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PubMed1997

Uncertainty analysis for wheelchair propulsion dynamics.

Wheelchair propulsion kinetic measurements require the use of custom pushrim force/moment measuring instruments which are not currently commercially available. With the ability to measure pushrim forces and moments has come the development of several dynamic metrics derived for analyzing key aspects of wheelchair propulsion. This paper presents several of the equations used to calculate or derive the primary variables used in the study of wheelchair propulsion biomechanics. The uncertainties for these variables were derived, and then numerically calculated for a current version of the SMARTWheel. The uncertainty results indicate that the SMARTWheel provides data which has better than 5 to 10% uncertainty, depending upon the variable concerned, at the maximum, and during most of the propulsion phase the uncertainty is considerably smaller (i.e., approximately 1%). The uncertainty analysis provides a more complete picture of the attainable accuracy of the SMARTWheel and of the degree of confidence with which the data can be recorded. The derivations and results indicate where improvements in measurement of wheelchair propulsion biomechanical variables are likely to originate. The most efficient approach is to address those variables in the design of the system which make the greatest contribution to the uncertainty. Future research will focus on the point of force application and examination of nonlinear effects.

Persons with Disabilities↗

PubMed2002

Importance of uncertainty and variability to predicted risks from trophic transfer of PCBs in dredged sediments.

Biomagnification of organochlorine and other persistent organic contaminants by higher trophic level organisms represents one of the most significant sources of uncertainty and variability in evaluating potential risks associated with disposal of dredged materials. While it is important to distinguish between population variability (e.g., true population heterogeneity in fish weight, and lipid content) and uncertainty (e.g., measurement error), they can be operationally difficult to define separately in probabilistic estimates of human health and ecological risk. We propose a disaggregation of uncertain and variable parameters based on: (1) availability of supporting data; (2) the specific management and regulatory context (in this case, of the U.S. Army Corps of Engineers/U.S. Environmental Protection Agency tiered approach to dredged material management); and (3) professional judgment and experience in conducting probabilistic risk assessments. We describe and quantitatively evaluate several sources of uncertainty and variability in estimating risk to human health from trophic transfer of polychlorinated biphenyls (PCBs) using a case study of sediments obtained from the New York-New Jersey Harbor and being evaluated for disposal at an open water off-shore disposal site within the northeast region. The estimates of PCB concentrations in fish and dietary doses of PCBs to humans ingesting fish are expressed as distributions of values, of which the arithmetic mean or mode represents a particular fractile. The distribution of risk values is obtained using a food chain biomagnification model developed by Gobas by specifying distributions for input parameters disaggregated to represent either uncertainty or variability. Only those sources of uncertainty that could be quantified were included in the analysis. Results for several different two-dimensional Latin Hypercube analyses are provided to evaluate the influence of the uncertain versus variable disaggregation of model parameters. The analysis suggests that variability in human exposure parameters is greater than the uncertainty bounds on any particular fractile, given the described assumptions.

Animals↗

PubMed2000

A systematic uncertainty analysis of an evaluative fate and exposure model.

Multimedia fate and exposure models are widely used to regulate the release of toxic chemicals, to set cleanup standards for contaminated sites, and to evaluate emissions in life-cycle assessment. CalTOX, one of these models, is used to calculate the potential dose, an outcome that is combined with the toxicity of the chemical to determine the Human Toxicity Potential (HTP), used to aggregate and compare emissions. The comprehensive assessment of the uncertainty in the potential dose calculation in this article serves to provide the information necessary to evaluate the reliability of decisions based on the HTP A framework for uncertainty analysis in multimedia risk assessment is proposed and evaluated with four types of uncertainty. Parameter uncertainty is assessed through Monte Carlo analysis. The variability in landscape parameters is assessed through a comparison of potential dose calculations for different regions in the United States. Decision rule uncertainty is explored through a comparison of the HTP values under open and closed system boundaries. Model uncertainty is evaluated through two case studies, one using alternative formulations for calculating the plant concentration and the other testing the steady state assumption for wet deposition. This investigation shows that steady state conditions for the removal of chemicals from the atmosphere are not appropriate and result in an underestimate of the potential dose for 25% of the 336 chemicals evaluated.

Air Pollutants↗

PubMed1997

Uncertainty and information flows in humanitarian agencies.

