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At least 397 records · Page 22Linked to original sources
PubMed1995

Accuracy of noncompartmental pharmacokinetic parameters estimated from bolus injection and steady-state infusion data.

A Monte Carlo simulation study was carried out to examine the accuracy of parameters derived from curve moments. Impulse response (IR) and washout (WO) concentration-time curves, based on a triexponential model, were analyzed by numerical integration and regression analysis. Both designs were tested according to their robustness to measurement error and model misspecification. Performance of the methods was judged using the median error (ME) and the median absolute error (MAE) of 1000 simulations. The WO design provided better estimates of mean disposition residence time and worse estimates of the normalized variance of disposition residence times (CVD2) than its rival. At 20% measurement noise, the MAE of CVD2 was less than 13%. The WO design was much more robust to model misspecification. Numerical integration performed as good as, or better than, regression analysis. Both methods are very sensitive to tail-area error, meaning that special attention needs to be paid to this aspect of experimental design. This study demonstrates that it is possible to obtain good estimates of higher moment parameters in a well-designed experiment.

Models, Theoretical↗
PubMedDate not supplied

Robust nonlinear autoregressive moving average model parameter estimation using stochastic recurrent artificial neural networks.

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals.

Algorithms↗
PubMed1986

Kinetic model of glucose-6-phosphate dehydrogenase from red blood cells. Parameter estimation from progress curves and simulation of regulatory properties.

A kinetic model of human and mouse glucose-6-phosphate dehydrogenase is presented which takes into account the substrates and all inhibitors of significant importance in the red cell. The parameter values were estimated by analysis of progress curves. The applicability of a new method based on non-linear regression to complex enzyme kinetics was proved. The in vivo-regulation of glucose-6-phosphate dehydrogenase is examined by determining elasticity coefficients and by using simple simulation experiments. The model is convenient to describe the behaviour of enzyme activity under physiological conditions.

Adenosine Triphosphate↗
PubMed1985

Pharmacokinetic calculator program for generation of initial parameter estimates from a three-compartment infusion model.

A polyexponential curve-stripping program, KIN, is described for use on the HP-41CV programmable calculator. The program may be used in the analysis of plasma-concentration-time curves for a three-compartment intravenous bolus or infusion model with linear elimination processes. The coefficients and hybrid rate constants of the exponential function are then used to compute pharmacokinetic parameters (volume of the central compartment, intercompartmental rate transfer constants), which may be used as initial estimates of model parameters in non-linear regression curve-fitting procedures.

Computers↗
PubMed1999

All maps of parameter estimates are misleading.

Maps are frequently used to display spatial distributions of parameters of interest, such as cancer rates or average pollutant concentrations by county. It is well known that plotting observed rates can have serious drawbacks when sample sizes vary by area, since very high (and low) observed rates are found disproportionately in poorly-sampled areas. Unfortunately, adjusting the observed rates to account for the effects of small-sample noise can introduce an opposite effect, in which the highest adjusted rates tend to be found disproportionately in well-sampled areas. In either case, the maps can be difficult to interpret because the display of spatial variation in the underlying parameters of interest is confounded with spatial variation in sample sizes. As a result, spatial patterns occur in adjusted rates even if there is no spatial structure in the underlying parameters of interest, and adjusted rates tend to look too uniform in areas with little data. We introduce two models (normal and Poisson) in which parameters of interest have no spatial patterns, and demonstrate the existence of spatial artefacts in inference from these models. We also discuss spatial models and the extent to which they are subject to the same artefacts. We present examples from Bayesian modelling, but, as we explain, the artefacts occur generally.

Bayes Theorem↗
PubMed1984

A new method of parameter estimation from progress curves.

A mathematical procedure is presented which permits kinetic parameters to be determined from progress curve data. The method is applicable to any kind of enzymatic reaction which can be described by a single rate equation. A general criterion is derived to check the accuracy of the method. An application to a simulated 2-substrate reaction is given.

