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PubMed2006

Quantifying uncertainty in the potential distribution of an invasive species: climate and the Argentine ant.

Maps of a species' potential range make an important contribution to conservation and invasive species risk analysis. Spatial predictions, however, should be accompanied by an assessment of their uncertainty. Here, we use multimodel inference to generate confidence intervals that incorporate both the uncertainty involved in model selection as well as the error associated with model fitting. In the case of the invasive Argentine ant, we found that it was most likely to occur where the mean daily temperature in mid-winter was 7-14 degrees C and maximum daily temperatures during the hottest month averaged 19-30 degrees C. Uninvaded regions vulnerable to future establishment include: southern China, Taiwan, Zimbabwe, central Madagascar, Morocco, high-elevation Ethiopia, Yemen and a number of oceanic islands. Greatest uncertainty exists over predictions for China, north-east India, Angola, Bolivia, Lord Howe Island and New Caledonia. Quantifying the costs of different errors (false negatives vs. false positives) was considered central for connecting modelling to decision-making and management processes.

Animals↗
PubMedDate not supplied

Children's sensitivity to their own relative ignorance: handling of possibilities under epistemic and physical uncertainty.

Children more frequently specified possibilities correctly when uncertainty resided in the physical world (physical uncertainty) than in their own perspective of ignorance (epistemic uncertainty). In Experiment 1 (N=61), 4- to 6-year-olds marked both doors from which a block might emerge when the outcome was undetermined, but a single door when they knew the block was hidden behind one door. In Experiments 2 (N=30; 5- to 6-year-olds) and 3 (N=80; 5- to 8-year-olds), children placed food in both possible locations when an imaginary pet was yet to occupy one, but in a single location when the pet was already hidden in one. The results have implications for interpretive theory of mind and "curse of knowledge."

Attitude↗
PubMed1991

Patients' experience of uncertainty in illness in two intensive coronary care units.

The Swedish concept "trygghet" is multidimensional and includes notions such as security, safety, confidence, certainty and trust. Patients in coronary pain are in special need of "trygghet". In order to study the certainty dimension of the concept the Mishel Uncertainty in Illness Scale, MUIS, was used. This 34 item Likert scale is based on four factors: multidimensional ambiguity, complexity, deficit information and unpredictability. A special Cardiovascular Population Scale is formed by utilising 16 of the items included in MUIS. Patients' experiences of uncertainty were studied in two Intensive Coronary Care Units, in different locations, and with different staffing and nursing routines. There were almost no significant differences in scores on MUIS between the two ICCU wards. In the background data, the only discriminating factor was the time between previous and current hospital stays. The shorter the time, the more the uncertainty.

Aged↗
PubMed1992

The effect of neglecting correlations when propagating uncertainty and estimating the population distribution of risk.

Interest in examining both the uncertainty and variability in environmental health risk assessments has led to increased use of methods for propagating uncertainty. While a variety of approaches have been described, the advent of both powerful personal computers and commercially available simulation software have led to increased use of Monte Carlo simulation. Although most analysts and regulators are encouraged by these developments, some are concerned that Monte Carlo analysis is being applied uncritically. The validity of any analysis is contingent on the validity of the inputs to the analysis. In the propagation of uncertainty or variability, it is essential that the statistical distribution of input variables are properly specified. Furthermore, any dependencies among the input variables must be considered in the analysis. In light of the potential difficulty in specifying dependencies among input variables, it is useful to consider whether there exist rules of thumb as to when correlations can be safely ignored (i.e., when little overall precision is gained by an additional effort to improve upon an estimation of correlation). We make use of well-known error propagation formulas to develop expressions intended to aid the analyst in situations wherein normally and lognormally distributed variables are linearly correlated.

Analysis of Variance↗
PubMed1996

Uncertainties in the link between global climate change and predicted health risks from pollution: hexachlorobenzene (HCB) case study using a fugacity model.

