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PubMed1981

Sensitivity analysis and optimization for a head movement model.

A sixth order nonlinear model for horizontal head rotations in humans is analyzed using an extended parameter sensitivity analysis and a global optimization algorithm. The sensitivity analysis is used in both the direct sense, as a model fitting tool, and in the indirect sense, as a guide to experimental design. Resolution is defined in terms of the sensitivity table, and is used to interpret the sensitivity results. Using sensitivity analyses, the head and eye movement systems are compared and contrasted. Controller signal parameters are the most influential. Their variations and effects on head movement trajectories and accelerations are investigated, and the conclusions are compared with clinical neurological findings. The global optimization algorithm, in addition to automating the fitting of various types of data, is combined with time optimality theory to give theoretical time-optimal inputs to the model.

Computers↗
PubMed2003

Validation and sensitivity analysis of probabilistic models of dietary exposure to micronutrients: an example based on vitamin B6.

Probabilistic modelling can be used to get an insight into the variability and uncertainty of the nutrient intake in a population. When a probabilistic model is used, it is important that it is validated. Furthermore, a sensitivity analysis of the model output can give an insight into the most important input variables of the model and can be used as an aid to describe the reliability of the model. In this study, four models to estimate vitamin B(6) intake among males and females were validated using the method of Kaaks et al. This method compares the relationship between three different kind of measurements with the unknown 'true' intake. In each of these four models, only one input variable (concentration or bioavailability) was changed compared with a reference model. A sensitivity analysis was also performed. The results of the validation showed that for males, a model using a fixed bioavailability factor at the food group level was valid, while for females a model using either a fixed value or a distribution for the bioavailability factor was valid. Use of a distribution for the concentration of vitamin B(6) in supplements was not valid. The results of the sensitivity analysis showed that the concentration of vitamin B(6) in food and supplements was the key contributor to variability and uncertainty in the model estimates of vitamin B(6) intake, in both males and females. All results indicated that when taking variability and uncertainty into account by using probabilistic modelling, the effect on the nutrient intake for nutrients that are present in many common eaten foods, is small. For these broadly available nutrients, fixed concentrations and bioavailability factors give a good estimate of the nutrient intake in a population. When using probabilistic modelling, it is very important to collect more actual information about the concentration.

Adult↗
PubMed1994

Sensitivity analysis of relative accommodation and vergence.

A sensitivity analysis was performed to determine the variation in response to changes in parameter values of a previously developed nonlinear static model of accommodation and vergence. To determine normal behavior, model simulation responses were computed using previously obtained parameter values in 4 subjects under 2 conditions. In the first, relative accommodation was evaluated by maintaining the vergence stimulus constant at 2.5 meter angles (MA) and varying the accommodative stimulus from -2.5 to 2.5 diopters (D) in 0.25-D steps. In the second, relative vergence was evaluated by maintaining the accommodative stimulus constant at 2.5 D and varying the vergence stimulus from 25 prism diopters (PD) base-in to 25 PD base-out in 5-PD steps. Sensitivity of the model parameters, consisting of controller gains for accommodation (ACG) and vergence (VCG), crosslink gains for accommodation-to-vergence (AC) and vergence-to-accommodation (CA), deadspace operators for accommodation (AE +/- AD) and vergence (VE +/- VD), and the tonic levels for accommodation (ABIAS) and vergence (VBIAS) were assessed by varying them at 50% and 150% of their normal values. It was found that the accommodation and vergence systems were most sensitive to variation in crosslink gain, moderately sensitive to variation in controller gain and tonic level, and least sensitive to variation in size of the deadspace. These results may provide a quantitative basis for the occurrence of ocular dysfunctions associated with abnormal crosslink gains, such as strabismus, in clinic patients.

Accommodation, Ocular↗
PubMed2004

Monte Carlo sensitivity analysis and Bayesian analysis of smoking as an unmeasured confounder in a study of silica and lung cancer.

Conventional confidence intervals reflect uncertainty due to random error but omit uncertainty due to biases, such as confounding, selection bias, and measurement error. Such uncertainty can be quantified, especially if the investigator has some idea of the amount of such bias. A traditional sensitivity analysis produces one or more point estimates for the exposure effect hypothetically adjusted for bias, but it does not provide a range of effect measures given the likely range of bias. Here the authors used Monte Carlo sensitivity analysis and Bayesian bias analysis to provide such a range, using data from a US silica-lung cancer study in which results were potentially confounded by smoking. After positing a distribution for the smoking habits of workers and referents, a distribution of rate ratios for the effect of smoking on lung cancer, and a model for the bias parameter, the authors derived a distribution for the silica-lung cancer rate ratios hypothetically adjusted for smoking. The original standardized mortality ratio for the silica-lung cancer relation was 1.60 (95% confidence interval: 1.31, 1.93). Monte Carlo sensitivity analysis, adjusting for possible confounding by smoking, led to an adjusted standardized mortality ratio of 1.43 (95% Monte Carlo limits: 1.15, 1.78). Bayesian results were similar (95% posterior limits: 1.13, 1.84). The authors believe that these types of analyses, which make explicit and quantify sources of uncertainty, should be more widely adopted by epidemiologists.