Half-Life↗
PubMed1984

Simulation studies of influenza epidemics: assessment of parameter estimation and sensitivity.

The influenza simulation model of Elveback et al is used to evaluate the accuracy of the maximum likelihood procedure of Longini et al for estimating the secondary attack rate in households. The sample population from the Tecumseh Respiratory Illness Study is mapped into the simulation model and simulations are carried out over a range of parameter values and conditions, some of which were derived from influenza seasons in Tecumseh and from the Seattle Flu Study for the years 1975-1980. The estimation procedure is found to be quite robust for parameter values preset within appropriate limits for influenza. However, a significant difference is found between the preset and estimated household contact parameter for epidemics of medium and high intensity when the preset value is zero. Incremental increases in the household contact parameter are shown to produce marked increases in the overall infection attack rate demonstrating that household spread is an important link in maintaining infection in other mixing groups such as schools, preschool groups and neighbourhood clusters of households.

Adolescent↗
PubMed2006

Assessment of dosing impact on intra-individual variability in estimation of parameters for basic indirect response models.

The application of D-optimization and the assessment of bias and precision of parameter estimates for four basic pharmacodynamic (PD) indirect response (IDR) models for ascending doses was examined using simulated data. While D-optimization provided four sampling times, each IDR model was used to generate eight data points per dose level. The PD parameters were: input rate constant (k (in)), disposition rate constant (k (out)), capacity constant (I (max) or S (max)), and sensitivity constant (IC (50) or SC (50)). A monoexponential pharmacokinetic function was applied with single doses increased by a factor of 10 to generate responses that vary from weak to fully saturable. For each dose, 100 replications of response data were simulated using independent normally distributed errors of CV = 20%. The original IDR model was fitted and PD parameters estimated. Histograms and descriptive statistics were generated. All parameters exhibited asymmetric distributions with positive coefficients of skewness except for I (max). Higher doses resulted in unbiased estimates of all PD parameters. The precision of parameter estimates improved with increasing doses except for IC (50) and SC (50) indicating that a single dose experimental design cannot be corrected by increasing dose in order to improve precision of estimates of IC (50) or SC (50). Highest variability was for IC (50) and SC (50) parameters. This study provides new insights into optimum study designs and recovery of parameters for basic IDR models.

Biological Availability↗
PubMed2004

A bayesian approach to parameter estimation for a crayfish (Procambarus spp.) bioaccumulation model.

Bioaccumulation models are used to describe chemical uptake and clearances by organisms. Averaged input parameter values are traditionally used and yield point estimates of model outputs. Hence, the uncertainty and variability of model predictions are ignored. Probabilistic modeling approaches, such as Monte Carlo simulation and the Bayesian method, have been recommended by the U.S. Environmental Protection Agency to provide a quantitative description of the degree of uncertainty and/or variability in risk estimates in ecological hazards and human health effects. In this study, a Bayesian analysis was conducted to account for the combined uncertainty and variability of model parameters in a crayfish bioaccumulation model. After a 5-d exposure in the LaBranche Wetlands (LA, USA), crayfish were analyzed for polycyclic aromatic hydrocarbon concentrations and lipid fractions. The posterior distribution of model parameters were derived from the joint posterior parameter distributions using a Markov chain Monte Carlo approach and the experimental data. The results were then used to predict the distribution of chrysene concentration versus time in the crayfish to compare the predicted ranges at the different study sites.

Animals↗
PubMed2006

Practical approach to parameter estimation for ASM3+ bio-P module applied to five-stage step-feed EBPR process.

