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At least 163 records · Page 9Linked to original sources
PubMed1991

Serial lung model for simulation and parameter estimation in body plethysmography.

A serial lung model with a compressible segment has been implemented to simulate different types of lung and airway disorders such as asthma, emphysema, fibrosis and upper airway obstruction. The model described can be used during normal breathing, and moreover the compliant segment is structured according to more recent physiological data. A parameter estimation technique was applied and its reliability and uniqueness were tested by means of sine wave input signals. The characteristics of the alveolar pressure/flow patterns simulated with the model agree to a great extent with those found in the literature. In the case of absence of noise the parameter estimation routine produced unique solutions for different simulated pathologic classes. The sensitivity of the different parameters depended on the values belonging to each class of pathology. Some more simplified models are presented and their advantages over the complex model in special types of pathology are demonstrated. Noise added to the simulated flow appeared to have no influence on the estimated parameters, in contradiction to the effects with noise added to the pressure signal. In that case effective resistance was accurately estimated. Where parameters had no influence, as for instance upper airway resistance in emphysema or peripheral airway resistance in upper airway obstruction, the measurement accuracy was less. In all other cases, a satisfactory accuracy could be obtained.

Humans↗
PubMed1990

A new approach to pharmacokinetic parameters: estimation of cefuroxime during haemodialysis.

A linear three-compartment model is proposed as a means of estimating disposition and extracorporeal elimination pharmacokinetic parameters during haemodialysis. It was created by connecting a compartment, corresponding to the amount of drug present in the dialysis cell, to the central compartment of the standard two-compartment model from which elimination would normally take place. The transfer rate constants between the central compartment and the 'dialysis cell' and vice versa were given in terms of the plasma flow, central compartment volume, and volume of plasma contained in the dialysis cell, and thus cannot be considered to be independent parameters. The product of the rate constant for elimination from the dialysis cell, k30, and the plasma volume within the cell was used to quantify the extracorporeal elimination and termed intrinsic dialyser clearance, CLint,D. The model was used to interpret the plasma level curves obtained after administering 750 mg cefuroxime by IV bolus to a group of patients undergoing haemodialysis. The distribution parameters estimated by non-linear regression were similar to those given in the literature; however, in five of the twelve cases, no estimate of cefuroxime elimination from the central compartment (k10) could be derived. On the other hand, the estimates of the elimination rate constant (k30) from the deep peripheral compartment were greater than the values given in the literature, ranging from 50.6 h-1 to 151.0 h-1 and CLint,D ranging from 3.1 h-1 to 8.90 l h-1. The error in measuring the flow of plasma which reaches the dialyser, its influence upon the estimation of the model parameters and the relationship between the parameters themselves and those customarily used in the study of drug haemodialysis are all discussed.

Cefuroxime↗
PubMed2004

Dynamic simulation and parameter estimation in river streams.

Predictions and quality management issues for environmental protection in river basins rely on water-quality models. The key step in model calibration and verification is obtaining the right values of the model parameters. Current practice in model calibration is such that the reaction coefficients are adjusted by trial-and-error until the predicted values and measured data are within a pre-selected margin of error, and this may be a very time consuming task. This study is directed towards developing a parameter estimation strategy coupled with the simulation of water quality models so that the heavy burden of finding reaction rate coefficients is overcome. Dynamic mass balances for different forms of nitrogen and phosphorus, biological oxygen demand, dissolved oxygen, coliforms, nonconservative constituent and algae were written for a single computational element. The model parameters conforming to those in QUAL2E water quality model were estimated by a nonlinear multi-response parameter estimation strategy coupled with a stiff integrator. Yesilirmak river basin around the city of Amasya in Turkey served as the prototype system for the model development. Samples were collected simultaneously from two stations, and concentrations of many water-quality constituents were determined either on-site or in laboratory. This dynamic data was then used for numerical parameter estimation during computer simulation. When the model was simulated with the estimated parameters, it was seen that the model was quite able to predict the dynamics of major water quality constituents. It is concluded that the proposed method shows promise for automatically generating reliable estimates of model parameters.

Humans↗
PubMed2005

Experimental design for optimal parameter estimation of an enzyme kinetic process based on the analysis of the Fisher information matrix.