Adult↗
PubMed2001

Sensitivity analysis and evaluation of microFacCO: a microscale motor vehicle emission factor model for CO emissions.

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting real-time site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by approximately 67% at ambient temperatures of 45 degrees F and > or = 95 degrees F in comparison with an ambient temperature of 75 degrees F. The input variable "emission failure standard rate" is more sensitive to estimating emission rates in the 1990s than in the 2000s. The estimation of emission rates is not very sensitive to relative humidity. MicroFacCO can also be applied to examine the contribution of emission rates per vehicle class and model year. The model evaluation is presented for tunnel studies at five locations. In general, this evaluation study found good agreement between the measured and the modeled emissions. These analyses and evaluations have identified the need for additional studies to update the high-speed (>35 mph) air conditioning (A/C) correction factor and to add effects due to road grades. MicroFacCO emission estimates are very sensitive to the emission standard failure rate. Therefore, the model performance can be greatly improved by using a local emission standard failure rate.

Algorithms↗
PubMed1985

Probabilistic sensitivity analysis using Monte Carlo simulation. A practical approach.

The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying one or more probability or utility estimates from baseline values to see if the optimal strategy changes--is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly assigned a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst.

Decision Making↗
PubMed2002

Sensitivity analysis for healthcare models fitted to data by statistical methods.

After fitting complex models to data using statistical methods, a sensitivity analysis can be carried out. This determines which parts of a model are causing the bulk of the uncertainty in the model predictions (model "output"), and is a decision-support tool for the modeller who contemplates refining a model further or collecting additional data. A simple methodology for carrying out a sensitivity analysis is described. It is envisaged that such a relatively quick insight-generating step would precede the use of a more formal decision-theoretic approach that would address specific questions. Its use is illustrated using a model for breast cancer screening previously published in this journal. A simpler 3-parameter screening model is used in a simulation study of the error of the method as a function of sample size.

Breast Neoplasms↗
PubMed1997

Morbidity and mortality due to ascariasis: re-estimation and sensitivity analysis of global numbers at risk.

This paper presents estimates of the global numbers of people at risk from morbidity related to infection with Ascaris lumbricoides and the numbers of deaths from this infection. Morbidity is classified into 4 types: deficits in growth and fitness which are contemporaneous with infection, or permanent, overt acute illness of mild to moderate severity, and complications involving hospitalization. The estimation of morbidity is based on theoretical models of parasite distributions developed in previous papers. A sensitivity analysis is carried out in which parameters of the model are varied using a Latin hypercube sampling technique. The results estimate approximately 1300 million infections globally with 59 million at risk of some morbidity. The estimate for acute illness is 12 million cases per year with approximately 10,000 deaths. Most morbidity is in children. Sensitivity analysis suggests that infection estimates will not vary greatly with changes in parameter values but that morbidity estimates may be highly variable.

Adolescent↗
PubMed1986

Sensitivity analysis of the influence of source-term and environmental parameters on the radiological risk of coal-fired plants.

A sensitivity analysis was undertaken to determine the influence of different source-term and environmental parameters on the radiological risks from a coal-fired plant (CFP). It was found that the release rate of radionuclides and the effective release height most significantly influence radiological risk. Site characteristics, such as rain scavenging coefficients and food acquirement habits, have a lesser influence, and some parameters, such as time delay before ingestion of contaminated food, have practically no influence on the radiological impact of a CFP. The contribution to radiation risks of different exposure modes (i.e. inhalation, ingestion and contact with ground surface) were also analyzed, as well as of specific radionuclides and human body organs. Results of the sensitivity analysis were interpreted in terms of the characteristics of the fuel, facilities and site of a CFP. It is concluded that by proper choice of coal, furnace, ash filtration and stack height, as well as by proper siting, the radiological impact of a CFP can be drastically reduced.

Coal↗
PubMed2006

Sensitivity analysis of Markov models for communities of competing sessile organisms.

