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Results for “Uncertainty modelling”

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PubMed1997

Contrast discrimination function: spatial cuing effects.

The effects of spatial cuing were measured for discrimination between an increment and a decrement on a target's pedestal contrast. Discrimination thresholds measured in the absence of a spatial cue were always higher than corresponding thresholds measured in the presence of a spatial cue, except when pedestal contrast was near zero. Uncued discrimination thresholds rose monotonically with pedestal contrast; cued discrimination thresholds formed a dipper function of pedestal contrast. A spatial-uncertainty model incorporating a nonlinear transducer produced similar results.

Contrast Sensitivity↗

PubMed1998

Cost analysis of the treatment of schizophrenia in the UK: a comparison of olanzapine and haloperidol.

A decision-tree simulation model is used to examine the costs associated with olanzapine versus haloperidol in the treatment of patients with schizophrenia in the UK. Parameter values and outcome scores were derived mainly from an international clinical trial. Resource consequences were examined on the basis of assumed service delivery and actual unit costs specific to the UK. While olanzapine is more expensive to prescribe than haloperidol, it generates savings by reducing utilisation of medical services. As a result, a comparison of the 2 drugs is approximately cost neutral. Model uncertainties are examined using extensive sensitivity analysis; in most scenarios, cost-neutral results are maintained. Olanzapine is more effective than haloperidol as measured by Brief Psychiatric Rating Scale scores and non-relapse rates. With such gains in effectiveness and near equivalence in terms of costs, olanzapine, in comparison with haloperidol, may represent a cost-effective treatment option.

Benzodiazepines↗

PubMed2026

Population-scale detection of methylation outliers from long-read genome sequencing.

BACKGROUND: Aberrant DNA methylation can mediate the functional effects of rare genetic variation and contribute to imprinting disorders, repeat expansion diseases, and other pathogenic regulatory mechanisms. Long-read sequencing technologies now enable genome-wide detection of CpG methylation alongside genetic variation from a single assay. However, methods for systematic identification and interpretation of methylation outliers from long-read sequencing data remain limited. METHODS: We developed METAFORA, a computational workflow for detecting methylation outlier regions from PacBio and Oxford Nanopore long-read sequencing data. METAFORA constructs population-level methylation references, segments the genome into correlated CpG blocks, infers technical and biological sources of variation through hidden factor estimation, models uncertainty due to variable depth sequencing, and computes covariate-adjusted methylation outlier scores for individual samples. We applied METAFORA across large long-read sequencing cohorts and integrated methylation outliers with multi-omic data. METAFORA is implemented as a snakemake workflow available at https://github.com/tjense25/METAFORA. RESULTS: METAFORA identified methylation outlier regions associated with rare structural variants, tandem repeat expansions, and imprinting abnormalities. We found outlier regions were enriched for molecular outliers across transcriptomic and chromatin accessibility datasets, supporting their functional relevance in gene regulation. In a representative case, METAFORA identified an imprinting defect affecting the GNAS locus associated with an STX16 deletion. CONCLUSIONS: METAFORA enables scalable detection and interpretation of methylation outliers from long-read sequencing data and provides a framework for integrating epigenetic outliers with genomic and multi-omic analyses. These approaches may improve interpretation of rare regulatory variation and support discovery of clinically relevant epigenetic abnormalities in genomic medicine.

DNA methylation↗

PubMed2000

PSG-EXPERT. An expert system for the diagnosis of sleep disorders.