The investigation of enzyme kinetics is increasingly important, especially for finding active substances and understanding intracellular behaviors. Therefore, the determination of an enzyme's kinetic parameters is crucial. For this a systematic experimental design procedure is necessary to avoid wasting time and resources. The parameter estimation error of a Michaelis-Menten enzyme kinetic process is analysed analytically to reduce the search area as well as numerically to specify the optimum for parameter estimation. From analytical analysis of the Fisher information matrix the fact is obtained, that an enzyme feed will not improve the estimation process, but substrate feeding is favorable with small volume flow. Unconstrained and constrained process conditions are considered. If substrate fed-batch process design is used instead of pure batch experiments the improvements of the Cramer-Rao lower bound of the variance of parameter estimation error reduces to 82% for mu(max) and to 60% for K(m) of the batch values in average.

Animals↗
PubMed2004

[Study on the calculation methods of confidence intervals of genetic parameter estimates by method R and number of repeating estimations].

The objective of the study was to probe the calculation methods of confidence intervals of genetic parameter estimates by Method R and influences of number of repeating estimations (NORE). Four models were used to generate the datasets by simulation. The datasets originated from 200 sires and 2 000 dams and simulation progressed by BLUP( Best Linear Unbiased Prediction) selection for five overlapping generations. Variance components were estimated by using multivariate multiplicative iteration (MMI), combined with preconditioned conjugate gradient (PCG) to solve the mixed model equations. Parameter estimates and their standard errors and confidence intervals were computed by classical method, classical method after Box-Cox transformation and bootstrapping. The results showed that when NORE was larger, all three methods were feasible, but when NORE was fewer, bootstrapping was recommended. Fewer NORE was feasible under simple models, but more NORE was needed for complex models. Direct heritabilities were overestimated with the increase of number of random effects, but underestimation was found for many parameters with the increase of iteration numbers of PCG and MMI.

Animals↗
PubMed1996

Multiple solutions, illegal parameter values, local minima of the sum of squares, and anomalous parameter estimates in least-squares fitting of the two-compartment pharmacokinetic model with absorption.

When the two-compartment model with absorption is fitted to data by nonlinear least squares, in general six different outcomes can be obtained, arising from permutation of the three exponential rate constants. The existence of multiple solutions in this sense is analogous to the flip-flop phenomenon in the one-compartment model. It is possible for parameter estimates to be inconsistent with the underlying physical model. Methods for recognizing such illegal estimates are described. Other common difficulties are that estimated values for two of the rate constants are almost identical with very large standard deviations, or that the parameter estimation algorithm converges poorly. Such unwanted outcomes usually signal a local (false) minimum of the sum of squares. They can be recognized from the ratio of largest to smallest singular value of the Jacobian matrix, and are, in principle, avoidable by starting the estimation algorithm with different initial values. There also exists a class of data sets for which all outcomes of fitting the usual equations are anomalous. A better fit to these data sets (smaller sum of squares) is obtained if two of the relevant rate constants are allowed to take complex conjugate values. Such data sets have usually been described as having "equal rate constants." A special form of the model equation is available for parameter estimation in this case. Precautions relating to its use are discussed.

Least-Squares Analysis↗
PubMed2005

Reliability of parameter estimation in respirometric models.

When modelling a biochemical system, the fact that model parameters cannot be estimated exactly stimulates the definition of tests for checking unreliable estimates and design better experiments. The method applied in this paper is a further development from Marsili-Libelli et al. [2003. Confidence regions of estimated parameters for ecological systems. Ecol. Model. 165, 127-146.] and is based on the confidence regions computed with the Fisher or the Hessian matrix. It detects the influence of the curvature, representing the distortion of the model response due to its nonlinear structure. If the test is passed then the estimation can be considered reliable, in the sense that the optimisation search has reached a point on the error surface where the effect of nonlinearities is negligible. The test is used here for an assessment of respirometric model calibration, i.e. checking the experimental design and estimation reliability, with an application to real-life data in the ASM context. Only dissolved oxygen measurements have been considered, because this is a very popular experimental set-up in wastewater modelling. The estimation of a two-step nitrification model using batch respirometric data is considered, showing that the initial amount of ammonium-N and the number of data play a crucial role in obtaining reliable estimates. From this basic application other results are derived, such as the estimation of the combined yield factor and of the second step parameters, based on a modified kinetics and a specific nitrite experiment. Finally, guidelines for designing reliable experiments are provided.

