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Results for “Uncertainty Quantification”

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PubMed2001

Climate change. Uncertainty and climate change assessments.

Clear and quantitative discussion of uncertainties is critical for public policy making on climate change. The recently completed report of the Intergovernmental Panel on Climate Change assessed the uncertainty in its findings and forecasts. The uncertainty assessment process of the IPCC should be improved in the future by using a consistent approach to quantifying uncertainty, focusing the quantification on the few key results most important for policy making. The uncertainty quantification procedure should be fully documented, and if expert judgment is used, a specific list of the experts consulted should be included.

Journal Article↗

PubMed2001

Use of computational fluid dynamics models for dosimetry of inhaled gases in the nasal passages.

Computational fluid dynamics (CFD) models of the nasal passages of a rat, monkey, and human are being used (1) to determine important factors affecting nasal uptake, (2) to make interspecies dosimetric comparisons, (3) to provide detailed anatomical information for the rat, monkey, and human nasal passages, and (4) to provide estimates of regional air-phase mass transport coefficients (a measure of the resistance to gas transport from inhaled air to airway walls) in the nasal passages of all three species. For many inhaled materials, lesion location in the nose follows patterns that are both site and species specific. For reactive, water-soluble (Category 1) gases, regional uptake can be a major factor in determining lesion location. Since direct measurement of airflow and uptake is experimentally difficult, CFD models are used here to predict uptake patterns quantitatively in three-dimensional reconstructions of the F344 rat, rhesus monkey, and human nasal passages. In formaldehyde uptake simulations, absorption processes were assumed to be as rapid as possible, and regional flux (transport rate) of inhaled formaldehyde to airway walls was calculated for rats, primates, and humans. For uptake of gases like vinyl acetate and acrylic acid vapors, physiologically based pharmacokinetic uptake models incorporating anatomical and physical information from the CFD models were developed to estimate nasal tissue dose in animals and humans. The use of biologically based models in risk assessment makes sources of uncertainty explicit and, in doing so, allows quantification of uncertainty through sensitivity analyses. Limited resources can then be focused on reduction of important sources of uncertainty to make risk estimates more accurate.

Animals↗

PubMed2004

Uncertainties in dose coefficients from ingestion of 131I, 137Cs, and 90Sr.

Quantification of uncertainties in doses from intakes of radionuclides is important in risk assessments and epidemiologic studies of individuals exposed to radiation. In this study, the uncertainties in the doses per unit intake (i.e., dose coefficients) for ingestion of 131I, 137Cs, and 90Sr by healthy individuals have been determined. Age-dependent thyroid dose coefficients were derived for 131I. The analysis for 131I uses recent measurements of thyroid volume obtained by ultrasonography, which indicate a thyroid mass lower than that previously obtained using autopsy measurements. The coefficients for 137Cs are determined using the relationship between the biological half-lives and the amount of potassium in the human body. The most recent International Commission on Radiological Protection biokinetic model was employed to determine the uncertainties for 90Sr. For 137Cs and 90Sr, the dose coefficients represent exposure in adulthood and they were determined for all organs of radiological importance. The uncertainty in the estimated dose coefficients represent state of knowledge estimates for a reference individual, and they are described by lognormal distributions with a specified geometric mean (GM) and geometric standard deviation (GSD). The estimated geometric means vary only slightly from the dose coefficients reported by ICRP publications. The largest uncertainty is observed in the dose coefficients for bone surface (GSD = 2.6), and red bone marrow (GSD = 2.4) in the case of ingestion of 90Sr. For most other organs, the uncertainty in the 90Sr dose coefficients is characterized by a GSD of 1.8 (or less for some organs). For 131I, the uncertainty in the thyroid dose coefficients is well represented by a GSD of 1.7 for both sexes and all ages other than infants for whom a GSD of 1.8 is more appropriate. The lowest uncertainties are obtained for the dose coefficients from ingestion of 137Cs (GSD = 1.24 for males; 1.4 for females). A dominant source of uncertainty in the ingestion dose coefficients is the variation of the biokinetic parameters. For 131I, the largest contribution to the uncertainty comes from the variation in the thyroid mass, but the contribution of the biokinetic parameters is comparable. The biokinetic parameters with the largest contribution to the uncertainty are (a) the fractional uptake from blood to thyroid in the case of ingestion of 131I, (b) the absorbed fraction from the gastrointestinal tract (f1) in the case of 90Sr, and (c) the amount of potassium in the body for 137Cs. The contribution to the uncertainty of the absorbed fraction (which accounts for the fraction of energy deposited in the target organ) is the smallest contributor to the uncertainty in the dose coefficients for most organs. To reduce the uncertainty in the dose estimated for a real individual, one should determine the above-mentioned parameters for the specified individual rather than to rely on assumptions for a reference individual.

