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Results for “sensitivity analysis”

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At least 163 records · Page 9Linked to original sources
PubMed2005

A sensitivity analysis of bias in relative risk estimates due to disinfection by-product exposure misclassification.

We conducted a sensitivity analysis of relative risk estimates using local area mean disinfection by-product exposures. We used Monte Carlo simulations to generate data representing 100 towns, each with 100 births (n=10,000). Each town was assigned a mean total trihalomethane (TTHM) exposure value (mean=45, SD=28) based on a variable number of sampling locations (range 2-10). True maternal TTHM exposure was randomly assigned from a lognormal distribution using that town's true mean value. We compared the effect of a 20 microg/l increase in TTHM exposure on the risk of small-for-gestational age infancy using the true maternal exposure compared to various weighting measures of the town mean exposures. The exposure metrics included: (1) unweighted town mean, (2) town mean weighted by the inverse variance of the town mean, (3) town mean weighted by the inverse standard deviation of the town mean, (4) town mean weighted by 1-(standard deviation of sites per town/mean across all towns), and (5) a randomly selected value from one of the sites within the town of residence. To estimate the magnitude of misclassification bias from using the town mean concentrations, we compared the true exposure odds ratios (1.00, 1.20, 1.50, and 2.00) to the mean exposure odds ratios from the five exposure scenarios. Misclassification bias from the use of unweighted town mean exposures ranged from 19 to 39%, increasing in proportion to the size of the true effect estimates. Weighted town mean TTHM exposures were less biased than the unweighted estimates of maternal exposure, with bias ranging from 0 to 23%. The weighted town mean analyses showed that attenuation of the true effect of DBP exposure was diminished when town mean concentrations with large variability were downweighted. We observed a trade-off between bias and precision in the weighted exposure analyses, with the least biased effects estimates having the widest confidence intervals. Effect attenuation due to intrasystem variability was most evident in absolute and relative terms for larger odds ratios.

Adult↗
PubMed2000

Sensitivity analysis of pharmacokinetic parameters in one-compartment models.

The quantitative properties of a compartmental model can be characterized by the pharmacokinetic parameters. The sensitivity analysis of pharmacokinetic parameters was studied in six kinds of one-compartment models. The sensitivity functions with respect to different pharmacokinetic parameters for these one-compartment models were deduced using the partial differentiation. The pharmacokinetic parameters were ranked according to their sensitivity functions.

Algorithms↗
PubMed1989

Alcohol and traffic safety: a sensitivity analysis of data from composite sources.

Risk factors associated with single-vehicle driver fatalities are explored in a sensitivity analysis of data from composite sources. Information on fatalities was taken from the Federal Accident Reporting System data base for 1976-1981. Characteristics of the driving population were given by the 1973 National Roadside Breath Testing Survey (Wolfe 1974). Using Bayes theorem and logistic regression analysis, the effect of changing driver characteristics on the probability of a fatality was explored. The method used is proposed for a case-control study in which the controls may not accurately represent the population from which the cases were drawn. Risk factors identified are generally in agreement with previous reports.

Accidents, Traffic↗
PubMed2001

A sensitivity analysis for publication bias in systematic reviews.

There is no simple method of correcting for publication bias in systematic reviews. We suggest a sensitivity analysis in which different patterns of selection bias can be tested against the fit to the funnel plot. Publication bias leads to lower values, and greater uncertainty, in treatment effect estimates. Two examples are discussed. An appendix lists the S-plus code needed for carrying out the analysis.

Antibiotic Prophylaxis↗
PubMed2006

Adjoint sensitivity analysis for a three-dimensional photochemical model: application to Southern California.

An adjoint method was used to investigate the sensitivity of peak ozone at selected sites in Southern California to nearly 900 model inputs including surface emissions, reaction rate coefficients, dry deposition velocities, boundary conditions, and initial conditions. Simulations showed large changes in ozone and ozone sensitivities at three sites investigated between summers 1987 and 1997 due to emission reductions. However, only small changes in ozone and ozone sensitivities were predicted between 1997 and 2010. Sensitivities of the differences in ozone between simulations with different emission scenarios were calculated and compared to sensitivities of ozone in each simulation. In some cases, the sensitivities of ozone differences were smaller than those of ozone itself, but in other cases, such as when the sensitivityto NOx emissions changed sign, sensitivities of differences were larger. The adjoint method was most useful for determining when and where model inputs affect, or have the potential to affect, an ozone response. For example, the method was used to plot the spatial distribution of important emission source regions to 1-hour versus 8-hour peak ozone. Changes in the distribution and sign of the adjoint function for emitted species revealed changes in the area of influence of pollutant emissions on peak ozone due to emission controls. The adjoint method provides useful information complementary to that obtained from forward sensitivity analysis methods.

