Role Of Sensitivity Analysis Research Articles

Missing data: A statistical framework for practice.

Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and the role of sensitivity analysis. This article seeks to address both applied practitioners and researchers interested in a more formal explanation of some of the results. For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate. In particular, we describe how multiple imputation can be readily used for sensitivity analyses, which are still infrequently performed. For those interested in more formal derivations, we give outline arguments for key results, use simple examples to show how methods relate, and references for full details. The ideas are illustrated with a cohort study, a multi-centre case control study and a randomised clinicaltrial.

An isogeometric numerical study of partially and fully implicit schemes for transient adjoint shape sensitivity analysis

In structural design optimization involving transient responses, time integration scheme plays a crucial role in sensitivity analysis because it affects the accuracy and stability of transient analysis. In this work, the influence of time integration scheme is studied numerically for the adjoint shape sensitivity analysis of two benchmark transient heat conduction problems within the framework of isogeometric analysis. It is found that (i) the explicit approach (β = 0) and semi-implicit approach with β < 0.5 impose a strict stability condition of the transient analysis; (ii) the implicit approach (β = 1) and semi-implicit approach with β > 0.5 are generally preferred for their unconditional stability; and (iii) Crank-Nicolson type approach (β= 0.5) may induce a large error for large time-step sizes due to the oscillatory solutions. The numerical results also show that the time-step size does not have to be chosen to satisfy the critical conditions for all of the eigen-frequencies. It is recommended to use β ≈ 0.75 for unconditional stability, such that the oscillation condition is much less critical than the Crank-Nicolson scheme, and the accuracy is higher than a fully implicit approach.

SENSITIVITY ANALYSIS IN MODELS OF TECHNOLOGICAL PROCESSES CONTROL

Features of technology of modern production of radio-electronic means and requirements to models of management of technological processes according to features of production are defined. The role of sensitivity analysis in the development of control models is shown. Traditionally applied sensitivity indices and methods of their estimation are considered. The limitations of their application due to the large range of products, the variability of the production technology of radio-electronic means, the non-linearity of the dynamics of technological processes and the lack of statistical data are shown. An algorithm for calculating fuzzy sensitivity parameters using a family of Johnson distributions is proposed. The application of the algorithm is effective in conditions of uncertainty of technological processes’ dynamics, uncontrollability of disturbances and insufficiency of statistical data.

Application of Decision Analytic Model in Health Economic Evaluation: Smoking Cessation Cases

Health economic evaluation that encompasses decision analytic model is a beneficial approach for assisting decision maker to choose the best health intervention for patients. Decision analytic model has been increasingly applied in health economic evaluation. This mathematical approach is mostly used for conducting cost-effectiveness of healthcare interventions.Decision tree and Markov model has been widely applied in the past 20 years. Decision tree is the simplest form of decision model that drawn by the series of branches and clear pathways. Meanwhile, Markov model is one of the powerful approaches that employ stochastic process in health economic eval­uation. This paper describes the applications of those two models in tobacco cessations, specifically for pharmacological interventions.First, decision tree for cost-effectiveness of smoking cessation program with pharmacist and thera­pies interventions compared to no program or self-aid cessation. Second, the application of Markov model estimates cost-effectiveness of veranicline, in comparison to bupropion. Markov model is constructed with morbidity and mortality states that consists of: well/no morbidities, lung cancer, COPD, stroke, myocar­dial infarction, and dead. This paper provides step by step of populating and constructing the model-with some modification of data. Several sections discuss the understanding of transition probabilities, costs data, cohort simulation, and the role of sensitivity analysis. Other models, despite deterministic approach, prob­abilistic approach are also reviewed.Both of models had both advantages and limitation that analysts should be aware of. Translating the ‘real world’ to mathematical model yields beneficial and insightful information for analysts. In addition, it could fulfill the need of evidence-based policy by decision maker. From simulation, the model may easy to be replicated-with appropriate context to generate evidence related health and costs.

Health Economic Evaluation: A Primer for Healthcare Professionals

In its broadest term, economic evaluation (EE) is a comparative analysis of the input (costs) and the output (consequences, outcomes) of two or more alternatives to see if they are economically beneficial or feasible. The earliest form of economic evaluation took place in mid-19th century and since then; three main forms of EE have evolved which are employed in various settings: cost-benefit analysis (CBA), cost-effectiveness analysis (CEA) and cost-utility analysis (CUA). Intended as a primer reading for clinicians, this article starts with the fundamental concepts of economics (e.g., costs, benefits, supply and demand, utilities and efficiency) and then combine them into principles for each tool for EE. The article will present a narrative critique of each EE in the context of modern healthcare system. As a conclusion, the article will mention some of the major challenges of these EE tools plus the role of sensitivity analysis.

