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PubMed2004

Dealing with medical uncertainty: a physician's perspective.

Uncertainty in diagnosis is frequently encountered in medical practice and causes stress in patients and physicians. Factors contributing to uncertainty include biological variability of patients, patient and physician bias, error in test interpretation, differing values and opinions of patients and physicians, and uncertainty surrounding decision-making. Physicians differ in their ability to tolerate uncertainty, and this varying tolerance has been linked with choice of specialty, increased test ordering, personal anxiety, increased cost of providing medical care, and decreased comfort with geriatric patients and patients with psychological problems. We review the current evidence for and effects of physician uncertainty in medical practice. Although uncertainty in practice cannot be completely eliminated, numerous strategies can be adopted to decrease uncertainty and enhance patients' trust. These include applying the best-available evidence-based information along with observance of core clinical practices, including meticulous history taking, excluding worrisome diagnoses, and involvement in shared decision-making.

Communication↗

PubMed2005

[Estimation of measurement uncertainty of analytical results for the determination of three active components from Gardenia jasminoides Ellis. by HPLC].

OBJECTIVE: To estimate the uncertainty for quantitative analysis results of geniposide, chloregenic acid and crocinl I in Gardenia jasminoides Ellis. METHODS: HPLC method was employed to determine the amounts of geniposide, choloregenic acid and crocinl I in Gardenia jasminoides Ellis. Analyzing the uncertainty sources arising from the procedure of analysis, the standard uncertainty and combined uncertainty and expanded uncertainty were calculated according to the data of HPLC. RESULTS: This method met the requirements of modern pharmaceutical analysis, and the expand uncertainties for the HPLC method of the three components are 0.1024, 0.2254, 0.1264, respectively. CONCLUSION: Applying measurement uncertainty to the evaluation of quantitative analysis results of active components in Gardenia jasminoides Ellis. is an improvement to the actual error evaluation system.

Algorithms↗

PubMed1999

Estimating uncertainty ranges for costs by the bootstrap procedure combined with probabilistic sensitivity analysis.

When an economic evaluation incorporates patient-level data, there are two types of uncertainty over the results: uncertainty due to variation in the sampled data, and uncertainty over the choice of modelling parameters and assumptions. Previously statistical methods have been used to estimate the extent of the former, and sensitivity analysis to estimate the extent of the latter. Ideally interval estimates for economic variables should reflect both types of uncertainty. This paper describes a method for combining bootstrapping with probabilistic sensitivity analysis to estimate a total 'uncertainty range' for incremental costs. The approach is illustrated using cost data from a randomized controlled trial of endoscopy for Helicobactor pylori negative young dyspeptic patients. The trial failed to demonstrate any clinical benefit from endoscopy, which was on average pound 395 more costly. The combined 95% uncertainty range for incremental costs (-pound 236 to pound 931) was wider than 95% intervals estimated by either probabilistic sensitivity analysis (pound 43 to pound 592) or the non-parametric bootstrap method (-pound 95 to pound 667) alone. The method can easily be extended to the calculation of uncertainty ranges for incremental cost-effectiveness ratios.

Confidence Intervals↗

PubMed1996

A probabilistic model for deriving soil quality criteria based on secondary poisoning of top predators. I. Model description and uncertainty analysis.

In previous studies, the risk of toxicant accumulation in food chains was used to calculate quality criteria for surface water and soil. A simple algorithm was used to calculate maximum permissable concentrations [MPC = no-observed-effect concentration/bioconcentration factor(NOEC/BCF)]. These studies were limited to simple food chains. This study presents a method to calculate MPCs for more complex food webs of predators. The previous method is expanded. First, toxicity data (NOECs) for several compounds were corrected for differences between laboratory animals and animals in the wild. Second, for each compound, it was assumed these NOECs were a sample of a log-logistic distribution of mammalian and avian NOECs. Third, bioaccumulation factors (BAFs) for major food items of predators were collected and were assumed to derive from different log-logistic distributions of BAFs. Fourth, MPCs for each compound were calculated using Monte Carlo sampling from NOEC and BAF distributions. An uncertainty analysis for cadmium was performed to identify the most uncertain parameters of the model. Model analysis indicated that most of the prediction uncertainty of the model can be ascribed to uncertainty of species sensitivity as expressed by NOECs. A very small proportion of model uncertainty is contributed by BAFs from food webs. Correction factors for the conversion of NOECs from laboratory conditions to the field have some influence on the final value of MPC5, but the total prediction uncertainty of the MPC is quite large. It is concluded that the uncertainty in species sensitivity is quite large. To avoid unethical toxicity testing with mammalian or avian predators, it cannot be avoided to use this uncertainty in the method proposed to calculate MPC distributions. The fifth percentile of the MPC is suggested as a safe value for top predators.

