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Results for “Uncertainty modelling”

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At least 325 records · Page 18Linked to original sources

PubMed2006

What determines the cost-effectiveness of diabetes screening?

AIMS/HYPOTHESIS: The cost-effectiveness of screening for diabetes is unknown but has been modelled previously. None of these models has taken account of uncertainty. We aimed to describe these uncertainties in a model where the outcome was CHD risk. SUBJECTS AND METHODS: Our model used population data from the Danish Inter99 study, and simulations were run in a theoretical population of 1,000,000 individuals. CHD risk was estimated using the UK Prospective Diabetes Study (UKPDS) risk engine, and risk reduction from published randomised clinical trials. Probabilistic sensitivity analysis was used to provide confidence intervals for modelled outputs. Uncertain parameter values were independently simulated from distributions derived from existing literature and deterministic sensitivity analysis performed using multiple model runs under different strategy choices and using extreme parameter estimates. RESULTS: In the least conservative model (low costs and multiplicative risk reduction for combined treatments), the 95% confidence interval of the incremental cost-effectiveness ratio varied from pound23,300-82,000. The major contributors to this uncertainty were treatment risk reduction model parameters: the risk reduction for hypertension treatment and UKPDS risk model intercept. Overall cost-effectiveness ratio was not sensitive to decisions about which groups to screen, nor the costs of screening or treatment. It was strongly affected by assumptions about how treatments combine to reduce risk. CONCLUSIONS/INTERPRETATION: Our model suggests that there is considerable uncertainty about whether or not screening for diabetes would be cost-effective. The most important but uncertain parameter is the effect of treatment. In addition to directly influencing current policy decisions, health care modelling can identify important unknown or uncertain parameters that may be the target of future research.

Adult↗

PubMed2004

Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.

Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).

Animals↗

PubMed2006

Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships.

Radiotherapy treatment outcome models are a complicated function of treatment, clinical and biological factors. Our objective is to provide clinicians and scientists with an accurate, flexible and user-friendly software tool to explore radiotherapy outcomes data and build statistical tumour control or normal tissue complications models. The software tool, called the dose response explorer system (DREES), is based on Matlab, and uses a named-field structure array data type. DREES/Matlab in combination with another open-source tool (CERR) provides an environment for analysing treatment outcomes. DREES provides many radiotherapy outcome modelling features, including (1) fitting of analytical normal tissue complication probability (NTCP) and tumour control probability (TCP) models, (2) combined modelling of multiple dose-volume variables (e.g., mean dose, max dose, etc) and clinical factors (age, gender, stage, etc) using multi-term regression modelling, (3) manual or automated selection of logistic or actuarial model variables using bootstrap statistical resampling, (4) estimation of uncertainty in model parameters, (5) performance assessment of univariate and multivariate analyses using Spearman's rank correlation and chi-square statistics, boxplots, nomograms, Kaplan-Meier survival plots, and receiver operating characteristics curves, and (6) graphical capabilities to visualize NTCP or TCP prediction versus selected variable models using various plots. DREES provides clinical researchers with a tool customized for radiotherapy outcome modelling. DREES is freely distributed. We expect to continue developing DREES based on user feedback.

Computer Simulation↗

PubMed2005

A framework for integrated pediatric palliative care: being with dying.

Recent studies highlight the need for an integrated model for palliative and end-of-life pediatric care. About 55,000 children die each year in the United States and, on any given day, about 8,600 children could benefit from care that acknowledges their limited life expectancy and severity of illness. Two case studies of children illustrate different approaches-one that aggressively applies all possible technologies to maximize chances of survival and another that focuses on the patient's overall quality of life and on healing rather than curing. The cases highlight characteristics of an integrated model of palliative care to address clinical, moral, and ethical uncertainties. This model integrates being with doing, provides for developing attunement and presence as capacities for being with children and their parents, and addresses challenges in the healthcare environment. Strategies for integrating palliative care into pediatric practice include listening, fostering respect for the child and parents across the organization, nurturing collaborative connections, managing uncertainty, tolerating ambiguity, making peace with conflict, and committing to self-care. Every pediatric nurse can play a role in making the vision of palliative care a reality integrated into the fabric of pediatric practice.

Benchmarking↗

PubMed2006

Investigation on the improvement of prediction by bootstrap model averaging.