Air Pollutants↗
PubMed2001

Sensitivity analysis, Monte Carlo risk analysis, and Bayesian uncertainty assessment.

Standard statistical methods understate the uncertainty one should attach to effect estimates obtained from observational data. Among the methods used to address this problem are sensitivity analysis, Monte Carlo risk analysis (MCRA), and Bayesian uncertainty assessment. Estimates from MCRAs have been presented as if they were valid frequentist or Bayesian results, but examples show that they need not be either in actual applications. It is concluded that both sensitivity analyses and MCRA should begin with the same type of prior specification effort as Bayesian analysis.

Bayes Theorem↗
PubMed2000

A sensitivity analysis on the atmospheric transformation and deposition of mercury in north-eastern USA

This paper presents the results of a sensitivity analysis on the factors that affect dry and wet deposition of atmospheric mercury (Hg), using a regional scale air quality model. Simulations were conducted for the north-eastern USA during a summer week and a winter week in 1997. Simulation results for the summer week and the winter week in general showed similar responses to changes in emission, environmental conditions, and alternative chemical mechanisms. Reduction of the ambient concentrations of soot or ozone was shown to reduce the wet deposition of Hg. When averaged over the summer and the winter week, the total deposition to the simulation domain would be reduced by 26% by reducing Hg emission from anthropogenic sources within the domain by 50%. For individual grids, however, only locations near local sources obtained noticeable reductions in ambient concentration and wet deposition due to the influence of re-emission from the natural surfaces and regional/global scale transport. The reduction in deposition would reach 36% if all Hg(II) emitted from anthropogenic sources were attached to particles. The total deposition was predicted to decrease by 22% when the gas phase Hg(II)-Hg(p) partitioning was included in the model. Only small changes in total deposition were observed by including the gas-phase ozone-Hg(0), reaction and the aqueous phase chlorine-Hg(0), reaction, and by lowering ambient concentrations of Hg(II) and Hg(p) at the upper lateral boundaries. During the summer week, Hg(II) deposition contributed 40% or more to the total deposition. The contribution increased to 70% in the winter week.

Journal Article↗
PubMed2004

Optimizing genetic circuits by global sensitivity analysis.

Artificial genetic circuits are becoming important tools for controlling cellular behavior and studying molecular biosystems. To genetically optimize the properties of complex circuits in a practically feasible fashion, it is necessary to identify the best genes and/or their regulatory components as mutation targets to avoid the mutation experiments being wasted on ineffective regions, but this goal is generally not achievable by current methods. The Random Sampling-High Dimensional Model Representation (RS-HDMR) algorithm is employed in this work as a global sensitivity analysis technique to estimate the sensitivities of the circuit properties with respect to the circuit model parameters, such as rate constants, without knowing the precise parameter values. The sensitivity information can then guide the selection of the optimal mutation targets and thereby reduce the laboratory effort. As a proof of principle, the in vivo effects of 16 pairwise mutations on the properties of a genetic inverter were compared against the RS-HDMR predictions, and the algorithm not only showed good consistency with laboratory results but also revealed useful information, such as different optimal mutation targets for optimizing different circuit properties, not available from previous experiments and modeling.

Algorithms↗
PubMedDate not supplied

Reanalysis of the Harvard Six Cities Study, part II: sensitivity analysis.

Following the validation and replication of the Harvard Six Cities Study (Krewski et al., this issue), we conducted a wide range of sensitivity analyses to explore the observed associations between long-term exposure to fine particle or sulfate air pollution and mortality. We examined the impact of alternative risk models on estimates of risk, taking into account covariates not included in the original analyses. These risk models provided a basis for identifying covariates that may confound or modify the association between fine particle or sulfate air pollution and mortality, and for identifying sensitive population subgroups. The possibility of confounding due to occupational exposures was also investigated. Residence histories were coded for the study subjects and were used to examine temporal patterns of exposure and risk. Our sensitivity analyses showed the mortality risk estimates for fine particle and sulfate air pollution to be highly robust against alternative risk models of the Cox proportional hazards family, including models with additional covariates from the original questionnaires not included in the original published analyses. There was limited evidence of departures from the proportional hazards assumption. Flexible exposure-response models provided some evidence of departures from linearity at both low and high sulfate concentrations. Incorporating information on changes over time in cigarette smoking and body mass index had little effect on the association between fine particles and mortality. There was limited evidence of variation in risk with attained age, gender, smoking status, occupational exposure to dust and fumes, marital status, heart or lung diseases, or lung function. However, air pollution risk did appear to decreasing with increasing educational attainment. Extensive adjustment for occupation using aggregate indices of occupational "dirtiness" and occupational exposure to known lung carcinogens had little impact on the mortality risks associated with particulate air pollution. Our evaluation of population mobility indicated that relatively few subjects moved from their original city of residence. Attempts to identify critical exposure time windows were limited by the lack of marked interindividual variation in temporal exposure patterns throughout the study period. Overall, this extensive sensitivity analysis both supported the conclusions reached by the original investigators and demonstrated the robustness of these conclusions to alternative analytic approaches.