Adolescent↗

PubMed2004

Quantification of variability and uncertainty for air toxic emission inventories with censored emission factor data.

Probabilistic emission inventories were developed for urban air toxic emissions of benzene, formaldehyde, chromium, and arsenic for the example of Houston. Variability and uncertainty in emission factors were quantified for 71-97% of total emissions, depending upon the pollutant and data availability. Parametric distributions for interunit variability were fit using maximum likelihood estimation (MLE), and uncertainty in mean emission factors was estimated using parametric bootstrap simulation. For data sets containing one or more nondetected values, empirical bootstrap simulation was used to randomly sample detection limits for nondetected values and observations for sample values, and parametric distributions for variability were fit using MLE estimators for censored data. The goodness-of-fit for censored data was evaluated by comparison of cumulative distributions of bootstrap confidence intervals and empirical data. The emission inventory 95% uncertainty ranges are as small as -25% to +42% for chromium to as large as -75% to +224% for arsenic with correlated surrogates. Uncertainty was dominated by only a few source categories. Recommendations are made for future improvements to the analysis.

Air Pollutants↗

PubMed2002

Quantification of variability and uncertainty in lawn and garden equipment NOx and total hydrocarbon emission factors.

Variability refers to real differences in emissions among multiple emission sources at any given time or over time for any individual emission source. Variability in emissions can be attributed to variation in fuel or feedstock composition, ambient temperature, design, maintenance, or operation. Uncertainty refers to lack of knowledge regarding the true value of emissions. Sources of uncertainty include small sample sizes, bias or imprecision in measurements, nonrepresentativeness, or lack of data. Quantitative methods for characterizing both variability and uncertainty are demonstrated and applied to case studies of emission factors for lawn and garden (L&G) equipment engines. Variability was quantified using empirical and parametric distributions. Bootstrap simulation was used to characterize confidence intervals for the fitted distributions. The 95% confidence intervals for the mean grams per brake horsepower/hour (g/hp-hr) emission factors for two-stroke engine total hydrocarbon (THC) and NOx emissions were from -30 to +41% and from -45 to +75%, respectively. The confidence intervals for four-stroke engines were from -33 to +46% for THCs and from -27 to +35% for NOx. These quantitative measures of uncertainty convey information regarding the quality of the emission factors and serve as a basis for calculation of uncertainty in emission inventories (EIs).

Air Pollutants, Occupational↗

PubMed2025

vcfgl: a flexible genotype likelihood simulator for VCF/BCF files.

MOTIVATION: Accurate quantification of genotype uncertainty is pivotal in ensuring the reliability of genetic inferences drawn from NGS data. Genotype uncertainty is typically modeled using Genotype Likelihoods (GLs), which can help propagate measures of statistical uncertainty in base calls to downstream analyses. However, the effects of errors and biases in the estimation of GLs, introduced by biases in the original base call quality scores or the discretization of quality scores, as well as the choice of the GL model, remain under-explored. RESULTS: We present vcfgl, a versatile tool for simulating genotype likelihoods associated with simulated read data. It offers a framework for researchers to simulate and investigate the uncertainties and biases associated with the quantification of uncertainty, thereby facilitating a deeper understanding of their impacts on downstream analytical methods. Through simulations, we demonstrate the utility of vcfgl in benchmarking GL-based methods. The program can calculate GLs using various widely used genotype likelihood models and can simulate the errors in quality scores using a Beta distribution. It is compatible with modern simulators such as msprime and SLiM, and can output data in pileup, Variant Call Format (VCF)/BCF, and genomic VCF file formats, supporting a wide range of applications. The vcfgl program is freely available as an efficient and user-friendly software written in C/C++. AVAILABILITY AND IMPLEMENTATION: vcfgl is freely available at https://github.com/isinaltinkaya/vcfgl.