Sensitivity analysis for missing data in regulatory submissions.

The National Research Council Panel on Handling Missing Data in Clinical Trials recommended that sensitivity analyses have to be part of the primary reporting of findings from clinical trials. Their specific recommendations, however, seem not to have been taken up rapidly by sponsors of regulatory submissions. The NRC report's detailed suggestions are along rather different lines than what has been called sensitivity analysis in the regulatory setting up to now. Furthermore, the role of sensitivity analysis in regulatory decision-making, although discussed briefly in the NRC report, remains unclear. This paper will examine previous ideas of sensitivity analysis with a view to explaining how the NRC panel's recommendations are different and possibly better suited to coping with present problems of missing data in the regulatory setting. It will also discuss, in more detail than the NRC report, the relevance of sensitivity analysis to decision-making, both for applicants and for regulators. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Global sensitivity analysis techniques to simplify the calibration of traffic simulation models. Methodology and application to the IDM car‐following model

As models are simplifications of reality, the management of the uncertainty arising along the whole modelling process is a crucial and delicate operation, which primarily affects the credibility of model results. In the field of traffic simulation to tackle this issue, it is common practice to include the model uncertainty alongside the uncertainty in the parametric inputs. However, reducing the uncertainty in the modelling process through the indirect estimation of the model parameters is far from being simple. In this picture, a key role can be played by model sensitivity analysis. In the present work, in particular, the role of sensitivity analysis in the management of modelling uncertainties is firstly illustrated. Then, one of the most advanced techniques to perform sensitivity analysis is explained and applied to identify, in the specific context of application, which of the input factors of two car-following models can be fixed without appreciably affecting a specific output of interest. Results confirmed the relevance of sensitivity analysis in driving analysts' activities for models' comprehension, calibration and validation, namely, for their appropriate use.

The handling of missing data in clinical trials

The problem of handling missing data in clinical trials is discussed, particularly in the light of two recent publications associated with the US and European regulators. The importance of assessing methods in the light of the target of the analysis – the estimand – is emphasized, and two types of estimand, de jure and de facto, are introduced. A key distinction is made between analyses in which missing data are ‘defined away’ and in which they represent a nuisance to be accommodated. The role of sensitivity analysis, which is acknowledged to play a major role in this setting, is considered. Finally, the recent recognition of the importance of collecting outcome data following protocol deviations is touched upon.

Active Surveillance Versus Surgery for Low Risk Prostate Cancer: A Clinical Decision Analysis

We assessed the effect of age, health status and patient preferences on outcomes of surgery vs active surveillance for low risk prostate cancer. We used Monte Carlo simulation of Markov models of the life courses of 200,000 men diagnosed with low risk prostate cancer and treated with surveillance or radical prostatectomy to calculate quality adjusted life expectancy, life expectancy, prostate cancer specific mortality and years of treatment side effects, with model parameters derived from the literature. We simulated outcomes for men 50 to 75 years old with poor, average or excellent health status (50%, 100% and 150% of average life expectancy, respectively). Sensitivity of outcomes to uncertainties in model parameters was tested. For 65-year-old men in average health, surgery resulted in 0.3 additional years of life expectancy, 1.6 additional years of impotence or incontinence and a 4.9% decrease in prostate cancer specific mortality compared to surveillance, for a net difference of 0.05 fewer quality adjusted life years. Increased age and poorer baseline health status favored surveillance. With greater than 95% probability, surveillance resulted in net benefits compared to surgery for age older than 74, 67 and 54 years for men in excellent, average and poor health, respectively. Patient preferences toward life under surveillance, biochemical recurrence of disease, treatment side effects and future discount rate affected optimal management choice. Older men and men in poor health are likely to have better quality adjusted life expectancy with active surveillance. However, specific individual preferences impact optimal choices and should be a primary consideration in shared decision making.

MACRO (v5.2): Model Use, Calibration, and Validation

MACRO is a one-dimensional dual-permeability model of variably saturated water flow and reactive solute transport in soil that has been used since the early 1990s as a research tool to investigate the effects of macropore flow on soil hydrology and contaminant transport under transient field conditions. It is also widely used in the form of bespoke versions in pesticide exposure and risk assessments for groundwater and surface waters, e.g., in registration procedures in the European Union (EU). Macropore flow is a highly episodic, fast, non-equilibrium process that can dominate the leaching of reactive solutes in structured soils. This has important consequences for model calibration and validation procedures. Firstly, it means that in addition to water contents and resident solute concentrations, water flows and flux concentrations measured at high time resolution are required. Secondly, it implies that more weight must be placed on flux data obtained during these important but short-lived episodic flow events if parameters controlling macropore flow are to be reliably estimated. Although the choice of approach will vary with the purpose, automatic or hybrid automatic/manual calibration procedures are generally recommended for MACRO, especially global methods that account for uncertainty within a multi-objective framework. Despite the complexity of the processes it attempts to represent, MACRO is a parsimonious model, requiring only five additional parameters to simulate water flow and reactive solute transport, compared to the use of the Richards equation and the advection-dispersion equation. Nevertheless, for practical reasons, the size of the parameter space that can be explored by calibration is often quite limited. This emphasizes the importance of measuring those parameters that can be measured and the role of sensitivity analyses in supporting the choice of parameters to calibrate. This will vary between applications, but at least for structured soils, all five parameters regulating the generation and strength of non-equilibrium flow and transport are usually rather influential.