Age Factors↗
PubMed2006

Parameter sensitivity analysis for activated sludge models No. 1 and 3 combined with one-dimensional settling model.

The aim of this study was to suggest a sensitivity analysis technique that can reliably predict effluent quality and minimize calibration efforts without being seriously affected by influent composition and parameter uncertainty in the activated sludge models No. 1 (ASM1) and No. 3 (ASM3) with a settling model. The parameter sensitivities for ASM1 and ASM3 were analyzed by three techniques such as SVM-Slope, RVM-SlopeMA, and RVM-AreaCRF. The settling model parameters were also considered. The selected highly sensitive parameters were estimated with a genetic algorithm, and the simulation results were compared as deltaEQ. For ASM1, the SVM-Slope technique proved to be an acceptable approach because it identified consistent sensitive parameter sets and presented smaller deltaEQ under every tested condition. For ASM3, no technique identified consistently sensitive parameters under different conditions. This phenomenon was regarded as the reflection of the high sensitivity of the ASM3 parameters. But it should be noted that the SVM-Slope technique presented reliable deltaEQ under every influent condition. Moreover, it was the simplest and easiest methodology for coding and quantification among those tested. Therefore, it was concluded that the SVM-Slope technique could be a reasonable approach for both ASM1 and ASM3.

Algorithms↗
PubMed2002

Sensitivity analysis for high quantiles of ochratoxin A exposure distribution.

Using available data from a consumption survey and contamination data on ochratoxin A (OA) in food, a sensitivity analysis (SA) for high quantiles (95th and 99th quantiles) of OA exposure distribution was carried out, obtained by a Monte Carlo simulation in French children. Exposure assessment for food contaminants is important to control the risk of foodborne diseases. Risk assessors are interested in high quantiles of contaminant exposure distributions. As these exposure distributions are generally very asymmetrical, it is difficult to obtain relevant and stable high quantiles in such a context. Determining OA exposure distribution is complex because it is based on the sum of elementary exposure distributions (eight foodstuffs are analysed here), and each one of these is the product of a consumption distribution and a contamination distribution. The SA enables us to quantify the influences of the parameter variability of the consumption and contamination probability density functions (pdf) which have been fitted to the data, our simulation model inputs, on the 95th and 99th quantiles of the output exposure distribution. After some preliminary trials, we have postulated a quadratic polynomial regression model for the quantiles of OA exposure distribution in view of undertaking this SA. This regression model comprises 32 main factors, their 496 two-factor interactions and their 32 quadratic terms. The 32 factors are the parameters of the fitted pdf: 16 parameters of Gamma distributions relative to the eight consumed foods and 16 parameters of Gamma distributions relative to the eight food OA contaminations. For an optimal parameter estimation of such a large model, we used an experimental design approach depending on a resolution-V fractional factorial design of 6561 experiments. The factor ranges are established by a preliminary study of bootstrap sampling. From the bootstrap samples, the factor ranges are obtained taking into account the correlation between the two parameters of the fitted Gamma pdf. A full exposure distribution is simulated for each of the 6561 experiments. The consumption dependencies are taken into account by the Iman and Conover method. On the basis of this analysis, validated and useful models for each desired quantile are obtained showing a major influence of the parameters of "Cereals" (consumption and contamination) and slightly less so for parameter of "Pork" consumption in the sensitivity of the quantiles.

Adolescent↗
PubMed2005

Publication bias in research synthesis: sensitivity analysis using a priori weight functions.

Publication bias, sometimes known as the "file-drawer problem" or "funnel-plot asymmetry," is common in empirical research. The authors review the implications of publication bias for quantitative research synthesis (meta-analysis) and describe existing techniques for detecting and correcting it. A new approach is proposed that is suitable for application to meta-analytic data sets that are too small for the application of existing methods. The model estimates parameters relevant to fixed-effects, mixed-effects or random-effects meta-analysis contingent on a hypothetical pattern of bias that is fixed independently of the data. The authors illustrate this approach for sensitivity analysis using 3 data sets adapted from a commonly cited reference work on research synthesis (H. M. Cooper & L. V. Hedges, 1994).

Data Interpretation, Statistical↗
PubMed1978

Parameter sensitivity analysis for designing experiments in kinetics.

For the Michaelis-Menten binding kinetics and for a two- and a three-compartmental model a parameter sensitivity analysis is performed to design experiments. According to the mathematical results in the case of the binding kinetics the substrate concentrations s approximately Km are optimal in view to the variance of the Michaelis constant Km. In the case of the two-compartment model the results of the calculations are tested by the use of computer-simulated data. The correspondence between the sensitivity and simulation results is satisfactory.