Humanitarian agencies struggle with high uncertainty. The focus here is on uncertainty that grows out of the internal complexity of the agencies themselves, rather than simply emanating from their war-ridden environments. This is illustrated with a study of uncertainty management and information processing in two UN agencies working for the victims of the conflict in southern Sudan. Data from 1995 relief distributions are used to simulate aspects of uncertainty; paradoxically, uncertainty increased with growing success of the relief operation. The findings is in tune with observations from the same region that Jok (1996) reported in this journal. While uncertainty may remain difficult to reduce, co-operation among agencies and public confidence in their work provide functional equivalents for certainty.

Data Collection↗

PubMed2004

Quantification of variability and uncertainty using mixture distributions: evaluation of sample size, mixing weights, and separation between components.

Variability is the heterogeneity of values within a population. Uncertainty refers to lack of knowledge regarding the true value of a quantity. Mixture distributions have the potential to improve the goodness of fit to data sets not adequately described by a single parametric distribution. Uncertainty due to random sampling error in statistics of interests can be estimated based upon bootstrap simulation. In order to evaluate the robustness of using mixture distribution as a basis for estimating both variability and uncertainty, 108 synthetic data sets generated from selected population mixture log-normal distributions were investigated, and properties of variability and uncertainty estimates were evaluated with respect to variation in sample size, mixing weight, and separation between components of mixtures. Furthermore, mixture distributions were compared with single-component distributions. Findings include: (1). mixing weight influences the stability of variability and uncertainty estimates; (2). bootstrap simulation results tend to be more stable for larger sample sizes; (3). when two components are well separated, the stability of bootstrap simulation is improved; however, a larger degree of uncertainty arises regarding the percentiles coinciding with the separated region; (4). when two components are not well separated, a single distribution may often be a better choice because it has fewer parameters and better numerical stability; and (5). dependencies exist in sampling distributions of parameters of mixtures and are influenced by the amount of separation between the components. An emission factor case study based upon NO(x) emissions from coal-fired tangential boilers is used to illustrate the application of the approach.

Journal Article↗

PubMed2006

Refraction and visual acuity measurements: what are their measurement uncertainties?

The details of refractive and visual acuity measurement have been examined using the International Standards Organization (ISO) guidelines for the estimates of uncertainty. It is clear that there is a number of sources of uncertainty in quoted values. Some of these can be analysed statistically, using the ISO guidelines and can be used to estimate an uncertainty in the final recorded values. These should be regarded as minimum uncertainties because there are other factors involved such as unwanted accommodation, which will affect the final outcome. The analysis shows that the standard uncertainty (+/- one standard deviation) is about 0.3 D in refractive error measurement and about 0.04 in LogMAR acuity. The normally quoted expanded uncertainty, which provides a 95 per cent confidence level, would then be 0.6 D in refractive measurement and 0.08 in LogMAR acuity.

Humans↗

PubMed2005

Assessing uncertainty in simulation-based maritime risk assessment.

Recent work in the assessment of risk in maritime transportation systems has used simulation-based probabilistic risk assessment techniques. In the Prince William Sound and Washington State Ferries risk assessments, the studies' recommendations were backed up by estimates of their impact made using such techniques and all recommendations were implemented. However, the level of uncertainty about these estimates was not available, leaving the decisionmakers unsure whether the evidence was sufficient to assess specific risks and benefits. The first step toward assessing the impact of uncertainty in maritime risk assessments is to model the uncertainty in the simulation models used. In this article, a study of the impact of proposed ferry service expansions in San Francisco Bay is used as a case study to demonstrate the use of Bayesian simulation techniques to propagate uncertainty throughout the analysis. The conclusions drawn in the original study are shown, in this case, to be robust to the inherent uncertainties. The main intellectual merit of this work is the development of Bayesian simulation technique to model uncertainty in the assessment of maritime risk. However, Bayesian simulations have been implemented only as theoretical demonstrations. Their use in a large, complex system may be considered state of the art in the field of computational sciences.

Journal Article↗

PubMed2005

Uncertainty analysis for computer model projections of hurricane losses.