OBJECTIVES: We illustrate a recently proposed two-step bootstrap model averaging (bootstrap MA) approach to cope with model selection uncertainty. The predictive performance is investigated in an example and in a simulation study. Results are compared to those derived from other model selection methods. METHODS: In the framework of the linear regression model we use the two-step bootstrap MA, which consists of a screening step to eliminate covariates thought to have no influence on the response, and a model-averaging step. We also apply the full model, variable selection using backward elimination based on Akaike's Information Criterion (AIC), the Bayes Information Criterion (BIC) and the bagging approach. The predictive performance is measured by the mean squared error (MSE) and the coverage of confidence intervals for the true response. RESULTS: We obtained similar results for all approaches in the example. In the simulation the MSE was reduced by all approaches in comparison to the full model. The smallest values are obtained for bootstrap MA. Only the bootstrap MA and the full model correctly estimated the nominal coverage. The backward elimination procedures led to substantial underestimation and bagging to an overestimation of the true coverage. The screening step of bootstrap MA eliminates most of the unimportant factors. CONCLUSION: The new bootstrap MA approach shows promising results for predictive performance. It increases practical usefulness by eliminating unimportant factors in the screening step.

Body Composition↗

PubMed1993

A predictive model for the behavior of radionuclides in lake systems.

This paper describes a predictive model for the behavior of 137Cs in lacustrine systems. The model was tested by comparing its predictions to contamination data collected in various lakes in Europe and North America. The migration of 137Cs from catchment basin and from bottom sediments to lake water was discussed in detail; these two factors influence the time behavior of contamination in lake water. The contributions to the levels of radionuclide concentrations in water, due to the above factors, generally increase in the long run. The uncertainty of the model, used as a generic tool for prediction of the levels of contamination in lake water, was evaluated. Data sets of water contamination analyzed in the present work suggest that the model uncertainty, at a 68% confidence level, is a factor 1.9.

Cesium Radioisotopes↗

PubMed2001

Modelling and simulation of variability and uncertainty in toxicokinetics and pharmacokinetics.

Two important methodological issues within the framework of the variability and uncertainty analysis of toxicokinetic and pharmacokinetic systems are discussed: (i) modelling and simulation of the existing physiologic variability in a population; and (ii) modelling and simulation of variability and uncertainty when there is insufficient or not well defined (e.g. small sample, semiquantitative, qualitative and vague) information available. Physiologically based pharmacokinetic models are especially suited for separating and characterising the physiologic variability from the overall variability and uncertainty in the system. Monte Carlo sampling should draw from multivariate distributions, which reflect all levels of existing dependencies in the intact organism. The population characteristics should be taken into account. A fuzzy simulation approach is proposed to model variability and uncertainty when there is semiquantitative, qualitative and vague information about the model parameters and their statistical distributions cannot be defined reliably.

Animals↗

PubMed2004

A comparison of microbial dose-response models fitted to human data.

A study of eight mathematical dose-response models for microbial risk assessment was conducted using infectivity and illness data on a variety of microbial pathogens from published studies with human volunteers. The purpose was to evaluate variability among the models for human microbial dose-response data in order to determine whether two-parameter models might suffice for most microbial dose-response data or whether three-parameter models should generally be fitted. Model variability was measured in terms of estimated ED01s and ED10s, with the view that these effective dose levels correspond to the lower and upper limits of the 1-10% risk range generally recommended for establishing benchmark doses in risk assessment. An investigation of the ranks of the ED01 and ED10 values among the models led to the conclusion that the two-parameter models captured at least as much uncertainty as the three-parameter models for the data examined. A further evaluation of the two-parameter models did not result in the selection of one "best" model, but it did provide some insights into the models' relative behavior. The model uncertainty analysis proposed by Kang et al. [Regulat. Toxicol. Pharmacol. 32 (2000) 68] using four two-parameter models was reinforced.

Algorithms↗

PubMed2000

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

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

Air Pollutants↗

PubMed2005

A benchmark methodology for managing uncertainties in urban runoff quality models.

In this paper we present a benchmarking methodology, which aims at comparing urban runoff quality models, based on the Bayesian theory. After choosing the different configurations of models to be tested, this methodology uses the Metropolis algorithm, a general MCMC sampling method, to estimate the posterior distributions of the models' parameters. The analysis of these posterior distributions allows a quantitative assessment of the parameters' uncertainties and their interaction structure, and provides information about the sensitivity of the probability distribution of the model output to parameters. The effectiveness and efficiency of this methodology are illustrated in the context of 4 configurations of pollutants' accumulation/erosion models, tested on 4 street subcatchments. Calibration results demonstrate that the Metropolis algorithm produces reliable inferences of parameters thus, helping on the improvement of the mathematical concept of model equations.

Algorithms↗

PubMed2006

A classical likelihood based approach for admixture mapping using EM algorithm.