This paper describes PSG-EXPERT, an expert system in the domain of sleep disorders exploring polysomnographic data. The developed software tool is addressed from two points of view: (1)--as an integrated environment for the development of diagnosis-oriented expert systems; (2)--as an auxiliary diagnosis tool in the particular domain of sleep disorders. Developed over a Windows platform, this software tool extends one of the most popular shells--CLIPS (C Language Integrated Production System) with the following features: backward chaining engine; graph-based explanation facilities; knowledge editor including a fuzzy fact editor and a rules editor, with facts-rules integrity checking; belief revision mechanism; built-in case generator and validation module. It therefore provides graphical support for knowledge acquisition, edition, explanation and validation. From an application domain point of view, PSG-Expert is an auxiliary diagnosis system for sleep disorders based on polysomnographic data, that aims at assisting the medical expert in his diagnosis task by providing automatic analysis of polysomnographic data, summarising the results of this analysis in terms of a report of major findings and possible diagnosis consistent with the polysomnographic data. Sleep disorders classification follows the International Classification of Sleep Disorders. Major features of the system include: browsing on patients data records; structured navigation on Sleep Disorders descriptions according to ASDA definitions; internet links to related pages; diagnosis consistent with polysomnographic data; graphical user-interface including graph-based explanatory facilities; uncertainty modelling and belief revision; production of reports; connection to remote databases.

Computer Systems↗

PubMed1998

Assessing uncertainty in microsimulation modelling with application to cancer screening interventions.

Microsimulation is fast becoming the approach of choice for modelling and analysing complex processes in the absence of mathematical tractability. While this approach has been developed and promoted in engineering contexts for some time, it has more recently found a place in the mainstream of the study of chronic disease interventions such as cancer screening. The construction of a simulation model requires the specification of a model structure and sets of parameter values, both of which may have a considerable amount of uncertainty associated with them. This uncertainty is rarely quantified when reporting micro-simulation results. We suggest a Bayesian approach and assume a parametric probability distribution to mathematically express the uncertainty related to model parameters. First, we design a simulation experiment to achieve good coverage of the parameter space. Second, we model a response surface for the outcome of interest as a function of the model parameters using the simulation results. Third, we summarize the variability in the outcome of interest, including variation due to parameter uncertainty, using the response surface in combination with parameter probability distributions. We illustrate the proposed method with an application of a microsimulator designed to investigate the effect of prostate specific antigen (PSA) screening on prostate cancer mortality rates.

Adult↗

PubMedDate not supplied

Markov chain Monte Carlo estimation of a multiparameter decision model: consistency of evidence and the accurate assessment of uncertainty.

Decision models are usually populated 1 parameter at a time, with 1 item of information informing each parameter. Often, however, data may not be available on the parameters themselves but on several functions of parameters, and there may be more items of information than there are parameters to be estimated. The authors show how in these circumstances all the model parameters can be estimated simultaneously using Bayesian Markov chain Monte Carlo methods. Consistency of the information and/or the adequacy of the model can also be assessed within this framework. Statistical evidence synthesis using all available data should result in more precise estimates of parameters and functions of parameters, and is compatible with the emphasis currently placed on systematic use of evidence. To illustrate this, WinBUGS software is used to estimate a simple 9-parameter model of the epidemiology of HIV in women attending prenatal clinics, using information on 12 functions of parameters, and to thereby compute the expected net benefit of 2 alternative prenatal testing strategies, universal testing and targeted testing of high-risk groups. The authors demonstrate improved precision of estimates, and lower estimates of the expected value of perfect information, resulting from the use of all available data.

AIDS Serodiagnosis↗

PubMedDate not supplied

Uncertainty in decision models analyzing cost-effectiveness: the joint distribution of incremental costs and effectiveness evaluated with a nonparametric bootstrap method.

PURPOSE: To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. METHODS: The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous transluminal angioplasty with bypass surgery for femoropopliteal lesions. Each probability in the model was simulated with a first-order Monte Carlo simulation to represent sampling uncertainty. Superimposed on this, a second-order Monte Carlo simulation was performed to represent parameter uncertainty, drawing the probability values from nonparametric distributions based on published data or from primary collected data as available. After simulation of a mixed (i.e., non-identical) cohort of 30,000 patients, 3,000 bootstrap samples of 1,000 patients each were drawn and the joint distribution of mean incremental costs and mean effectiveness gained was evaluated. RESULTS: Using a bootstrap sample size of 1,000 patients, 92.7% of the joint distribution of mean incremental costs and mean effectiveness gained fell in the quadrant where angioplasty dominated bypass surgery. Another 6.9% of samples demonstrated either greater effectiveness with an incremental cost-effectiveness ratio of at most $20,000/QALY gained, or cost savings with a ratio of at least $20,000 saved/QALY lost. CONCLUSION: A nonparametric bootstrap method can be used to estimate the joint distribution of mean incremental costs and mean effectiveness gained, and the results can provide an understanding of the uncertainty in a cost-effectiveness analysis based on a decision model.