Projecting losses associated with hurricanes is a complex and difficult undertaking that is wrought with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real-time estimates grew from 2 to 3 billion dollars in losses late on August 12 to a peak of 50 billion dollars for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm hit the resort areas of Charlotte Harbor near Punta Gorda and then went on to Orlando in the central part of the state, with early poststorm estimates converging on a damage estimate in the 28 to 31 billion dollars range. Comparable damage to central Florida had not been seen since Hurricane Donna in 1960. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has recognized the role of computer models in projecting losses from hurricanes. The FCHLPM established a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a Holland B parameter hurricane wind field model. Uncertainty analysis quantifies the expected percentage reduction in the uncertainty of wind speed and loss that is attributable to each of the input variables.

Journal Article↗

PubMed1999

Uncertainty in biomonitoring and kinetic modeling.

Uncertainty in exposure assessment and uncertainty in kinetic models of early effects after exposure to a toxin are addressed in this paper. Sources of uncertainty in the determination of exposure of workers in chemical industry exposed to dioxins are exhibited and a simple kinetic model for biomonitor measurements of the concentrations from occupational exposure is derived. Model uncertainty, and uncertainty in the model parameters of physiologically-based pharmacokinetic models (PBPK models) are addressed when these models are used to estimate the effective dose in risk assessment. Uncertainty in the model parameters originating from the use of different statistical analysis methods is exhibited for Hill type nonlinear kinetics of enzyme induction mediated by a toxin.

Environmental Exposure↗

PubMed2002

The effect of set-up uncertainties, contour changes, and tissue inhomogeneities on target dose-volume histograms.

Understanding set-up uncertainty effects on dose distributions is an important clinical problem but difficult to model accurately due to their dependence on tissue inhomogeneities and changes in the surface contour (i.e., variant effects). The aims are: (1) to evaluate and quantify the invariant and variant effects of set-up uncertainties, contour changes and tissue inhomogeneities on target dose-volume histograms (DVHs); (2) to propose a method to interpolate (variant) DVHs. We present a lung cancer patient to estimate the significance of set-up uncertainties, contour changes and tissue inhomogeneities in target DVHs. Differential DVHs are calculated for 15 displacement errors (with respect to the isocenter) using (1) an invariant shift of the dose distribution at the isocenter, (2) a full variant calculation, and (3) a B-spline interpolation applied to sparsely sampled variant DVHs. The collapsed cone algorithm was used for all dose calculations. Dosimetric differences are quantified with the root mean square (RMS) deviation and the equivalent uniform dose (EUD). To determine set-up uncertainty effects, weighted mean EUDs, assuming normally distributed displacement errors, are used. The maximum absolute difference and RMS deviation in the integral DVHs' relative dose between (1) the invariant and calculated curves are 65.2% and 5.8% and (2) the interpolated and calculated curves are 16.9% and 2.5%. Similarly, the maximum absolute difference and RMS deviation in mean EUD as a function of the set-up uncertainty's standard deviation between (1) the invariant and calculated curves are 0.02 and 0.01 Gy; and (2) the interpolated and calculated curves are 0.01 and 0.006 Gy. Since a "worst-case" example is selected, we conclude that, in the majority of clinical cases, the variant effects of contour changes, tissue inhomogeneities and set-up uncertainties on EUD are negligible. Interpolation is a valid, efficient method to approximate DVHs.

Algorithms↗

PubMedDate not supplied

Uncertainty analysis of field placement error measurements using digital portal and simulation image correlations.

An uncertainty analysis of a field placement error measurement method using correlations between digital portal and simulation images has been performed. This analysis used the Monte Carlo method to simulate random uncertainties arising in image registration and portal field edge detection. The uncertainty analysis used data from a field placement error study to predict the dependences of the measurement method upon image matchpoint number and spatial distribution, image registration, and uncertainties arising in the detection and description of the field edge in a portal image. It was shown that the uncertainty in the field registration had the most significant effect upon the measurement uncertainty, whereas the number of matchpoints had an extremely limited effect (beyond the minimum required for the mathematical procedure used in registration). In contrast, it was shown that an accurate field placement error measurement required that the number of pixels describing a field edge used for comparisons should exceed 20, thus automatically setting a minimum field length necessary for field placement comparisons. It was also shown that the spatial uniformity of the matchpoints in an image pair had a limited effect upon the field placement error measurement, provided that the points are distributed over about 15% of the image area.

Humans↗

PubMed2000

The effect of dose calculation uncertainty on the evaluation of radiotherapy plans.