Bacteria↗
PubMed1991

The effects of measurement errors in the plasma radioactivity curve on parameter estimation in positron emission tomography.

The effects of three error sources in plasma curve measurements on parameter estimation in kinetic analysis of positron emission tomography (PET) fluorodeoxyglucose (FDG) data are investigated by computer simulation. The three error sources are: (1) measurement noise in the radioactivity concentrations of plasma samples; (2) linear interpolation between adjacent plasma sampling points of the plasma time activity curve; and (3) incorrect weights used for the least-squares regression. All three error sources are found to increase the variability of the parameter estimates, with the first one a primary error source in normal PET FDG studies. The performance of five estimation methods which account for the error sources are evaluated. When the noise variances of the plasma and the tissue measurements are not known, an iterative weighting procedure is shown to give accurate and reliable estimates.

Computer Simulation↗
PubMed1998

Ignorability and parameter estimation in longitudinal pharmacokinetic studies.

In the analysis of longitudinal pharmacokinetic data, both balanced (equal number of samples per subject) and unbalanced data are used. It is implicitly assumed that the process that caused the missing data can be ignored. A simulation study was performed to determine the effect of ignoring the missing data (i.e., "ignorability") on the accuracy and precision of parameter estimation in longitudinal pharmacokinetic studies. A two-compartment model with multiple intravenous bolus inputs was assumed. Subjects with balanced data sets had six samples, and those with unbalanced data had 1 to 5 samples missing (i.e., supplied in a decreasing order from 5 to 1 samples). The proportion of subjects with 1 to 5 samples missing varied from 25% to 75% in a fixed sample size of 100. The effect of ignorability was studied at intersubject variability ranging from 15% to 60% for a drug assumed to be dosed at its elimination half-life. One hundred replicate data sets of 100 subjects each were simulated for each missing data scenario. The accuracy of parameter estimation was not significantly affected by the amount of ignorable missing data at any given level of variability. However, the precision of parameter estimation was affected by the degree of "missingness."

Humans↗
PubMedDate not supplied

Modeling macropore flow effects on pesticide leaching: inverse parameter estimation using microlysimeters.

Macropore flow is a key factor determining pesticide fate, but models accounting for this process need parameters that cannot be easily measured. This study was conducted to investigate the use of inverse techniques to estimate parameters controlling macropore flow and pesticide fate in the dual-permeability model MACRO. Undisturbed columns were sampled at three landscape positions (hilltop, slope, hollow) with contrasting texture and organic carbon content. Transient leaching experiments were performed for an anionic tracer and the herbicide MCPA (4-chloro-2methylphenoxy acetic acid) during a 4-mo period, first under natural rainfall, and then under controlled irrigation in the laboratory. The tracer breakthrough for the liner-textured soil from the hilltop showed strong evidence of macropore flow, resulting in a rapid leaching of MCPA, while leaching was minimal from the organic-rich hollow soil, since macropore flow was weaker and adsorption stronger. The MACRO model was linked to the inverse modeling program SUFI (Sequential Uncertainty Fitting) to enable calibration of nine key model parameters. Based on calculated model efficiencies, MACRO-SUFI gave generally good predictions of water movement and tracer and pesticide transport, although some errors were attributed to difficulties in simulating the effects of soil moisture on degradation and the timing of water outflows. Even after calibration, significant uncertainties remained for some key parameters controlling macropore flow. Nevertheless, the parameter estimates were significantly different between landscape positions and could also be related to basic soil properties. The posterior uncertainty ranges could probably be reduced with a more exhaustive sampling of the parameter space and improved experimental designs.

2-Methyl-4-chlorophenoxyacetic Acid↗
PubMed1992

Effects of violating local independence on IRT parameter estimation for the Binomial Trials Model.