Angioplasty, Balloon↗

PubMed2002

[Parameters identification and uncertainty analysis for environmental model].

This paper examined a case study of hydrological model for identifying parameter uncertainty by using three sensitivity analysis methods: HSY algorithm, linear regressional method and coupling analysis method. The results showed that optimal algorithms cannot give a sound explanation for complexity of model structure and identifying model parameters via uncertainty analysis methods presented an effective alternative to understand model system.

Algorithms↗

PubMed2007

Pricing matrix model: dealing with uncertainty.

A previous publication in this journal showed that the pricing matrix model (PMM) allows an assessment of the pricing potential of a new innovative product. When the PMM is going to be used for the determination of a drug price for a new drug in the strategic product planning process, it is important that this methodology is reliable. In the initial paper the PMM only yielded an expected price for the new antidepressant without generating an estimate of the probability that the new drug would indeed be listed at this expected price. In this manuscript we present various methodologies to deal with uncertainty in the PMM. We introduce the concept of price acceptability curves. The conclusion of this paper is that the incorporation of uncertainty into the PMM will lead to a more accurate assessment of the pricing potential of a new drug.

Antidepressive Agents↗

PubMed2004

The importance of accounting for the uncertainty of published prognostic model estimates.

OBJECTIVES: Reported is the importance of properly reflecting uncertainty associated with prognostic model estimates when calculating the survival benefit of a treatment or technology, using liver transplantation as an example. METHODS: Monte Carlo simulation techniques were used to account for the uncertainty of prognostic model estimates using the standard errors of the regression coefficients and their correlations. These methods were applied to patients with primary biliary cirrhosis undergoing liver transplantation using a prognostic model from a historic cohort who did not undergo transplantation. The survival gain over 4 years from transplantation was estimated. RESULTS: Ignoring the uncertainty in the prognostic model, the estimated survival benefit of liver transplantation was 16.7 months (95 percent confidence interval [CI], 13.5 to 20.1), and was statistically significant (p < .001). After adjusting for model uncertainty using the standard errors of the regression coefficients, the estimated survival benefit was 17.5 months (95 percent CI, -3.9 to 38.5) and was no longer statistically significant. An additional adjustment for the correlation between regression coefficients widened the 95 percent confidence interval slightly: the estimated survival benefit was 17.0 months (95 percent CI: -4.6 to 38.6). CONCLUSIONS: It is important that the precision of regression coefficients is available for users of published prognostic models. Ignoring this additional information substantially underestimates uncertainty, which can then impact misleadingly on policy decisions.

Cohort Studies↗

PubMed2005

Combining quantitative and qualitative measures of uncertainty in model-based environmental assessment: the NUSAP system.

This article discusses recent experiences with the Numeral Unit Spread Assessment Pedigree (NUSAP) system for multidimensional uncertainty assessment, based on four case studies that vary in complexity. We show that the NUSAP method is applicable not only to relatively simple calculation schemes but also to complex models in a meaningful way and that NUSAP is useful to assess not only parameter uncertainty but also (model) assumptions. A diagnostic diagram can be used to synthesize results of quantitative analysis of parameter sensitivity and qualitative review (pedigree analysis) of parameter strength. It provides an analytic tool to prioritize uncertainties according to quantitative and qualitative insights in the limitations of available knowledge. We show that extension of the pedigree scheme to include societal dimensions of uncertainty, such as problem framing and value-laden assumptions, further promotes reflexivity and collective learning. When used in a deliberative setting, NUSAP pedigree assessment has the potential to foster a deeper social debate and a negotiated management of complex environmental problems.