Several disease-mapping methods have been proposed recently, which use the information generated by recent admixture of populations from historically distinct geographic origins. These methods include both classic likelihood and Bayesian approaches. In this study we directly maximize the likelihood function from the hidden Markov Model for admixture mapping using the EM algorithm, allowing for uncertainty in model parameters, such as the allele frequencies in the parental populations. We determined the robustness of the proposed method by examining the ancestral allele frequency estimate and individual marker-location specific ancestry when the data were generated by different population admixture models and no learning sample was used. The proposed method outperforms a widely used Bayesian MCMC strategy for data generated from various population admixture models. The multipoint information content for ancestry was derived based on the map provided by Smith et al. (2004) and the associated statistical power was calculated. We examined the distribution of admixture LD across the genome for both real and simulated data and established a threshold for genome wide significance applicable to admixture mapping studies. The software ADMIXPROGRAM for performing admixture mapping is available from authors.

Algorithms↗

PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗

PubMed2005

A stochastic model simulating pathogen-specific mastitis control in a dairy herd.

The objective of this study was to develop a model simulating mastitis control in dairy herds and to investigate how sensitive the model is when varying the effect parameters according to the uncertainty. The model simulates 9 pathogen-specific mastitis types, each of which can be subclinical or clinical. The clinical cases can be 1 of 4 severities defined according to the effect of the mastitis case: mild, moderate, severe, and permanent effect. The risk factors include lactation stage, parity, yield level, previous diseases, season, and contagious spread of the infection from herd mates. Occurrence of mastitis is modeled to have direct effects on feed intake, body weight, milk yield, somatic cell count in the milk, subsequent mastitis cases within the cow and in herd mates, voluntary and involuntary culling, mortality, and milk withdrawal. Thirty-five scenarios were simulated to study model behavior and model sensitivity. The consequences per cow/yr of mastitis in the default simulated herd included 0.42 clinical mastitis occurrences, 0.56 subclinical mastitis occurrences, loss of 385-kg milk yield, a 1.3% reduced feed intake, 61-kg milk withdrawal and 146 in reduced economic net return. Based on scenarios demonstrating model behavior and sensitivity analysis, the model appears to produce valid consequences of mastitis control strategies. Representation of the effect of subclinical mastitis and of variation in mastitis severity was concluded in this study to be important when modeling mastitis economics in a dairy herd. The model offers the opportunity to study the long-term herd specific effects of a wide range of control strategies against mastitis.

Animals↗

PubMed2004

Bayesian methods for regional-scale eutrophication models.

We demonstrate a Bayesian classification and regression tree (CART) approach to link multiple environmental stressors to biological responses and quantify uncertainty in model predictions. Such an approach can: (1) report prediction uncertainty, (2) be consistent with the amount of data available and (3) be flexible enough to permit updates and improvements. Tree-based methods are a flexible approach useful for variable subset selection and when the analyst suspects global nonlinearity and cannot (or does not want to) specify the functional form of possible interactions a priori. We use the US EPA National Eutrophication Survey data to fit three models demonstrating the methods and to highlight important differences arising from slightly different model specifications. The Bayesian approach offers many advantages, including the estimation of the value of new information and proper probability distributions on the variable of interest as an output, which can be directly used in risk assessment or decision-making.

Bayes Theorem↗

PubMed1997

Estimates of neutron relative biological effectiveness derived from the Japanese atomic bomb survivors.

PURPOSE: To investigate neutron relative biological effectiveness. MATERIALS AND METHODS: The latest Japanese atomic bomb survivor cancer incidence and mortality datasets with the current (DS86) dosimetry system are analysed using generalized relative risk models and generalized absolute risk models, both with and without recently indicated adjustments to the Hiroshima DS86 neutron dose estimates. RESULTS: Without adjustments to the Hiroshima neutron doses, the best estimate of neutron relative biological effectiveness for all tumours in the incidence data is 63.3 (95% CI < 0-275.3) when a generalized relative risk model is used; when a generalized absolute risk model is used in the incidence data the best estimate is 53.5 (95% CI < 0-201.0); when a generalized relative risk model is used in the mortality data, the best estimate is 287.7 (95% CI 38.0- > 10[3]). When likely adjustments are made to the Hiroshima neutron doses the best estimate of neutron relative biological effectiveness in the incidence data using a generalized relative risk model is 15.1 (95% CI < 0-51.4); when a generalized absolute risk model is used in the incidence data the best estimate is 9.0 (95% CI < 0-32.9); when a generalized relative risk model is used in the mortality data the best estimate is 55.1 (95% CI 9.5-280.3). Although there are no significant differences between groupings of the solid tumour sites in their estimated neutron relative biological effectiveness, there are indications that the neutron relative biological effectiveness of solid tumours is lower than that of leukaemia, whether or not adjustments are made to the Hiroshima neutron dose estimates. Uncertainties in the likely adjustments to the DS86 Hiroshima neutron and gamma dose estimates as well as uncertainties in the modelling of excess risk in the two cities (Hiroshima and Nagasaki) imply that these findings should be treated with caution. CONCLUSIONS: Likely adjustments to the Hiroshima neutron dose estimates imply a substantial increase in information on neutron relative biological effectiveness. Whether or not adjustments are made to the Hiroshima neutron doses, there are indications of inconsistency between the estimates of neutron relative biological effectiveness for solid tumours and leukaemia. Dosimetric and modelling uncertainties mean that these findings should be treated with caution.