Monte Carlo dose calculations will potentially reduce systematic errors that may be present in currently used dose calculation algorithms. However, Monte Carlo calculations inherently contain random errors, or statistical uncertainty, the level of which decreases inversely with the square root of computation time. Our purpose in this study was to determine the level of uncertainty at which a lung treatment plan is clinically acceptable. The evaluation methods to decide acceptability were visual examination of both isodose lines on CT scans and dose volume histograms (DVHs), and reviewing calculated biological indices. To study the effect of systematic and/or random errors on treatment plan evaluation, a simulated "error-free" reference plan was used as a benchmark. The relationship between Monte Carlo statistical uncertainty and dose was found to be approximately proportional to the square root of the dose. Random and systematic errors were applied to a calculated lung plan, creating dose distributions with statistical uncertainties of between 0% and 16% (1 s.d.) at the maximum dose point and also distributions with systematic errors of -16% to 16% at the maximum dose point. Critical structure DVHs and biological indices are less sensitive to calculation uncertainty than those of the target. Systematic errors affect plan evaluation accuracy significantly more than random errors, suggesting that Monte Carlo dose calculation will improve outcomes in radiotherapy. A statistical uncertainty of 2% or less does not significantly affect isodose lines, DVHs, or biological indices.

Algorithms↗

PubMed2004

Dosimetric uncertainties of three-dimensional dose reconstruction from two-dimensional data in a multi-institutional study.

Inconsistencies in the treatment planning process leading to dosimetric uncertainties may affect conclusions drawn from interinstitutional radiation oncology clinical trials. The purpose of this study was to assess the dosimetric uncertainties resulting from the process of reconstructing three-dimensional dose distributions from two-dimensional treatment plan information provided by participating institutions in a randomized clinical trial. This study was based on American College of Radiology Protocol #427, Locally Advanced Multi-Modality Protocol; a multi-institutional phase II randomized study involving radiation therapy for patients with inoperable non-small cell lung cancer. Several sources of dosimetric uncertainty were identified and analyzed, including image quality of hard-copy computed tomography (CT) images, slice spacing of CT scans, treatment position, interpretations of target volumes by radiation oncologists, the contouring of normal anatomic structures, and the use of common beam models for all dose calculations. Each source of uncertainty was investigated using a set of plans, with the ideal characteristics of digital images with 3-mm axial slice spacing and a flat couch, consisting of eight cases from Vanderbilt University Medical Center with electronically transferred CT data. The target volume DVH values were dependent on the additional uncertainty introduced by differences in delineation of the target volumes by the participating radiation oncologists. The DVH values for the lungs and heart were dependent on image quality and treatment position. Esophagus DVH values were not dependent on any of the sources of uncertainty. None of the structure DVH values were dependent on slice thickness or variations in the contouring of normal anatomic structures. Reconstruction of three-dimensional dose distributions from two-dimensional treatment plan information may be useful in cases for which digital CT data is not available or for historical data review. However, dosimetric accuracy will depend on image quality of the treatment planning CT data and consistency in the delineation of tumor volumes.

Body Burden↗

PubMed1998

Spreadsheet method for evaluation of biochemical reaction rate coefficients and their uncertainties by weighted nonlinear least-squares analysis of the integrated monod equation

A convenient method for evaluation of biochemical reaction rate coefficients and their uncertainties is described. The motivation for developing this method was the complexity of existing statistical methods for analysis of biochemical rate equations, as well as the shortcomings of linear approaches, such as Lineweaver-Burk plots. The nonlinear least-squares method provides accurate estimates of the rate coefficients and their uncertainties from experimental data. Linearized methods that involve inversion of data are unreliable since several important assumptions of linear regression are violated. Furthermore, when linearized methods are used, there is no basis for calculation of the uncertainties in the rate coefficients. Uncertainty estimates are crucial to studies involving comparisons of rates for different organisms or environmental conditions. The spreadsheet method uses weighted least-squares analysis to determine the best-fit values of the rate coefficients for the integrated Monod equation. Although the integrated Monod equation is an implicit expression of substrate concentration, weighted least-squares analysis can be employed to calculate approximate differences in substrate concentration between model predictions and data. An iterative search routine in a spreadsheet program is utilized to search for the best-fit values of the coefficients by minimizing the sum of squared weighted errors. The uncertainties in the best-fit values of the rate coefficients are calculated by an approximate method that can also be implemented in a spreadsheet. The uncertainty method can be used to calculate single-parameter (coefficient) confidence intervals, degrees of correlation between parameters, and joint confidence regions for two or more parameters. Example sets of calculations are presented for acetate utilization by a methanogenic mixed culture and trichloroethylene cometabolism by a methane-oxidizing mixed culture. An additional advantage of application of this method to the integrated Monod equation compared with application of linearized methods is the economy of obtaining rate coefficients from a single batch experiment or a few batch experiments rather than having to obtain large numbers of initial rate measurements. However, when initial rate measurements are used, this method can still be used with greater reliability than linearized approaches.