Computer Simulation↗

PubMed2006

Assessment of uncertainty in a probabilistic model of consumer exposure to pesticide residues in food.

The assessment of consumer exposure to pesticides is an important part of pesticide regulation. Probabilistic modelling allows analysis of uncertainty and variability in risk assessments. The output of any assessment will be influenced by the characteristics and uncertainty of the inputs, model structure and assumptions. While the use of probabilistic models is well established in the United States, in Europe problems of low acceptance, sparse data and lack of guidelines are slowing the development. The analyses in the current paper focused on the dietary pathway and the exposure of UK toddlers. Three single food, single pesticide case studies were used to parameterize a simple probabilistic model built in Crystal Ball. Data on dietary consumption patterns were extracted from National Diet and Nutrition Surveys, and levels of pesticide active ingredients in foods were collected from Pesticide Residues Committee monitoring. The effect of uncertainty on the exposure estimate was analysed using scenarios, reflecting different assumptions related to sources of uncertainty. The most influential uncertainty issue was the distribution type used to represent input variables. Other sources that most affected model output were non-detects, unit-to-unit variability and processing. Specifying correlation between variables was found to have little effect on exposure estimates. The findings have important implications for how probabilistic modelling should be conducted, communicated and used by policy and decision makers as part of consumer risk assessment of pesticides.

Body Weight↗

PubMed1997

A Markov model for HIV disease progression including the effect of HIV diagnosis and treatment: application to AIDS prediction in England and Wales.

Back-calculation is a widely used method to estimate HIV incidence rates, and is commonly based on times of AIDS diagnosis. Following up earlier work, we extend this method to also incorporate knowledge of times of HIV diagnosis (first positive test). This is achieved through the use of a Markov model which describes the progress of an HIV infected person through various stages, and which allows causal connections between events to be explicitly modelled. Estimation is based on maximum likelihood, the likelihood being calculated within a discretized version of the Markov model. The effect of sampling uncertainty and model uncertainty (sensitivity) is evaluated simultaneously by means of a combined bootstrap and simulation procedure. At each replication we resample both the data and the model (from a set of possible models described by randomizing one or more parameters). For instance, uncertain knowledge about the incubation distribution affects the estimates of some parameters, but not others. The Markov approach is applied to the prediction of AIDS incidence for homosexuals in England and Wales up to the year 2000.

AIDS Serodiagnosis↗

PubMed2006

A stochastic regression approach to analyzing thermodynamic uncertainty in chemical speciation modeling.

Chemical speciation modeling is a vital tool for assessing the bioavailability of inorganic species, yet significant uncertainties in thermodynamic parameters and model form limit its potential for decision-making. In this paper we present a novel method for the quantification of thermodynamic parameter uncertainty and ionic strength correction model uncertainty using Bayesian Markov Chain Monte Carlo (MCMC) estimation methods. These methods allow for the inclusion of correlation modeling, which has not been present in previous work. The MCMC simulations are used to model a natural river water to determine the uncertainty in the calculated environmental speciation of ethylenediamenetetraacetate, a chelating agent that has attracted considerable environmental interest. The results indicate that incorporating correlation among related thermodynamic parameters into the uncertainty model is necessary to correctly quantify the overall system uncertainty. This result indicates the superiority of MCMC estimation methods overtraditional Monte Carlo methods when available data are used to estimate parameter uncertainty in systems with closely related model parameters.

Bayes Theorem↗

PubMed2005

Uncertainty due to model choice in variant Creutzfeldt-Jakob disease projections.