Computers↗
PubMed1984

Sensitivity analysis for an improved estimation of respiratory mechanics parameters.

In this paper a respiratory mechanics model is considered, which is characterized by a biquadratic input impedance, and a sensitivity analysis has been carried out to determine the influence of experimental conditions on parameter estimation. This analysis was effected with data obtained experimentally, in three different patients under intermittent positive pressure ventilation. In all three cases, the model's input impedance demonstrated a maximum sensitivity in relation to the various parameters included in the field of frequencies from 0 to 10 Hz. This seems to suggest therefore, that the use of a low-pass filter with a cut-off frequency equal to 10 Hz could improve the signal/noise ratio and, consequently, the accuracy of the estimation of the parameters. Furthermore, the use of a system input with a bandwidth of 0-10 Hz provides the experimental conditions, under which good estimates of the parameters can be obtained. This conclusion has also been confirmed by simulation studies which have been conducted with different types of input signals.

Biomechanical Phenomena↗
PubMed2004

Numerical methods for improving sensitivity analysis and parameter estimation of virus transport simulated using sorptive-reactive processes.

Using one- and two-dimensional homogeneous simulations, this paper addresses challenges associated with sensitivity analysis and parameter estimation for virus transport simulated using sorptive-reactive processes. Head, flow, and conservative- and virus-transport observations are considered. The paper examines the use of (1) observed-value weighting, (2) breakthrough-curve temporal moment observations, and (3) the significance of changes in the transport time-step size. The results suggest that (1) sensitivities using observed-value weighting are more susceptible to numerical solution variability, (2) temporal moments of the breakthrough curve are a more robust measure of sensitivity than individual conservative-transport observations, and (3) the transport-simulation time step size is more important than the inactivation rate in solution and about as important as at least two other parameters, reflecting the ease with which results can be influenced by numerical issues. The approach presented allows more accurate evaluation of the information provided by observations for estimation of parameters and generally improves the potential for reasonable parameter-estimation results.

Adsorption↗
PubMed2001

Sensitivity analysis of the urinary excretion of plutonium.

The transfer parameters of a new age-related model for plutonium metabolism, which have the largest influence on urinary excretion, are determined by applying a sensitivity analysis and assuming a direct uptake into blood. Realistic cases of contamination via ingestion and inhalation were considered as well and sensitivity coefficients for non-systemic parameters were calculated. The most important parameters in relation to the path of intake are identified and the effects of the modifying factors proposed by the ICRP in the case of alteration of respiratory tract physiology are briefly examined.

Absorption↗
PubMedDate not supplied

Phantom pain: a sensitivity analysis.

PURPOSE: To analyse how decisions to dichotomise the frequency and impediment of phantom pain into absent and present influence the outcome of studies by performing a sensitivity analysis on an existing database. METHOD: Five hundred and thirty-six subjects were recruited from the database of an orthopaedic workshop and filled out a questionnaire in which the following items were assessed: demographics, side, date, level and reason of amputation, presence and frequency of phantom sensations, phantom pain and stump pain, and impediment due to phantom pain. RESULTS: The prevalence of phantom pain ranged from 7-72% when different cut off points for the frequency of phantom pain were applied. The significance of the various risk factors for the prevalence of phantom pain changed when different cut off points were applied. Only stump pain and phantom sensations were significant risk factors for all cut off points. Risk factors for the impediment of phantom pain changed when different cut off points were applied and these risk factors were different from those for the prevalence of phantom pain. CONCLUSION: The choice of cut off points influences the outcome of phantom pain studies considerably. This study provides some insight into the differences in prevalence and risk factors found in literature.

Adult↗
PubMed1988

Latin hypercube sampling and the sensitivity analysis of a Monte Carlo epidemic model.

Discrete, algorithmic simulation and Monte Carlo methodologies are currently used in population biology, connectionist cognitive modeling, and physics. However, little is typically known about the sensitivity of such models to changes in the values of the model features. Traditional methods of sensitivity analysis for systems of differential equations do not apply. Sometimes, one or two parameters are modified at a time in an ad hoc fashion in an attempt to assess sensitivity. To include more model features and their interactions in a sensitivity study, while limiting computer utilization, various sampling methods have been suggested. In this article, a sensitivity study based on a Latin hypercube (LH) sampling design is compared with a similar study using a full factorial (FF), fixed-point sample. A discrete, Monte Carlo model of epidemics of influenzavirus infections in a human community is used for illustrative purposes. Although the FF scheme used over 14 times as many samples as the LH sampling one, both provided comparable predictive ability and comparable information about simulation sensitivity to model features.

Adolescent↗