Industrial societies have altered the earth's environment in ways that could have important, longterm ecological, economic, and health implications. In this paper, we examine the extent to which uncertainty about global climate change could impact the precision of predictions of secondary outcomes such as health impacts of pollution. Using a model that links global climate change with predictions of chemical exposure and human health risk in the Western region of the United States of America (U.S.), we define parameter variabilities and uncertainties and we characterize the resulting outcome variance. As a case study, we consider the public health consequences from releases of hexachlorobenzene (HCB), a ubiquitous multimedia pollutant. By constructing a matrix that links global environmental change both directly and indirectly to potential human-health effects attributable to HCB released into air, soil, and water, we define critical parameter variances in the health risk estimation process. We employ a combined uncertainty/sensitivity analysis to investigate how HCB releases are affected by increasing atmospheric temperature and the accompanying climate alterations that are anticipated. We examine how such uncertainty impacts both the expected magnitude and calculational precision of potential human exposures and health effects. This assessment reveals that uncertain temperature increases of up to 5 degrees C have little impact on either the magnitude or precision of the public-health consequences estimated under existing climate variations for HCB released into air and water in the Western region of the U.S.

Biometry↗
PubMed1997

An approach for modeling noncancer dose responses with an emphasis on uncertainty.

This paper presents an approach for characterizing the probability of adverse effects occurring in a population exposed to dose rates in excess of the Reference Dose (RfD). The approach uses a linear threshold (hockey stick) model of response and is based on the current system of uncertainty factors used in setting RfDs. The approach requires generally available toxicological estimates such as No-Observed-Adverse-Effect Levels (NOAELs) or Benchmark Doses and doses at which adverse effects are observed in 50% of the test animals (ED50s). In this approach, Monte Carlo analysis is used to characterize the uncertainty in the dose response slope based on the range and magnitude of the key sources of uncertainty in setting protective doses. The method does not require information on the shape of the dose response curve for specific chemicals, but is amenable to the inclusion of such data. The approach is applied to four compounds to produce estimates of response rates for dose rates greater than the RfD.

Acetamides↗
PubMed1998

Stochastic response surface methods (SRSMs) for uncertainty propagation: application to environmental and biological systems.

Comprehensive uncertainty analyses of complex models of environmental and biological systems are essential but often not feasible due to the computational resources they require. "Traditional" methods, such as standard Monte Carlo and Latin Hypercube Sampling, for propagating uncertainty and developing probability densities of model outputs, may in fact require performing a prohibitive number of model simulations. An alternative is offered, for a wide range of problems, by the computationally efficient "Stochastic Response Surface Methods (SRSMs)" for uncertainty propagation. These methods extend the classical response surface methodology to systems with stochastic inputs and outputs. This is accomplished by approximating both inputs and outputs of the uncertain system through stochastic series of "well behaved" standard random variables; the series expansions of the outputs contain unknown coefficients which are calculated by a method that uses the results of a limited number of model simulations. Two case studies are presented here involving (a) a physiologically-based pharmacokinetic (PBPK) model for perchloroethylene (PERC) for humans, and (b) an atmospheric photochemical model, the Reactive Plume Model (RPM-IV). The results obtained agree closely with those of traditional Monte Carlo and Latin Hypercube Sampling methods, while significantly reducing the required number of model simulations.

Air Pollutants↗
PubMed2005

Accounting for both random errors and systematic errors in uncertainty propagation analysis of computer models involving experimental measurements with Monte Carlo methods.

A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.

Journal Article↗
PubMed1998

Therapist in distress: team-supervision of social workers and family therapists who work and live under political uncertainty.

This article describes and analyzes a 2-year supervision process with social workers and family therapists who live and work under conditions of uncertainty on the West Bank. The systemic orientation used in this specific approach to supervision emphasizes the double role of the therapist: one as part of the therapeutic system, and the second as a member of the same community that is living in political uncertainty. The analysis revealed that a long-term supervision process, in which the supervisor encouraged a containing context, was meaningful to the group. As a result of this secure atmosphere, the group was ready to talk about painful issues like loss as the result of war and terrorist attacks, loss as a result of immigration, and loss of ideals. Furthermore, the members of the group were ready to confront the possibility of relocation and their role in such a situation. The techniques used in the process, such as narrative and metaphors, were implemented by the members in their daily professional interventions. The flexibility between working on regular professional issues and issues related to stress and uncertainty seemed useful to the supervision, as well as the political dialogue that was created between the supervisor and the group.