For some statistical applications, uncertainty due to unverifiable assumptions can be much greater than that arising from the random variability that is quantified by conventional confidence intervals. The case of projecting possible maximum numbers of eventual deaths due to variant Creutzfeldt-Jakob disease in the United Kingdom provides an extreme example of this phenomenon. The need for parametric extrapolation of the incubation distribution, along with non-identifiability of the number of infected persons, makes assumptions very influential. Several publications in leading science journals gave upper bounds that were 100-fold to 20 000-fold lower than projections from other plausible models given here that fit the data about as well. The crucial assumption for projections is how the risk of death increases with time since infection, and exponential growth is an obvious choice for pessimistic models. Parametric extrapolation from the generalized lambda, generalized F, or lognormal distributions produced upper bounds much lower than exponential extrapolation (i.e. assuming a Gompertz distribution, which fit the data up to early 2002 quite well). Had the publications considered such possibilities, they would have reached much weaker conclusions and been less suitable for leading general science journals. The scientific publication process may have inherent disincentives for thorough assessment of uncertainty.

Adolescent↗

PubMed2006

Optimal intervention for an epidemic model under parameter uncertainty.

We will be concerned with optimal intervention policies for a continuous-time stochastic SIR (susceptible-->infective-->removed) model for the spread of infection through a closed population. In previous work on such optimal policies, it is common to assume that model parameter values are known; in reality, uncertainty over parameter values exists. We shall consider the effect upon the optimal policy of changes in parameter estimates, and of explicitly taking into account parameter uncertainty via a Bayesian decision-theoretic framework. We consider policies allowing for (i) the isolation of any number of infectives, or (ii) the immunisation of all susceptibles (total immunisation). Numerical examples are given to illustrate our results.

Algorithms↗

PubMed2006

Assessment of uncertainties in the modelling of CSOs.

Engineers generally use simulation tools such as SWMM, MOUSE, HYDROWORKS, etc. to simulate the operation of sewer systems. Equations used by these models to represent the hydraulics of sewer systems are well known as well as their performance. Thus assessment of the performance needs to suppose that the user is an expert who is able to describe each facility in the most accurate way according to the model. In Western Europe, important parts of sewer systems are old and combined structures. Such structures very often include very complicated special structures, especially within CSO facilities. Experience shows that the performance limits of the models do not rely on the accuracy of the equations, but on the ability of the modeller to describe these special structures properly. The objective of this research is to assess the part of errors and uncertainties which originate from modellers' inconsistencies in the description of special structures. Even if the results are obtained with specific software (CANOE), results can easily be generalised to other models, since the equations used (Barré de Saint Venant, etc.) are almost the same.

Computer Simulation↗

PubMed2003

Modeling variability and uncertainty associated with inhaled weapons-grade PuO2.

The work presented relates to developing a stochastic version of the ICRP 66 respiratory tract deposition model and applying the stochastic model to characterize the variability/uncertainty associated with inhaled PuO2 for a hypothetical population of nuclear workers engaged in light work-related exercise. The parameter uncertainty/variability distributions used are essentially the same as the FORTRAN-based stochastic deposition model of Bolch et al. known as LUDUC (LUng Dose Uncertainty Code). Based on Crystal Ball software, this stochastic deposition model includes particle polydispersity, which Bolch et al. did not discuss. This paper first compares model-simulated regional deposition probability distributions to deterministic results based on LUDEP (LUng Dose Evaluation Program) software, which implements the ICRP 66 deterministic deposition model. For these comparisons, a particle density of 3 g cm(-3) (for hypothetical radioactive particles) was used. The range of possible depositions generated by LUDUC and the Crystal Ball program results revealed LUDEP's limitations. Even though LUDEP tends to use parameters that represent average parameter values for adult males, it overestimates deposition in the lower regions of the lung for most of the population. The Crystal Ball program was then used to generate radioactivity intake distributions for single and multiple PuO2 particle intakes by a hypothetical population of nuclear workers for the stochastic intake (STI) paradigm. These distributions of radioactivity intake are evaluated for the five primary regions of the respiratory tract as defined in the ICRP Publication 66. The results reveal that when a particle has been deposited, the radioactivity is likely to be low if it is in the lower regions (< 10 Bq for the bb and AI regions), but it may be quite large in the upper regions (as much as 600 Bq for the ET1, and ET2 regions), and the distributions for radioactivity become less and less skewed to the right, as particles penetrate deeper within the respiratory tract.

Administration, Inhalation↗