance of undertaking a sensitivity analysis that considers the spatially distributed nature of model predictions, rather than simply assessing the sensitivity of one or more model parameters that are assumed to represent the distributed behaviour of the system. A spatially distributed sensitivity analysis is applied to the output from a distributed model of turbulent river flow, used to simulate the flow processes in a natural river channel bifurcation. Examples are provided for an input parameter which illustrates the importance of sensitivity analysis with respect to model assessment and error analysis. The distributed nature of the analysis suggests the importance of spatial feedback in environmental systems that more traditional approaches to sensitivity analysis cannot reveal.

Sensitivity Analysis of Clay Barriers

This paper illustrates the importance of sensitivity analysis in the design of clay barriers. Theoretical modelling simulations are performed using the finite element method in which realistic scenarios for unsaturated flow are conceived. Results show that the adjoint operator technique can be an efficient component to evaluate the impact of the thickness of the clay barriers as well as in the values of hydraulic conductivity values on pressure head distribution and integrated travel time.

Equity and Efficiency Consequences of Unemployment Insurance Reform in Canada: The Importance of Sensitivity Analyses

This paper conducts an evaluation of the equity and efficiency consequences of proposals for the reform of the Canadian Unemployment Insurance program. The sensitivity of results to the measure of welfare chosen and to the model employed to simulate individual behavioral responses is illustrated. Copyright 1991 by The London School of Economics and Political Science.