The Smith Company: a case on capital budgeting and real options

PurposeThis paper aims to provide upper‐level accounting and/or finance students with a review of the intricacies of option pricing, discounted cash flow (DCF) capital budgeting decision models, various types of real options, how risk analysis of long‐term capital investments can be facilitated by explicit consideration of real options, and the role of sensitivity analysis in the analysis of capital investment projects with real options.Design/methodology/approachThe paper describes a fictional company facing a risky capital investment proposal. Students evaluate the investment proposal using traditional DCF analysis (i.e. the net present value method), and then re‐run the analysis by incorporating the existence of real options into the analysis of the proposed investment. Finally, students see the value of using Crystal Ball software for conducting sensitivity analysis as part of the decision‐making process.FindingsThere are two primary conceptual lessons that students realize by completing this educational case: real‐options analysis is a conceptually correct and robust way to explicitly deal with project uncertainty, and failure to explicitly consider real options in the analysis of capital investment projects may result in suboptimal decision making.Originality/valueThis case covers all major real‐option topics required for the certified management accountant exam. Further, the case fills a void in the literature of accounting education as this literature pertains to the availability of case material regarding the use of real options as an extension to conventional capital budgeting techniques.

Applying Sensitivity Analyses to Overall Survival in Cancer Clinical Trials

TO THE EDITOR: “Role of Sensitivity Analyses in Assessing Progression-Free Survival in Late-Stage Oncology Trials” by Bhattacharya et al is an important statistical technique that assesses whether the results of phase III trials are robust and likely to be generalizable. The authors focus their comments and state that it is “important to conduct additional analyses (sensitivity analyses) of studies where progression-free survival (PFS) or time to tumor progression (TTP) is the primary end point.” Thus, implied in their emphasis is that overall survival (OS), the ultimate gold-standard outcome (the time from random assignment to death), may not be subject to confounding issues and does not require similar consideration. Bhattacharya et al state that “the documented date of first progression is often after the actual true (unknown) date of progression, which is not the case for OS.” However, they do not consider events that could similarly affect the timing of the OS primary event. Death, although an objective and measurable event, is affected by various clinical acts that can substantially influence this outcome. It is not the issue of when death occurred but various influences that affect the outcome. For instance, similar patients failing interventional therapy at the same time point after therapy may be kept alive (eg, avoiding the primary outcome) by heroic measures such as intensive care unit care or simple measures such as treating a pneumonia, or some may have only basic noninterventional hospice/end-of-life therapy offered that does not artificially prolong survival. Additionally, patients with bulky tumors compared with those with trivial tumor burdens, similarly failing therapy at the same time point without further therapy, may have different survival outcomes unrelated to the primary interventional therapy. It is therefore equally important for sensitivity testing to be pursued when phase III studies are positive for OS when differences are statistically different, but not robust biologically, because it is becoming more common for outcomes to be measured in a few weeks or months when large numbers of patients are entered on a trial. This is especially important when drugs or biologic are approved for generalizable intervention based on modest time increments for OS.

The Cournot game under a fuzzy decision environment

The conventional precision-based decision analysis methodology is invalid for business decision analysis when precise assessment data seldom exist. This paper considers the Cournot game with fuzzy demand and fuzzy costs that are assumed to be triangular fuzzy numbers. Our model utilizes the weighted center of gravity (WCoG) method to defuzzify the fuzzy profit function into a crisp value. We derive the equilibrium Cournot quantity of each firm by simultaneously solving the first-order condition of each firm. Our model explicitly derives the necessary condition to avoid an unreasonable outcome of negative equilibrium quantities and lack of flexibility for modification of the ranking method. In addition, we examine the standard deviation of the fuzzy profit resulting from the fuzziness of each firm’s cost and market demand functions. We conduct sensitivity analysis to investigate the effect of parameter perturbations on firms’ outcomes. The results indicate that the center of parameter plays an important role in sensitivity analysis and dominates over variations in equilibrium quantity due to a perturbation of fuzzy parameters.