Journal Article↗

PubMed2004

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application.

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Algorithms↗

PubMedDate not supplied

Correlations in uncertainty analysis for medical decision making: an application to heart-valve replacement.

A Monte Carlo uncertainty analysis with correlations between parameters is applied to a Markov-chain model that is used to support the choice of a replacement heart-valve. The objective is to quantify the effects of uncertainty in and of correlations between probabilities of valve-related events on the life expectancies of four valve types. The uncertainty in the logit- and log-transformed parameters-mostly representing probabilities and durations-is modeled as a multivariate normal distribution. The univariate distributions are obtained through values for the median and the 0.975 quantile of each parameter. Correlations between parameters are difficult to quantify. A sensitivity analysis is suggested to study their influences on the uncertainty in valve preference prior to further elicitation efforts. The results of the uncertainty analysis strengthen the conclusions from a preceding study, which did not include uncertainty in the model parameters, where the homograft turned out to be the best choice. It is concluded that the influence of correlations is limited in most cases. Preference statements become more certain when the correlation between valve types increases.

Adult↗

PubMed1996

Uncertainty and stress in women hospitalized with high-risk pregnancy.

The purpose of this descriptive correlational study was to describe how women hospitalized with high-risk pregnancy perceive the uncertainties and stress of their situations. A sample consisting of 58 hospitalized antepartum women in a tertiary maternity hospital completed the Uncertainty Stress Scale-High-Risk Pregnancy Version 48 hours after admission and at the time of discharge, when going home undelivered. Uncertainty at admission was found to be moderately low for 86% of the women, and was significantly lower at the time of discharge. Higher levels of uncertainty were positively correlated with higher levels of stress from the uncertainty in this sample. Women who stayed longer in the hospital reported higher uncertainty. These results have implications for the assessment, support, teaching, and discharge planning provided by nurses during antepartum hospitalization.

Adult↗

PubMed2003

Causal uncertainty, claimed and behavioural self-handicapping.

BACKGROUND: Causal uncertainty beliefs involve doubts about the causes of events, and arise as a consequence of non-contingent evaluative feedback: feedback that leaves the individual uncertain about the causes of his or her achievement outcomes. Individuals high in causal uncertainty are frequently unable to confidently attribute their achievement outcomes, experience anxiety in achievement situations and as a consequence are likely to engage in self-handicapping behaviour. AIMS: Accordingly, we sought to establish links between trait causal uncertainty, claimed and behavioural self-handicapping. SAMPLE: Participants were N=72 undergraduate students divided equally between high and low causally uncertain groups. METHOD: We used a 2 (causal uncertainty status: high, low) x 3 (performance feedback condition: success, non-contingent success, non-contingent failure) between-subjects factorial design to examine the effects of causal uncertainty on achievement behaviour. Following performance feedback, participants completed 20 single-solution anagrams and 12 remote associate tasks serving as performance measures, and 16 unicursal tasks to assess practice effort. Participants also completed measures of claimed handicaps, state anxiety and attributions. RESULTS: Relative to low causally uncertain participants, high causally uncertain participants claimed more handicaps prior to performance on the anagrams and remote associates, reported higher anxiety, attributed their failure to internal, stable factors, and reduced practice effort on the unicursal tasks, evident in fewer unicursal tasks solved. CONCLUSIONS: These findings confirm links between trait causal uncertainty and claimed and behavioural self-handicapping, highlighting the need for educators to facilitate means by which students can achieve surety in the manner in which they attribute the causes of their achievement outcomes.

Achievement↗