Adaptation, Psychological↗
PubMedDate not supplied

Resolving the uncertainty of preterm symptoms: women's experiences with the onset of preterm labor.

OBJECTIVE: To describe expectant women's experiences with the onset of preterm labor. DESIGN: Qualitative, using grounded theory methods. SETTING: Southwestern tertiary women's hospital. PARTICIPANTS: Thirty pregnant women who were less than 35 weeks gestation, had experienced preterm labor within the past 7 days, and had no previous experience with preterm labor. DATA SOURCE: Taped and transcribed interviews. RESULTS: Themes that emerged from the interview data included the following: recognition and naming of sensations, a consistent pattern of attribution of symptoms, the threat or risk inferred by the attributed cause of the symptom pattern, the associated certainty or uncertainty about these attributions, the process of interpreting and verifying symptom meaning, and the decision to self-manage the symptoms or engage health care assistance. The core process of women experiencing the onset of preterm labor symptoms was identified as "resolving the uncertainty of preterm labor symptoms: recognizing and responding to the possibilities." CONCLUSIONS: Preterm labor often is not within expectant women's consciousness. They may attribute the symptoms to nonthreatening causes, which results in delays in seeking care for preterm labor. Education about symptom patterns at the onset of preterm labor will increase the probability that women and their health care providers will recognize and interpret the early, subtle symptoms that herald the onset of preterm labor. Uncertainty in illness theory and attribution theory offer frameworks for understanding women's experiences with the onset of preterm labor.

Adult↗
PubMed1999

Uncertainty in estimating exposure using a toxicokinetic model. The example of 2,3,7,8-tetrachlorodibenzo-p-dioxin.

This paper deals with sources of uncertainty in the use of a minimal physiological toxicokinetic model to obtain dose estimates for a dose-response analysis of cancer in an occupational cohort. Toxicokinetic models make it possible to construct exposure parameters that are more closely related to the individual dose than traditional measures of exposures to toxic agents. However, the process introduces a wide array of sources of uncertainty. Selecting a model structure to describe the kinetics of a toxic agent implies necessarily making simplifications and assumptions that influence the range of applicability of the model. Once a model has been selected, the value of certain model parameters (constants) must be assigned, for example, from anthropometric data. The question then arises of how sensitive the model predictions are to variations in the values of these constants. Other model parameters, typically those describing the kinetics of the agent, are next estimated from actual data. There may be limitations in the data concerning, for example, sparseness (too few observations per subject) or missing values. The methods used for parameter estimation carry their own set of assumptions that need to be appropriate to the situation at hand. In summary, the dioxin example is used to characterize the sources of uncertainty at different levels, such as model structure, methods and data used for parameter estimation, estimation of occupational exposure, and imputation of missing values in exposure indices derived from the kinetic model.

Environmental Exposure↗
PubMed1999

Uncertainty in risk assessment. Current efforts and future hopes.

The incorporation of sampling variability in estimates of excess risk has been part of risk assessment practice for decades. Currently, there is a strong desire to incorporate understanding of biological mechanisms into the models used for exposure assessment and exposure-response modeling. In addition, representing population heterogeneity in the assessment of risks and the identification of sensitive subpopulations is of great concern. Finally, the communication of uncertainty and variability remains a challenge to risk assessors. Based upon the presentations of workshop faculty, a summary of current practice when addressing uncertainty together with conjectures concerning future challenges for addressing uncertainty, are presented.

Dose-Response Relationship, Drug↗
PubMed2003

Limitations of a convolution method for modeling geometric uncertainties in radiation therapy. II. The effect of a finite number of fractions.