The appropriateness of the Binomial Trials Model for test data that consist of multiple attempts of the same item needs to be determined because the presence of learning or fatigue effects may violate the model's assumption of local independence. The purpose of this study was to determine what effect the severity of the violation of local independence (VLI), coupled with different sample size (SS), test length (TL), and test difficulty (TD) had on the estimation of the model difficulty parameter, b, using computer simulation techniques. Each of the following conditions was replicated 100 times under a completely crossed design: SS (100, 200, 500, 2,000); TL (5, 10, 20, 25 attempts); TD (-1.2, 0.0, 1.2); and VLI (from no violation to complete violation). Examinee ability or latent trait was pseudorandomly drawn from a standard normal distribution, and the b-parameter was estimated using a maximum likelihood procedure on generated test scores. Regardless of SS, TL, and TD, the b-parameter tended to be overestimated for situations in which the VLI condition simulated fatigue and underestimated when the VLI condition simulated late-test learning or practice effect. The findings suggest that violations of local independence, at least as simulated in this study, could seriously bias the difficulty parameter estimates if all examinees tested exhibited the dependency.

Bias↗
PubMedDate not supplied

An investigation of numerical grid effects in parameter estimation.

Modern ground water characterization and remediation projects routinely require calibration and inverse analysis of large three-dimensional numerical models of complex hydrogeological systems. Hydrogeologic complexity can be prompted by various aquifer characteristics including complicated spatial hydrostratigraphy and aquifer recharge from infiltration through an unsaturated zone. To keep the numerical models computationally efficient, compromises are frequently made in the model development, particularly, about resolution of the computational grid and numerical representation of the governing flow equation. The compromise is required so that the model can be used in calibration, parameter estimation, performance assessment, and analysis of sensitivity and uncertainty in model predictions. However, grid properties and resolution as well as applied computational schemes can have large effects on forward-model predictions and on inverse parameter estimates. We investigate these effects for a series of one- and two-dimensional synthetic cases representing saturated and variably saturated flow problems. We show that "conformable" grids, despite neglecting terms in the numerical formulation, can lead to accurate solutions of problems with complex hydrostratigraphy. Our analysis also demonstrates that, despite slower computer run times and higher memory requirements for a given problem size, the control volume finite-element method showed an advantage over finite-difference techniques in accuracy of parameter estimation for a given grid resolution for most of the test problems.

Calibration↗
PubMed2000

The influence of tomograph sensitivity on kinetic parameter estimation in positron emission tomography imaging studies of the rat brain.

We investigated the influence of tomograph sensitivity on reliability of parameter estimation in positron emission tomography studies of the rat brain. The kinetics of two tracers in rat striatum and cerebellum were simulated. A typical injected dose of 10 MBq and a reduced dose of 1 MBq were assumed. Kinetic parameters were estimated using a region of interest (ROI) analysis and two pixel-by-pixel analyses. Striatal binding potential was estimated as a function of effective tomograph sensitivity (S(eff)) using a simplified reference tissue model. A S(eff) value of > or =1% was required to ensure reliable parameter estimation for ROI analysis and a S(eff) of 3-6% was required for pixel-by-pixel analysis. We conclude that effective tomograph sensitivity of 3% may be an appropriate design goal for rat brain imaging.

Animals↗
PubMed2005

Parameter estimation in the magnitude-only and complex-valued fMRI data models.

In functional magnetic resonance imaging, voxel time courses are complex-valued data but are traditionally converted to real magnitude-only data ones. At a large signal-to-noise ratio (SNR), the magnitude-only data Ricean distribution is approximated by a normal distribution that has been suggested as reasonable in magnitude-only data magnetic resonance images for an SNR of 5 and potentially as low as 3. A complex activation model has been recently introduced by Rowe and Logan [Rowe, D.B., and Logan, B.R. (2004). A complex way to compute fMRI activation. NeuroImage, 23 (3):1078-1092] that is valid for all SNRs. The properties of the parameter estimators and activation statistic for these two models and a more accurate Ricean approximation based on a Taylor series expansion are characterized in terms of bias, variance, and Cramer-Rao lower bound. It was found that the unbiased estimators in the complex model continued to be unbiased for lower SNRs while those of the normal magnitude-only data model became biased as the SNR decreased and at differing levels for the regression coefficients. The unbiased parameter estimators from the approximate magnitude-only Ricean Taylor model were unbiased to lower SNRs than the magnitude-only normal ones. Further, the variances of the parameter estimators achieved their minimum value in the complex data model regardless of SNR while the magnitude-only data normal model and Ricean approximation using a Taylor series did not as the SNR decreased. Finally, the mean activation statistic for the complex data model was higher and not SNR dependent while it decreased with SNR in the magnitude-only data models but less so for the approximate Ricean model. These results indicate that using the complex data model and not approximations to the true magnitude-only Ricean data model is more appropriate at lower SNRs. Therefore, since the computational cost is relatively low for the complex data model and since the SNR is not inherently known a priori for all voxels, the complex data model is recommended at all SNRs.