Software↗

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

Uncertainty in liquid chromatographic analysis of pharmaceutical product: influence of various uncertainty sources.

An ISO GUM measurement uncertainty estimation procedure was developed for a liquid-chromatographic drug quality control method-assay of simvastatin in drug formulation. In quantification of uncertainty components several practical approaches for including difficult-to-estimate uncertainty sources (such as uncertainty due to peak integration, uncertainty due to nonlinearity of the calibration curve, etc.) have been presented. Detailed analysis of contributions of the various uncertainty sources was carried out. The results were calculated based on different definitions of the measurand and it was demonstrated that unequivocal definition of the measurand is essential in order to get rigorous uncertainty estimate. Two different calibration methods - single-point (1P) and five-point (5P) - were used and the obtained uncertainties and uncertainty budgets were compared. Results calculated using 1P and 5P calibrations agree very well. The uncertainty estimate for 1P is only slightly larger than with 5P calibration.

Calibration↗

PubMed2026

Development and validation of a liquid chromatography-tandem mass spectrometry method for the quantification of twenty-five steroids in equine serum.

Steroids are potential biomarkers for monitoring equine pregnancy. However, immunoassays currently used for their quantification suffer from cross-reactivity and limited specificity, thus requiring more accurate methods. This study reports the development and validation of a robust liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for simultaneous quantification of 25 steroids covering the main biosynthetic pathways of progestogens, corticosteroids, androgens, and estrogens. Steroids were extracted by protein precipitation followed by evaporation, derivatization, and reconstitution before LC-MS/MS analysis. A surrogate matrix was used for calibration and validation to avoid endogenous interference. Validation was performed according to and partly adapted from Clinical and Laboratory Standards Institute guidelines (CLSI), including linearity, trueness, precision, limits of detection and quantification, measurement uncertainty, recovery, matrix effects, carryover, selectivity, and stability. Calibration curves were fitted using the best-performing weighted linear or quadratic regression model, yielding excellent linearity (R2 > 0.990), trueness between -9.0% and 2.3%, and intra- and inter-day precision <6.3%. Lower limits of quantification ranged from 2.07 to 2250 pg/mL depending on physiological analytes concentration. Extraction recovery averaged 24.3-114.9%, matrix effects were acceptable, and accuracy ranged from 94.4% to 98.9%. No carryover or interferences were detected. Measurement uncertainty remained <15%. This study presents the first LC-MS/MS method partially validated per CLSI criteria for the quantification of 24 steroids in equine serum. The method offers a sensitive and specific alternative to immunoassays and provides a robust tool for equine steroid profiling with potential applications in pregnancy monitoring, placentitis diagnosis, and fetal sex determination.

Animals↗

PubMed1994

Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability.

In quantitative uncertainty analysis, it is essential to define rigorously the endpoint or target of the assessment. Two distinctly different approaches using Monte Carlo methods are discussed: (1) the end point is a fixed but unknown value (e.g., the maximally exposed individual, the average individual, or a specific individual) or (2) the end point is an unknown distribution of values (e.g., the variability of exposures among unspecified individuals in the population). In the first case, values are sampled at random from distributions representing various "degrees of belief" about the unknown "fixed" values of the parameters to produce a distribution of model results. The distribution of model results represents a subjective confidence statement about the true but unknown assessment end point. The important input parameters are those that contribute most to the spread in the distribution of the model results. In the second case, Monte Carlo calculations are performed in two dimensions producing numerous alternative representations of the true but unknown distribution. These alternative distributions permit subject confidence statements to be made from two perspectives: (1) for the individual exposure occurring at a specified fractile of the distribution or (2) for the fractile of the distribution associated with a specified level of individual exposure. The relative importance of input parameters will depend on the fractile or exposure level of interest. The quantification of uncertainty for the simulation of a true but unknown distribution of values represents the state-of-the-art in assessment modeling.