Practical Considerations in HIV Testing in the Emergency Department, Characteristics of Diagnostic Tests, and the Role of Sensitivity Analysis in Observational Studies: Answers to March 2008 Journal Club Questions

Practical Considerations in HIV Testing in the Emergency Department, Characteristics of Diagnostic Tests, and the Role of Sensitivity Analysis in Observational Studies: Answers to March 2008 Journal Club Questions

Elastic wave scattering in a solid half-space with a circular cylindrical hole using the Distributed Point Source Method

Ultrasonic wave scattering in a solid half-space containing a circular cylindrical hole is studied. The solid is struck by a bounded ultrasonic beam. Distributed Point Source Method (DPSM) which is a semi-analytical technique is adopted to model the ultrasonic field. A finite size transducer is used to generate the ultrasonic field. The circular hole in the solid is modeled by passive point sources. These point sources along with the point sources placed near the fluid–solid interface contribute to the scattered wave field in the solid. Even though the wave scattering by a circular hole in a solid is a classical problem this is the first time the complete variation of the scattered wave field generated by a bounded ultrasonic beam in a solid half-space with a circular hole is shown. The solution of this problem will help us to understand the distortion of the ultrasonic field in a solid and in the neighboring fluid due to the presence of a cylindrical anomaly which plays an important role in sensitivity analysis and calibration of different non-destructive testing techniques. Numerical examples for 1 MHz and 2.25 MHz transducers are given and scattered wave fields generated by holes of different diameters in an aluminum half-space are compared with the wave fields generated in the defect-free environment.

The role of sensitivity analysis in ecological modelling

This work illustrates available best practices to run a sensitivity analysis for ecological models. The properties of recommended methods and their ranges of application are illustrated by applying the sensitivity analysis techniques to two test cases. The first one is a classic Lotka–Volterra model, while the second one is a study on a fish population dynamics. Both quantitative and qualitative approaches are applied and the differences between local and global techniques are highlighted by using the test cases.

Sensitivity analysis based methodology to estimate the best set of parameters for heterogeneous kinetic models

The role of sensitivity analysis during kinetic parameter estimation is discussed in this work. An approach consisting in various steps: initialization of parameter values, nonlinear parameter estimation, and parameter sensitivity analysis, is proposed to assure that kinetic parameters are properly estimated and the convergence of the objective function to the global minimum is achieved. The method is illustrated with experimental data reported in the literature for the hydrodesulfurization of benzothiophene. The values of kinetic parameters obtained with the proposed methodology gave slightly lower values of the sum of square errors between experimental and predicted reaction rates compared with reported ones.

Efficient sensitivity analysis for flow and transport in the Earth's crust and mantle

SUMMARY This paper focuses on the role of sensitivity analysis in studies of water transport in the environmental and geophysical sciences. Sensitivity analysis is useful in model development to determine how changes in parameter input will affect a model’s response. It is also used in data assimilation to incorporate observations into a model. The adjoint method allows an efficient calculation of a model’s sensitivity when the number of parameters exceeds the number of observations. In this paper we present studies demonstrating the use of the adjoint method for modelling water transport in both the Earth’s crust and mantle.

Ten Heuristics for Interdisciplinary Modeling Projects

Complex environmental and ecological problems require collaborative, interdisciplinary efforts. A common approach to integrating disciplinary perspectives on these problems is to develop simulation models in which the linkages between system components are explicitly represented. There is, however, little guidance in the literature on how such models should be developed through collaborative teamwork. In this paper, we offer a set of heuristics (rules of thumb) that address a range of challenges associated with this enterprise, including the selection of team members, negotiating a consensus view of the research problem, prototyping and refining models, the role of sensitivity analysis, and the importance of team communication. These heuristics arose from a comparison of our experiences with several interdisciplinary modeling projects. We use one such experience—a project in which natural scientists, social scientists, and local residents came together to investigate the sustainability of small indigenous communities in the Arctic—to illustrate the heuristics.

Uncertainty in the economic evaluation of health care technologies: the role of sensitivity analysis.

Given the increased use of economic evaluation to inform decision making in the health service, it is particularly important that the research methods used are critically assessed and, where possible, improved. The systematic handling of uncertainty in economic evaluation is an important area that remains methodologically underdeveloped. With the increased use of the clinical trial as a vehicle for economic evaluation, there has been recent interest in how the statistical methods routinely employed to handle uncertainty in clinical research might be applied to economic evaluation. This paper reviews the types of uncertainty that exist in economic evaluation and argues that some forms of uncertainty are not amenable to statistical methods. Sensitivity analysis is not a single approach but can take a number of different forms. The different types of sensitivity analysis are reviewed, with an indication of their strengths and weaknesses in relation to the different types of uncertainty in economic evaluation.