Algorithms↗

PubMedDate not supplied

Influence of autoregressive model parameter uncertainty on spectral estimates of heart rate dynamics.

Linear autoregressive (AR) model-based heart rate (HR) spectral analysis has been widely used to study HR dynamics. Owing to system and measurement noise, the parameters of an AR model have intrinsic statistical uncertainty. In this study, we evaluate how this AR parameter uncertainty can translate to uncertainty in HR power spectra. HR time series, obtained from seven subjects in supine and standing positions, were fitted to AR models by least squares minimization via singular value decomposition. Spectral uncertainty due to inexact parameter estimation was assessed through a Monte Carlo study in which the AR model parameters were varied randomly according to their Gaussian distributions. Histogram techniques were used to evaluate the distribution of 50,000 AR spectral estimates of each HR time series. These Monte Carlo uncertainties were found to exceed those predicted by previous theoretical approximations. It was determined that the uncertainty of AR HR spectral estimates, particularly the locations and magnitudes of spectral peaks, can often be large. The same Monte Carlo analysis was applied to synthetic AR time series and found levels of spectral uncertainty similar to that of the HR data, thus suggesting that the results of this study are not specific to experimental HR data. Therefore, AR spectra may be unreliable, and one must be careful in assigning pathophysiological origins to specific spectral features of any one spectrum.

Adult↗

PubMed2002

Measurement uncertainty in the pH measurement procedure.

A measured value without even an approximate knowledge of the uncertainty is worthless. The uncertainty is part of every measured value and specification of the uncertainty is part of every analytical procedure. The uncertainty makes the value independent of its origin. The basis for estimation of the uncertainty is the "Guide to the Expression of Uncertainty in Measurement ". For some procedures, however, for example pH measurement, several problems arise in practice. This article describes a practical and inexpensive way of calculating the uncertainty of pH values.

Journal Article↗

PubMed2000

Estimation of the uncertainty of CRMs in accordance with GUM: application to the certification of four enzyme CRMs.

Uncertainties of four enzyme-CRMs that have recently been certified in a co-operation between the IRMM and the International Federation for Clinical Chemistry were estimated. Estimation was based on the sum of the uncertainties of characterization, homogeneity and stability. Data from the certification collaborative study were used to estimate laboratory uncertainties, which form the basis for the uncertainty of characterization. Estimations for the uncertainty of homogeneity were derived from classical homogeneity studies. The estimations of uncertainty of stability caused the most difficulties. Realistic uncertainties fitting the needs of customers while being derived from measurement data based on theoretical considerations were obtained.

Algorithms↗

PubMed2001

Estimating the uncertainty of stability for matrix CRMs.

The new version of ISO Guide 34 requires producers of certified reference materials (CRMs) to include contributions of possible instability to the overall CRM uncertainty, to obtain a value for the uncertainty in compliance with the Guide to the Expression of the Uncertainty in Measurement (GUM). A pragmatic approach to estimating the uncertainty of stability is presented. It relies on regression analysis of stability data with subsequent testing of the slope of the regression line for significance. If the slope is found to be statistically insignificant, a shelf life is chosen and the uncertainty connected with this time is estimated via the standard deviation of the slope. This uncertainty is included in the overall uncertainty of the CRM. This approach is explained with examples showing its applicability to matrix CRMs.

Chemistry Techniques, Analytical↗

PubMed2002

Uncertainty analysis of predicted disturbance from off-road vehicular traffic in complex landscapes at fort hood.

The US Army Engineering Research Development Center (ERDC) uses a modified form of the Revised Universal Soil Loss Equation (RUSLE) to estimate spatially explicit rates of soil erosion by water across military training facilities. One modification involves the RUSLE support practice factor (P factor), which is used to account for the effect of disturbance by human activities on erosion rates. Since disturbance from off-road military vehicular traffic moving through complex landscapes varies spatially, a spatially explicit nonlinear regression model (disturbance model) is used to predict the distribution of P factor values across a training facility. This research analyzes the uncertainty in this model's disturbance predictions for the Fort Hood training facility in order to determine both the spatial distribution of prediction uncertainty and the contribution of different error sources to that uncertainty. This analysis shows that a three-category vegetation map used by the disturbance model was the greatest source of prediction uncertainty, especially for the map categories shrub and tree. In areas mapped as grass, modeling error (uncertainty associated with the model parameter estimates) was the largest uncertainty source. These results indicate that the use of a high-quality vegetation map that is periodically updated to reflect current vegetation distributions, would produce the greatest reductions in disturbance prediction uncertainty.

Conservation of Natural Resources↗

PubMed2006

Improving efficiency of uncertainty analysis in complex integrated assessment models: the case of the RAINS emission module.

Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure.