Humans↗

PubMed2001

The death of cost-minimization analysis?

Four different types of evaluation methods, cost-benefit analysis (CBA), cost-utility analysis (CUA), cost-effectiveness analysis (CEA) and cost-minimization analysis (CMA), are usually distinguished. In this note, we pronounce the (near) death of CMA by showing the rare circumstances under which CMA is an appropriate method of analysis. We argue that it is inappropriate for separate and sequential hypothesis tests on differences in effects and costs to determine whether incremental cost-effectiveness (or cost-utility) should be estimated. We further argue that the analytic focus should be on the estimation of the joint density of cost and effect differences, the quantification of uncertainty surrounding the incremental cost-effectiveness ratio and the presentation of such data as cost-effectiveness acceptability curves. Two examples from recently published CEA are employed to illustrate the issues. The first shows a situation where analysts might be tempted (inappropriately) to employ CMA rather than CEA. The second illustrates one of the rare circumstances in which CMA may be justified as a legitimate form of analysis.

Anticoagulants↗

PubMed1998

Toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin to several ecological receptor groups: a short review.

Toxicity among TCDD isomers can vary significantly. Isomers containing four to six chlorine atoms possess the greatest potential for biological activity, as well as the highest acute toxicity. Information available in the literature focuses largely on fish and terrestrial mammal studies. Toxicity data for aquatic invertebrates and plants is wanting, and essentially no studies pertaining to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) accumulation in these organisms could be found. No data were procurable regarding TCDD toxicity in marine mammals or reptiles. Current knowledge is not sufficient to accurately characterize TCDD toxicity in ecological receptors, with quantification of uncertainty being particularly difficult given the limited base of knowledge.

Animals↗

PubMed2006

Rapid headspace solid-phase microextraction/gas chromatographic/mass spectrometric assay for the quantitative determination of some of the main odorants causing off-flavours in wine.

In this study we present a rapid and simultaneous assay method using headspace (HS) solid-phase microextraction (SPME)/gas chromatography (GC)/electron impact (EI) mass spectrometry (MS) (selected ion monitoring) for contaminants causing the principal organoleptic defects of wine (2,4,6-trichloroanisole, 2,3,4,6-tetrachloroanisole, pentachloroanisole, 2,4,6-tribromoanisole, 1-octen-3-ol, geosmin, 2-methylisoborneol, 3-isopropyl-2-methoxypyrazine, fenchol, fenchone, 2-methoxy-3,5-dimethylpyrazine, 4-ethylphenol, 4-ethylguaiacol, 4-vinylphenol, 4-vinylguaiacol, 3-isobutyl-2-methoxypyrazine, guaiacol and ethyl acetate). The method was validated according to protocols NF ISO 5725-1, 2 and NF V03-110. Its characteristics (limit of detection (LOD), limit of quantification (LOQ), uncertainties) were determined after having optimised the SPME parameters. The target contaminants were quantified in the wines below their threshold of perception with a satisfactory relative standard deviation for all the analytes except ethyl acetate (RSD=36%); for that, the assay method permits clear differentiation of the wines that are at risk of presenting an acescent character, i.e. containing more than 120mgL(-1) ethyl acetate. The target volatile and odorous substances were determined at concentrations significantly below their threshold of perception in a hydroalcoholic context and their threshold of recovery in wines.

Acetates↗

PubMed2005

A probabilistic effect assessment model for hazardous substances at the workplace.