Convolution methods can be used to model the effect of geometric uncertainties on the planned dose distribution in radiation therapy. This requires several assumptions, including that the patient is treated with an infinite number of fractions, each delivering an infinitesimally small dose. The error resulting from this assumption has not been thoroughly quantified. This is investigated by comparing dose distributions calculated using the Convolution method with the result of Stochastic simulations of the treatment. Additionally, the dose calculated using the conventional Static method, a Corrected Convolution method, and a Direct Simulation are compared to the Stochastic result. This analysis is performed for single beam, parallel opposed pair, and four-field box techniques in a cubic water phantom. Treatment plans for a simple and a complex idealized anatomy were similarly analyzed. The average maximum error using the Static method for a 30 fraction simulation for the three techniques in phantoms was 23%, 11% for Convolution, 10% for Corrected Convolution, and 10% for Direct Simulation. In the two anatomical examples, the mean error in tumor control probability for Static and Convolution methods was 7% and 2%, respectively, of the result with no uncertainty, and 35% and 9%, respectively, for normal tissue complication probabilities. Convolution provides superior estimates of the delivered dose when compared to the Static method. In the range of fractions used clinically, considerable dosimetric variations will exist solely because of the random nature of the geometric uncertainties. However, the effect of finite fractionation appears to have a greater impact on the dose distribution than plan evaluation parameters.

Algorithms↗
PubMed2004

Limitations of a convolution method for modeling geometric uncertainties in radiation therapy: the radiobiological dose-per-fraction effect.

The convolution method can be used to model the effect of random geometric uncertainties into planned dose distributions used in radiation treatment planning. This is effectively done by linearly adding infinitesimally small doses, each with a particular geometric offset, over an assumed infinite number of fractions. However, this process inherently ignores the radiobiological dose-per-fraction effect since only the summed physical dose distribution is generated. The resultant potential error on predicted radiobiological outcome [quantified in this work with tumor control probability (TCP), equivalent uniform dose (EUD), normal tissue complication probability (NTCP), and generalized equivalent uniform dose (gEUD)] has yet to be thoroughly quantified. In this work, the results of a Monte Carlo simulation of geometric displacements are compared to those of the convolution method for random geometric uncertainties of 0, 1, 2, 3, 4, and 5 mm (standard deviation). The alpha/betaCTV ratios of 0.8, 1.5, 3, 5, and 10 Gy are used to represent the range of radiation responses for different tumors, whereas a single alpha/betaOAR ratio of 3 Gy is used to represent all the organs at risk (OAR). The analysis is performed on a four-field prostate treatment plan of 18 MV x rays. The fraction numbers are varied from 1-50, with isoeffective adjustments of the corresponding dose-per-fractions to maintain a constant tumor control, using the linear-quadratic cell survival model. The average differences in TCP and EUD of the target, and in NTCP and gEUD of the OAR calculated from the convolution and Monte Carlo methods reduced asymptotically as the total fraction number increased, with the differences reaching negligible levels beyond the treatment fraction number of > or =20. The convolution method generally overestimates the radiobiological indices, as compared to the Monte Carlo method, for the target volume, and underestimates those for the OAR. These effects are interconnected and attributed to assuming an infinite number of fractions inherent in the implementation of the convolution technique, irrespective of the uniqueness of each treatment schedule. Based on the fraction numbers analyzed (1-50), and the range of fraction numbers normally used clinically (> or =20), the convolution method can be used safely to estimate the effects of random geometric uncertainties on prostate treatment radiobiological outcomes, for both the target and the OAR. Although the results of this study is likely to apply to other clinical sites and treatment techniques other than the four-field, further validation similar to those done in this study may be necessary prior to clinical implementation.

Algorithms↗
PubMedDate not supplied

Kermas for various substances averaged over the energy spectra of fast neutron therapy beams: a study in uncertainties.

Kermas for various substances averaged over the energy spectra of fast neutron therapy beams, as well as ratios of average kermas relative to muscle, were calculated in an attempt to estimate the uncertainties introduced in these quantities by the poor knowledge of the elemental kerma functions, actual neutron energy spectra, and composition of tissues and other materials. Average kermas have uncertainties of the order of 7%-25%, while for ratios of average kermas the uncertainties are of the order of 2%-5% for materials of clinical interest. It is concluded that the ratio of average kerma of muscle to A-150 tissue-equivalent plastic should be 0.93 +/- 0.03 for the new p + Be clinical neutron beams.

Adipose Tissue↗
PubMed1996

Target margins for random geometrical treatment uncertainties in conformal radiotherapy.