Air Pollution↗

PubMed2001

Acknowledgment of uncertainty: a fundamental means to ensure scientific and ethical validity in clinical research.

Recognition of the importance of uncertainty in the design of randomized, controlled trials (RCT) has reached the status of a principle. The "uncertainty principle," or less ambiguously, equipoise, holds that a patient should be enrolled in an RCT only if there is substantial uncertainty about which of the trial treatments would benefit the patient most. In fact, the "uncertainty principle" addresses the most important issue of a clinical trial--the choice of an adequate comparative control. Studies in which intervention and control group are believed to be non-equivalent violate the uncertainty principle. Therefore, one would expect that both editors and authors would be particularly careful to include a statement concerning prior beliefs of the investigator(s) about the uncertainty of the treatments that are reported. However, we found no evidence of such a policy in the randomized, controlled trials we examined. We also show that there is a predictable relationship between the uncertainty principle, that is, the moral principle upon which trials are based, and the ultimate outcomes of clinical trials. We postulate that about 50% of innovations are successful, leading to the conclusion that preserving the ethics of clinical research may be the best investment strategy available.

Ethics, Professional↗

PubMed2004

Uncertainties in estimates of the risks of late effects from space radiation.

Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Using the linear-additivity model for radiation risks, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain an estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including a deep space outpost and Mars missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative metrics, e.g., the number of days in space without exceeding a given risk level within well-defined confidence limits.

Aluminum↗

PubMed2005

Sources of uncertainty in model predictions: lessons learned from the IAEA Forest and Fruit Working Group model intercomparisons.

The International Atomic Energy Agency (IAEA), through the BIOMASS program, has provided a unique international forum for assessing the relative contribution of different sources of uncertainty associated with environmental modeling. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte-Carlo modeling are often recommended. The issue of model uncertainty is still rarely addressed in practical applications and the use of several alternative models to derive a range of model outputs (similar to what was done in IAEA model intercomparisons) is one of a few available techniques. This paper addresses the often overlooked issue of what we call 'modeler uncertainty,' i.e., differences in problem formulation, model implementation and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit and Forest Working Groups created under the BIOMASS program (BIOsphere Modeling and ASSessment). The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that the parameter uncertainty (as evaluated by a probabilistic Monte-Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in fate and transport modeling and risk characterization.

Fruit↗

PubMed2006

Combining the cost of reducing uncertainty with the selection of risk assessment models for remediation decision of site contamination.

The decision as to whether a contaminated site poses a threat to human health and should be cleaned up relies increasingly upon the use of risk assessment models. However, the more sophisticated risk assessment models become, the greater the concern with the uncertainty in, and thus the credibility of, risk assessment. In particular, when there are several equally plausible models, decision makers are confused by model uncertainty and perplexed as to which model should be chosen for making decisions objectively. When the correctness of different models is not easily judged after objective analysis has been conducted, the cost incurred during the processes of risk assessment has to be considered in order to make an efficient decision. In order to support an efficient and objective remediation decision, this study develops a methodology to cost the least required reduction of uncertainty and to use the cost measure in the selection of candidate models. The focus is on identifying the efforts involved in reducing the input uncertainty to the point at which the uncertainty would not hinder the decision in each equally plausible model. First, this methodology combines a nested Monte Carlo simulation, rank correlation coefficients, and explicit decision criteria to identify key uncertain inputs that would influence the decision in order to reduce input uncertainty. This methodology then calculates the cost of required reduction of input uncertainty in each model by convergence ratio, which measures the needed convergence level of each key input's spread. Finally, the most appropriate model can be selected based on the convergence ratio and cost. A case of a contaminated site is used to demonstrate the methodology.

Decision Making↗

PubMed2005

Quantifying and reducing uncertainty in life cycle assessment using the Bayesian Monte Carlo method.

The traditional life cycle assessment (LCA) does not perform quantitative uncertainty analysis. However, without characterizing the associated uncertainty, the reliability of assessment results cannot be understood or ascertained. In this study, the Bayesian method, in combination with the Monte Carlo technique, is used to quantify and update the uncertainty in LCA results. A case study of applying the method to comparison of alternative waste treatment options in terms of global warming potential due to greenhouse gas emissions is presented. In the case study, the prior distributions of the parameters used for estimating emission inventory and environmental impact in LCA were based on the expert judgment from the intergovernmental panel on climate change (IPCC) guideline and were subsequently updated using the likelihood distributions resulting from both national statistic and site-specific data. The posterior uncertainty distribution of the LCA results was generated using Monte Carlo simulations with posterior parameter probability distributions. The results indicated that the incorporation of quantitative uncertainty analysis into LCA revealed more information than the deterministic LCA method, and the resulting decision may thus be different. In addition, in combination with the Monte Carlo simulation, calculations of correlation coefficients facilitated the identification of important parameters that had major influence to LCA results. Finally, by using national statistic data and site-specific information to update the prior uncertainty distribution, the resultant uncertainty associated with the LCA results could be reduced. A better informed decision can therefore be made based on the clearer and more complete comparison of options.