Algorithms↗
PubMed1993

Relationship between dipole parameter estimation errors and measurement conditions in magnetoencephalography.

The relationship between dipole parameter estimation errors and measurement conditions in magnetoencephalography is determined by computer simulation. The model uses a single current dipole in a spherical homogeneous medium. Dipole parameters are estimated using a moving dipole procedure. Signal-to-noise ratio (SNR) is defined as the square-root of the ratio of the average signal power to the average noise power over all measurement points. At SNR > 20, accurate estimation can be carried out independently of dipole depth and coil size. At SNR < 20, dipole depth influences estimation error. When the dipole is located near the center of the sphere, the measurement region should include both extrema of the magnetic field to minimize estimation error. However, when the dipole is not so deep, the position of the measurement region does not influence estimation error. When SNR < 4, estimation error increases as coil size increases. Coil size minimizing estimation error is determined by the ratio of environmental magnetic field noise to electrical noise. For a constant size of measurement region, increasing the number of measurement points decreases estimation error to a certain level. This error level depends on SNR. The number of measurement points required to minimize estimation error also depends on SNR.

Computer Simulation↗
PubMed1990

Evaluation of population (NONMEM) pharmacokinetic parameter estimates.

The application of population pharmacokinetic analysis has received increasing attention in the last few years. The main goal of this report is to make investigators aware of the necessity of independent evaluation of the results obtained from a population analysis based on observational studies. We also describe with the help of a specific example (a new synthetic opiate Alfentanil) how such evaluation can be performed for parameter estimates obtained with the software system NONMEM. The method differs depending on the type of serum concentration data that are used for the evaluation. A general method is described, based on the regression model used in NONMEM, that can test for bias in the estimates of fixed and random effects independent of the number of observations per patient and dosing. Since the procedure for testing for statistically significant bias in the prediction of the average concentration and its variability can be relatively complex, we propose that generally available program packages performing estimation of the pharmacokinetic parameters from observational data should contain the necessary software to evaluate the reliability of the parameter estimates on a second data set.

Alfentanil↗
PubMedDate not supplied

An improved parameter estimation method for Hodgkin-Huxley models.

We consider whole-cell voltage-clamp data of isolated currents characterized by the Hodgkin-Huxley paradigm. We examine the errors associated with the typical parameter estimation method for these data and show them to be unsatisfactorally large especially if the time constants of activation and inactivation are not sufficiently separated. The size of these errors is due to the fact that the steady-state and kinetic properties of the current are estimated disjointly. We present an improved parameter estimation method that utilizes all of the information in the voltage-clamp conductance data to estimate steady-state and kinetic properties simultaneously and illustrate its success compared to the standard method using simulated data and data from P. interruptus shal channels expressed in oocytes.

Animals↗
PubMed1997

Averaging pharmacokinetic parameter estimates from experimental studies: statistical theory and application.

In most experimental pharmacokinetic studies, parameter estimates are computed separately for each subject, then averaged across subjects. Average estimators for ratios and functions of parameters are often of interest; examples include half-life and clearance. For these parameters, recommendations regarding averaging using the arithmetic versus the harmonic mean have been based on computer simulations.1-3 The goal in this paper was to demonstrate that these empirically generated results can be derived using approximations for the expected values of reciprocals and ratios. We first consider estimating the reciprocal of a parameter, and predict the earlier simulation results for half-life. We additionally predict results for clearance when computed as dose divided by area under the curve. Next we consider estimating the ratio of two parameters, and predict the earlier simulation results for clearance in a first-order exponential model. As a further example, we predict results for the mean residence time in noncompartmental analysis. These approximations provide a unifying approach that can be used to determine optimal summary estimators, without the need for extensive computer simulations.

Models, Chemical↗