1. Communities of competing sessile organisms are often modelled using Markov chains. Sensitivity analysis of the stationary distribution of these models tells us how we expect the abundance of each organism to respond to changes in interactions between species. This is important for conservation and management. 2. Markov models for such communities have usually been formulated in discrete time. Each column of the discrete-time transition matrix must sum to 1 (column stochasticity). Sensitivity analysis therefore involves defining a pattern of compensation that maintains column stochasticity as a single transition probability changes. There is little biological theory about the appropriate compensation pattern, but the usual choices involve changing only the elements of a single column of the transition matrix. 3. I argue that if the underlying dynamics occur in continuous time, each transition probability is the net outcome of direct and many indirect interactions. 4. Determining the consequences of changing a single direct interaction will often be of interest. I show how this can be achieved using a continuous-time model. The resulting discrete-time compensation pattern is quite different from those that have been considered elsewhere, with changes occurring in many columns. 5. I also show how to determine which direct interactions are being changed under any discrete-time compensation pattern.

Ecosystem↗
PubMed2003

A mathematical model of phytoremediation for petroleum contaminated soil: sensitivity analysis.

Phytoremediation is an attractive treatment technology for many contaminated sites due to its cost effectiveness and public acceptance. We present a sensitivity analysis of important parameters from a screening level model for phytoremediation by grass species of weathered petroleum-contaminated sites. The conceptual framework is that root movement through contaminated soil will enhance contaminant biodegradation by providing a local environment more favorable for petroleum degrading microorganisms--the so-called rhizosphere effect. Common questions in phytoremediation are, "What species should be planted?" and "What management practices should be followed?" These choices may affect degradation kinetics, root biomass (and therefore rhizosphere volume), and the root turnover. Important model parameters are the rate constants, rhizosphere volume, and the rate of root turnover. We present a sensitivity analysis with the aim of identifying the most important factors for improving phytoremediation effectiveness. For simulations of the phytoremediation of weathered diesel range organics, our results indicate that annual species, with higher root turnover, are preferred over perennial species with the caveat of equal degradation rate constants, that is, no species-dependent effects. In addition, the results suggest that the management of nonrhizosphere soil could play an important role in the overall effectiveness of phytoremediation. Finally, the effect of increasing root biomass or increasing the rhizosphere thickness is approximately equivalent with respect to the ultimate removal of the contaminants.

Environmental Pollution↗
PubMed2002

Primary neck management among patients with cancer of the oral cavity without clinical nodal metastases: A decision and sensitivity analysis.

BACKGROUND: A standardized neck management strategy for oral cancer patients without clinical nodal metastases remains to be established. Consequently, a decision and sensitivity analysis of two neck management protocols, involving either prophylactic neck dissection or careful observation, was conducted using the Oral Cancer Registry of Kyushu, Japan. METHODS: We calculated probabilities of subclinical nodal metastases and 5-year survival using the registry data. A two-way sensitive analysis was conducted using the probabilities and parameters of the complete nodal metastasis resection rate (x) and a utility rating that describes the health state induced by dissection (y) compared with the neck condition in a careful-observation group. RESULTS: We solved the threshold curve for y and x for the expected utility between the two groups. The results showed that prophylactic neck dissection must guarantee a complete resection of subclinical nodal metastases with no disadvantage to health state to be evaluated as equally satisfactory as careful observation. CONCLUSIONS: Careful observation involving standardized systematic preoperative and postoperative screening of the neck seems preferable to prophylactic neck dissection for oral cancer patients without subclinical nodal metastases.

Carcinoma, Squamous Cell↗
PubMed2003

Semi-automated sensitivity analysis to assess systematic errors in observational data.

BACKGROUND: Published epidemiologic research usually provides a quantitative assessment of random error for effect estimates, but no quantitative assessment of systematic error. Sensitivity analysis can provide such an assessment. METHODS: We describe a method to reconstruct epidemiologic data, accounting for biases, and to display the results of repeated reconstructions as an assessment of error. We illustrate with a study of the effect of less-than-definitive therapy on breast cancer mortality. RESULTS: We developed SAS code to reconstruct the data that would have been observed had a set of systematic errors been absent, and to convey the results. After 4,000 reconstructions of the example data, we obtained a median estimate of relative hazard equal to 1.5 with a 95% simulation interval of 0.8-2.8. The relative hazard obtained by conventional analysis equaled 2.0, with a 95% confidence interval of 1.2-3.4. CONCLUSIONS: Our method of sensitivity analysis can be used to quantify the systematic error for an estimate of effect and to describe that error in figures, tables, or text. In the example, the sources of error biased the conventional relative hazard away from the null, and that error was not accurately communicated by the conventional confidence interval.

Bias↗
PubMed2003

[Application of sensitivity analysis method in mode analysis of hypergravitation training facility].

OBJECTIVE: To increase the natural frequencies of hypergravitation training facility. METHOD: The natural frequencies of the hypergravitation training facility were modified by using sensitivity analysis method. RESULT: The natural frequencies of the hypergravitation training facility were improved by modification of physical and geometrical parameters of sensitive elements. CONCLUSION: The computational results indicates that sensitivity analysis method is feasible.