Bayes Theorem↗

PubMed2002

The Intolerance of Uncertainty Scale: psychometric properties of the English version.

Research is now suggesting that intolerance of uncertainty may be very important in understanding worry and may play a key role in the etiology and maintenance of worry. The present study attempted to further our understanding of intolerance of uncertainty by examining the psychometric properties of the English version of the Intolerance of Uncertainty Scale (IUS), which has already been validated in French. Factor analysis indicated that the IUS has a four-factor structure that represents the idea that uncertainty is stressful and upsetting, uncertainty leads to the inability to act, uncertain events are negative and should be avoided, and being uncertain is unfair. The IUS has excellent internal consistency, good test-retest reliability over a five-week period, and convergent and divergent validity when assessed with symptom measures of worry, depression, and anxiety. Overall, this study suggests that the IUS is a sound measure of intolerance of uncertainty and supports the idea that intolerance of uncertainty is an important construct involved in worry.

Adult↗

PubMed2001

Uncertainty and variability in risk from trophic transfer of contaminants in dredged sediments.

The risks associated with bioaccumulative contaminants must be considered when evaluating dredged material disposal alternatives. The bioaccumulation of organochlorines and other contaminants by higher trophic level organisms represents one of the most significant sources of uncertainty in risk assessment. Both population variability (e.g. true population heterogeneity in body weight, lipid content, etc.) and uncertainty (e.g. measurement error) in trophic transfer can lead to large errors in predicted risk values for ecological receptors. This paper describes and quantitatively evaluates sources of uncertainty and variability in estimating the risk to an ecological receptor (osprey) from the trophic transfer of polychlorinated biphenyls (PCBs) in sediments from the New York-New Jersey (NY-NJ) Harbor. The distribution of toxicity quotients is obtained using a food chain model for the osprey and specifying distributions for input parameters, which are disaggregated to represent either uncertainty or variability. PCB concentrations in sediment and water are treated as predominantly uncertain, whereas lipid content in fish, feeding preferences, and fish weight are assumed to contribute primarily to population variability in PCB accumulation. The analysis shows that point estimates of reasonable maximum exposure (RME) exceed the uncertainty bounds on the 95th percentile of variability. The analysis also shows that uncertainties in the sediment and water contaminant concentrations contribute more to the range of risk estimates than does the variability in the population exposure parameters. The separation of uncertainty and variability in food chain models can help to support management decisions regarding dredged material disposal by providing a quantitative expression of the confidence in ecological risk estimates. A rationale is provided for the distinction between uncertain and variable parameters based on management goals and data availability.

Animals↗

PubMedDate not supplied

Uncertainty and psychologic stress after coronary angioplasty and coronary bypass surgery.

OBJECTIVE: To examine the trajectory of uncertainty and symptoms of psychologic stress during the first 3 months after coronary angioplasty and coronary bypass surgery and to study the impact of social support on uncertainty and psychologic stress in these patient populations. DESIGN: Descriptive, correlative. SETTING: Urban community, at-home interviews. PATIENTS: Male patients with angioplasty (n = 22) and bypass (n = 25) at 1 and 3 months after treatment. Age range was 39 to 75 years (mean 58 years). OUTCOME MEASURES: Mishel Uncertainty in Illness scores, General Health Questionnaire scores, and Perceived Social Support Scale scores. INTERVENTION: Patients underwent either percutaneous transluminal coronary angioplasty or coronary artery bypass grafting as treatment for coronary artery disease. RESULTS: Data were analyzed with repeated measures analysis of variance and Pearson correlation coefficients. Results showed that at both time periods angioplasty patients were more uncertain than bypass patients (p < 0.05), and that regardless of procedure, patients reported fewer symptoms of psychologic stress at 3 months than at 1 month (p < 0.01). Patients with high social support had less uncertainty and psychologic stress than patients with low support (p < 0.05). Analysis of the social support and treatment group interaction showed that angioplasty patients with low perceived social support had significantly more psychologic stress than angioplasty patients with high support (p < 0.01). Analysis of the correlations between uncertainty and psychologic stress in the angioplasty and bypass grafting procedure groups after control for social support revealed that social support was a significant mediator of the relationship between uncertainty and stress only among patients undergoing percutaneous transluminal coronary angioplasty. There was little evidence of a mediating role for social support in the coronary artery bypass grafting group. CONCLUSIONS: These results suggest that angioplasty patients may be in particular need of interventions aimed at reducing uncertainty, and that interventions that increase social support could be important in achieving this reduction.

Adult↗