A major problem in risk assessment is the quantification of uncertainties. A probabilistic model was developed to consider uncertainties in the effect assessment of hazardous substances at the workplace. Distributions for extrapolation factors (time extrapolation, inter- and intraspecies extrapolation) were determined on the basis of appropriate empirical data. Together with the distribution for the benchmark dose obtained from substance-specific dose-response modelling for the exemplary substances 2,4,4-trimethylpentene (TMP) and aniline, they represent the input distributions for probabilistic modelling. These distributions were combined by Monte Carlo simulation. The resulting target distribution describes the probability that an aspired protection level for workers is achieved at a certain dose and the uncertainty associated with the assessment. In the case of aniline, substance-specific data on differences in susceptibility (between species; among humans due to genetic polymorphisms of N-acetyltransferase) were integrated in the model. Medians of the obtained target distributions of the basic models for TMP and aniline, but not of the specific aniline model are similar to deterministically derived reference values. Differences of more than one order of magnitude between the medians and the 5th percentile of the target distributions indicate substantial uncertainty associated with the effect assessment of these substances. The probabilistic effect assessment model proves to be a practical tool to integrate quantitative information on uncertainty and variability in hazard characterisation.

Alkenes↗

PubMed1997

Secondary endpoints cannot be validly analyzed if the primary endpoint does not demonstrate clear statistical significance.

There is lack of consensus surrounding the interpretation of observed treatment effects for secondary clinical endpoints when the primary endpoint for which the clinical trial was initially designed does not meet the objective of a demonstrated effect. We provide some arguments to support caution in making inferences for secondary endpoints in this situation. We examine the definitions of primary and secondary endpoints within the context of a hypothesis-testing framework for multiple endpoints, and we address the relationship of the correlation structure of these endpoints and the statistical adjustments needed to preserve experiment-wise type I error for a valid inference. We also address the hypothesis-testing framework and the estimation framework for valid inference, focusing on the interpretation of p-values associated with differentially powered hypothesis tests for each endpoint to detect an important clinical effect. We point out the limitations on the strength of evidence (and quantification of uncertainty) for a secondary endpoint effect that can be derived from only one study and introduce the likelihood of replication of the finding in another study of identical size and design as a useful concept to guide this interpretation.

Clinical Trials as Topic↗

PubMed2000

[Methodological problems in health-environment ecological studies].

Different approaches are used to study the effects of the environment on health. We restrict our discussion to observational studies at the aggregated level, the so-called ecological studies. We discuss several sources of bias for group-level studies and consider questions relating to the link between individual-level and group-level dose-effect relationship, the difference between group exposure and environmental exposure, and the influence of measurement error and variability in the exposure. Taking into consideration confounding factors in the analyses is another important item that is discussed. A final item concerns the necessity of studying the temporal direction of the effect, as well as assessing the existence of a potential threshold in the effect. As a broad conclusion, we can say that realistic quantification of uncertainty in dose-effect relationships is a delicate task that requires the systematic consideration of all sources of variability, as well as a transparent sensitivity analysis of the choices and hypotheses made during the statistical analysis.

Bias↗

PubMed2004

Antimicrobial resistance: a microbial risk assessment perspective.

The emergence of antimicrobial-resistant microorganisms in both humans and food animals is a growing concern. Debate has centred on links between antimicrobial use in the production of food animals and the emergence of resistant organisms in the human population. Consequently, microbial risk assessment (MRA) is being used to facilitate scientific investigations of the risks related to the food chain, including quantification of uncertainty and prioritization of control strategies. MRA is a scientific tool that can be used to evaluate the level of exposure and the subsequent risk to human health relating to a specific organism or particular type of resistance. This paper reviews the recent applications of MRA in the area of antimicrobial resistance, and in particular, it focuses on the methods, assumptions and data limitations. Since MRA outputs are dependent on the quality of data inputs used in their development, we aim to promote the generation of good quality data by describing the properties that data should ideally possess for MRA and by highlighting the benefit of data generation specifically for inclusion in MRAs.

Animals↗

PubMed2005

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates.

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (gamma, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of gamma near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

Arachis↗