In this study we investigate a method for positioning the margin required around the clinical target volume (CTV) to account for the random geometrical treatment uncertainties during conformal radiotherapy. These uncertainties are introduced by patient setup errors and CTV motion within the patient. Three-dimensional dose distributions are calculated for two four-field box techniques and a three-field technique, using rectangular fields. In addition, dose calculations are performed for four prostate cases, treated with a three-field conformal technique. The effects of random rotational and translational deviations on the delivered dose are described as a convolution of the "static" dose with the distribution of the deviations. For the rectangular field techniques, these convolutions are performed with a range of standard deviations (SDs) of the distribution of random translations (0-7 mm in the three directions) and rotations (0 degree-5 degrees around the main axes). Two centers of rotation are considered: the isocenter and a position that is 3.5 cm shifted with respect to the isocenter. For the prostate cases, the random deviations are estimated by combining the results from organ motion and setup accuracy studies. The required margin is defined as the change in the position of the static 95% isodose surface by the convolution and it is approximated by a morphological erosion operator, applied to the static 95% isodose surface. When the center of rotation coincides with the isocenter the change in the position of the static 95% isodose surface can accurately be described by an erosion operator. For the rectangular field techniques, the margin is equal to about 0.7 SD of the distribution of translations, independent of the distribution of rotations. When the center of rotation does not coincide with the isocenter and rotations are considerable, the margin is strongly place dependent, and the accuracy of the approximation by an erosion operator is much lower. In conclusion, margins for random uncertainties can be approximated by a dilation operator (inverse of an erosion operator) when the center of rotational deviations coincides with the isocenter. The size of the margin is about 0.7 SD of the distribution of translations. When rotational deviations are present and the center of rotation does not coincide with the isocenter, the margin can become strongly place dependent and the convolution computation should be incorporated in the planning system.

Biophysical Phenomena↗
PubMed2003

Seabed reflection measurement uncertainty.

The seabed reflection coefficient is a fundamental property of the ocean waveguide. Measurements of the frequency and angular dependence of the reflection coefficient can provide information about the geoacoustic properties of the seabed or can be used as an input to propagation models. The uncertainty of the measurements must be known in order to determine prediction uncertainties for the acoustic field and/or the geoacoustic properties. Analysis indicates that the reflection measurements have a standard deviation from +/- 0.5-1 dB at full angular resolution depending on frequency and experiment geometry. The dominant contribution to the error is source amplitude variability, and a new processing approach was developed that reduces the error for frequencies above a few hundred Hz. Further reduction in the uncertainty can be obtained by averaging in angle, for example, a +/- 1 degrees angle averaging leads to a standard deviation of less than +/- 0.5 dB. Errors in the angle estimate are a few tenths of a degree from 0-34 degrees grazing angle: the crucial angular range for predicting long-range propagation or for geoacoustic property inversion.

Journal Article↗
PubMed2004

Within-ear and across-ear interference in a dichotic cocktail party listening task: effects of masker uncertainty.

Increases in masker variability have been shown to increase the effects of informational masking in non-speech listening tasks, but relatively little is known about the influence that masker uncertainty has on the informational components of speech-on-speech masking. In this experiment, listeners were asked to extract information from a target phrase that was presented in their right ear while ignoring masking phrases that were presented in the same ear as the target phrase and in the ear opposite the target phrase. The level of masker uncertainty was varied by holding constant or "freezing" the talkers speaking the masking phrases, the semantic content used in the masking phrases, or both the talkers and the semantic content in the masking phrases within each block of 120 trials. The results showed that freezing the semantic content of the masking phrase in the target ear was the only reduction in masker uncertainty that ever resulted in a significant improvement in performance. Providing feedback after each trial improved performance overall, but did not prevent the listeners from making incorrect responses that matched the content of the frozen target-ear masking phrase. However, removing the target-ear contents corresponding to the masking phrase from the response set resulted in a dramatic improvement in performance. This suggests that the listeners were generally able to understand both of the phrases presented to the target ear, and that their incorrect responses in the task were almost entirely a result of their inability to determine which words were spoken by the target talker.

Adult↗