Humans↗

PubMed1996

Atmospheric dispersion and deposition of 131I released from the Hanford Site.

Approximately 2.6 x 10(4) TBq (700,000 Ci) of 131I were released to the air from reactor fuel processing plants on the Hanford Site in southcentral Washington State from December 1944 through December 1949. The Hanford Environmental Dose Reconstruction Project developed a suite of codes to estimate the doses that might have resulted from these releases. The Regional Atmospheric Transport Code for Hanford Emission Tracking (RATCHET) computer code is part of this suite. The RATCHET code implements a Lagrangian-trajectory, Gaussian-puff dispersion model that uses hourly meteorological and release rate data to estimate daily time-integrated air concentrations and surface contamination for use in dose estimates. In this model, iodine is treated as a mixture of three species (inorganic gases, organic gases, and particles). Model deposition parameters are functions of the mixture and meteorological conditions. A resistance model is used to calculate dry deposition velocities. Equilibrium between concentrations in the precipitation and the air near the ground is assumed in calculating wet deposition of gases, and irreversible washout of the particles is assumed. RATCHET explicitly treats the uncertainties in model parameters and meteorological conditions. Uncertainties in 131I release rates and partitioning among the nominal species are treated by varying model input. The results of 100 model runs for December 1944 through December 1949 indicate that monthly average air concentrations and deposition have uncertainties ranging from a factor of two near the center of the time-integrated plume to more than an order of magnitude near the edge. These results indicate that approximately 10% of the 131I released to the atmosphere decayed during transit in the study area, approximately 56% was deposited within the study area, and the remaining 34% was transported out of the study area while still in the air.

Air Pollutants, Radioactive↗

PubMed2001

Modeling and Monte Carlo simulation of TCDD transport in a river.

A one-dimensional water quality model to assess the long-term fate of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in three compartments (water, sediment, fish) of a river has been developed using the literature data on various model parameters. The transient deterministic model with constant or nonrandom parameters is solved numerically by the method of orthogonal collocation, while an analytical solution is developed for the steady-state model. The impact of uncertainty in several model parameters has been studied by means of Monte Carlo simulations assuming that the uncertain parameters are uncorrelated and can be modeled by three probability distributions (uniform, normal and lognormal). For the case of a high TCDD discharge into a small, shallow river, we find that the maximum TCDD contents of water and fish are well below the prescribed safe limits. We also find that the effects of uncertainty on water quality metrics are quite complex or nonintuitive and can be substantial. This is especially true for TCDD in fish, which can be higher by as much as 50-70% than the deterministic predictions, if the parameter uncertainties follow uniform distributions.

Animals↗

PubMed2000

Radiation pneumonitis after breast cancer irradiation: analysis of the complication probability using the relative seriality model.

BACKGROUND: Toxicity of the respiratory system is quite common after radiotherapy of thoracic tumors; breast cancer patients represent one of the groups for which there is also a long expected survival. The quantification of lung tissue response to irradiation is important in designing treatments associated with a minimum of complications and maximum tumor control. METHODS: The study population consisted of 68 patients who received irradiation for breast cancer at Stage II. Radiation pneumonitis was retrospectively assessed on the basis of clinical symptoms and radiological findings. For each patient, a measure of the exposure (i.e., the lung dose-volume histogram [DVH]) and a measure of the outcome was available. Based on these data, a maximum likelihood fitting to the relative seriality model was performed. The uncertainties of the model parameters were calculated and their impact on the dose-response curve was studied. The optimum parameter set was then applied to 5 other patient groups treated for breast cancer, and the normal tissue complication probability (NTCP) was calculated. Each group was individuated by the radiotherapy treatment technique used; the dose distribution in the lung was described by a mean DVH and the incidence of radiation pneumonitis in each group was known. Lung radiosensitivity was assumed to be homogeneous through all of the calculations. RESULTS: The relative seriality model could describe the dataset. The volume effect was found to be relevant in the description of radiation pneumonitis. Age was found to be associated with increased risk of radiation pneumonitis. Two distinct dose-response curves were obtained by splitting the group according to age. The impact of the parameter uncertainties on the dose-response curve was quite large. The parameter set determined could be used predictively on 3 of the 5 patient groups. CONCLUSION: The complication data could be modeled with the relative seriality model. However, further independent datasets, classified according to the same endpoint, must be analyzed before introducing NTCP modeling in clinical practice.

Age Factors↗