Astronomical Phenomena↗
PubMedDate not supplied

Probabilistic sensitivity analysis for decision trees with multiple branches: use of the Dirichlet distribution in a Bayesian framework.

In structuring decision models of medical interventions, it is commonly recommended that only 2 branches be used for each chance node to avoid logical inconsistencies that can arise during sensitivity analyses if the branching probabilities do not sum to 1. However, information may be naturally available in an unconditional form, and structuring a tree in conditional form may complicate rather than simplify the sensitivity analysis of the unconditional probabilities. Current guidance emphasizes using probabilistic sensitivity analysis, and a method is required to provide probabilistic probabilities over multiple branches that appropriately represents uncertainty while satisfying the requirement that mutually exclusive event probabilities should sum to 1. The authors argue that the Dirichlet distribution, the multivariate equivalent of the beta distribution, is appropriate for this purpose and illustrate its use for generating a fully probabilistic transition matrix for a Markov model. Furthermore, they demonstrate that by adopting a Bayesian approach, the problem of observing zero counts for transitions of interest can be overcome.

Asthma↗
PubMed2006

Flows of dioxins and furans in coastal food webs: inverse modeling, sensitivity analysis, and applications of linear system theory.

Rate constant bioaccumulation models are applied to simulate the flow of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the coastal marine food web of Frierfjorden, a contaminated fjord in southern Norway. We apply two different ways to parameterize the rate constants in the model, global sensitivity analysis of the models using Extended Fourier Amplitude Sensitivity Test (Extended FAST) method, as well as results from general linear system theory, in order to obtain a more thorough insight to the system's behavior and to the flow pathways of the PCDD/Fs. We calibrate our models against observed body concentrations of PCDD/Fs in the food web of Frierfjorden. Differences between the predictions from the two models (using the same forcing and parameter values) are of the same magnitude as their individual deviations from observations, and the models can be said to perform about equally well in our case. Sensitivity analysis indicates that the success or failure of the models in predicting the PCDD/F concentrations in the food web organisms highly depends on the adequate estimation of the truly dissolved concentrations in water and sediment pore water. We discuss the pros and cons of such models in understanding and estimating the present and future concentrations and bioaccumulation of persistent organic pollutants in aquatic food webs.

Animals↗
PubMed2003

Influences of parameter uncertainties within the ICRP-66 respiratory tract model: a parameter sensitivity analysis.

An important aspect in model uncertainty analysis is the evaluation of input parameter sensitivities with respect to model outcomes. In previous publications, parameter uncertainties were examined for the ICRP-66 respiratory tract model. The studies were aided by the development and use of a computer code LUDUC (Lung Dose Uncertainty Code) which allows probabilities density functions to be specified for all ICRP-66 model input parameters. These density functions are sampled using Latin hypercube techniques with values subsequently propagated through the ICRP-66 model. In the present study, LUDUC has been used to perform a detailed parameter sensitivity analysis of the ICRP-66 model using input parameter density functions specified in previously published articles. The results suggest that most of the variability in the dose to a given target region is explained by only a few input parameters. For example, for particle diameters between 0.1 and 50 microm, about 50% of the variability in the total lung dose (weighted sum of target tissue doses) for 239PuO2 is due to variability in the dose to the alveolar-interstitial (AI) region. In turn, almost 90% of the variability in the dose to the AI region is attributable to uncertainties in only four parameters in the model: the ventilation rate, the AI deposition fraction, the clearance rate constant for slow-phase absorption of deposited material to the blood, and the clearance rate constant for particle transport from the AI2 to bb1 compartment. A general conclusion is that many input parameters do not significantly influence variability in final doses. As a result, future research can focus on improving density functions for those input variables that contribute the most to variability in final dose values.

Absorption↗
PubMedDate not supplied

Sensitivity analysis and the expected value of perfect information.

Measures of decision sensitivity that have been applied to medical decision problems were examined. Traditional threshold proximity methods have recently been supplemented by probabilistic sensitivity analysis, and by entropy-based measures of sensitivity. The authors propose a fourth measure based upon the expected value of perfect information (EVPI), which they believe superior both methodologically and pragmatically. Both the traditional and the newly suggested sensitivity measures focus entirely on the likelihood of decision change without attention to corresponding changes in payoff, which are often small. Consequently, these measures can dramatically overstate problem sensitivity. EVPI, on the other hand, incorporates both the probability of a decision change and the marginal benefit of such a change into a single measure, and therefore provides a superior picture of problem sensitivity. To lend support to this contention, the authors revisit three problems from the literature and compare the results of sensitivity analyses using probabilistic, entropy-based, and EVPI-based measures.

Adult↗