Various parameter optimization approaches to a five-stage step-feed EBPR process modeled using the ASM3+bio-P module were examined. Five stoichiometric (Y(STO,NO), Y(H,O2, Y(H,NO,) Y(PAO,O2), Y(PO4)) and seven kinetic parameters (k(STO), eta(NO), b(H), mu(max),PAO, q(PHA), q(PP), mu(max),A) were estimated. The optimization approaches could be classified based on the data sources (batch experiments or CSTR operation data) and the number of target variables used in calculating the objective function. Optimized parameter values obtained by each approach were validated with CSTR operation data that were not used for parameter optimization. The results showed that the parameter optimization only with batch experimental results could not be directly applied to CSTR operation data. ASM3 + bio-P module parameters could be finely optimized only with CSTR operation data when sufficient target variables for objective function calculation were applied. When the number of target variables was increased, prediction performance was significantly improved. Once optimized, the model was able to predict the characteristic features of the five-stage step-feed process; namely, a high PAO yield, fast PAO growth, fast X(pp) storage, slow X(STO) and X(PHA) storage.

Bioreactors↗
PubMed1976

Parameter estimation in studying circadian rhythms.

In the model under consideration for the circadian rhythm study there are three unknown physiological parameters involved: the level, the amplitude and the phase. This paper concerns the estimation of the group level, group amplitude and group phase of a certain group of individuals based on their time series data. Special attention is paid to the amplitude and phase parameters, and solutions are obtained for both the small-sample and the large-sample cases.

Circadian Rhythm↗
PubMed1995

The regulation of malaria parasitaemia: parameter estimates for a population model.

Classical studies of non-immune individuals infected with Plasmodium falciparum reveal that the infection may be regulated for long periods at a relatively stable parasite density, despite the enormous growth potential of a parasite that continually replicates within host erythrocytes. This suggests that the parasite population may be controlled by density-dependent mechanisms, and in theory the most obvious of these is competition between parasites for host erythrocytes. Here we evaluate the role of this mechanism in the regulation of parasitaemia, by modelling the basic population interaction between parasites and erythrocytes in a form that allows all the essential parameters to be estimated from clinical data. Our results show that competition cannot account for the total regulation of P. falciparum, but when combined with immune mechanisms it may play a more important role than is generally supposed. Further analysis of the model indicates that in the long term, parasite replication at low parasite densities can contribute significantly to the high degree of anaemia observed in natural infection, a conclusion which is not obvious from simple clinical observation.

Anemia↗
PubMed2005

Dosimetric parameters estimation using PENELOPE Monte-Carlo simulation code: Model 6711 a 125I brachytherapy seed.

The dosimetric parameters for characterization of a low-energy interstitial brachytherapy source (125)I are examined. In this work, the radial dose function, g(r), anisotropy function F(r,theta), and the absolute dose rate, Lambda, around (125)I seed model 6711 have been estimated by means of the PENELOPE Monte-Carlo (MC) simulation code. The results obtained are in good agreement with the corresponding values recommended by TG-43 that are based in experimental and MC published results.

Brachytherapy↗
PubMed2002

A Bayesian approach to parameter estimation in HIV dynamical models.

In the context of a mathematical model describing HIV infection, we discuss a Bayesian modelling approach to a non-linear random effects estimation problem. The model and the data exhibit a number of features that make the use of an ordinary non-linear mixed effects model intractable: (i) the data are from two compartments fitted simultaneously against the implicit numerical solution of a system of ordinary differential equations; (ii) data from one compartment are subject to censoring; (iii) random effects for one variable are assumed to be from a beta distribution. We show how the Bayesian framework can be exploited by incorporating prior knowledge on some of the parameters, and by combining the posterior distributions of the parameters to obtain estimates of quantities of interest that follow from the postulated model.

Bayes Theorem↗
PubMed2005

[Genetic parameter estimation for inosine-5-monophosphate and intramuscular fat contents and other meat quality traits in chicken muscle].

The genetic parameters for some important flavor traits like inosine-5'-monophosphate (IMP) and intramuscular fat (IMF) contents in breast meat were estimated using a MTDFREML procedure on 1063 male, 90-day-old, purebred Beijing-You meat-type chicks (BJY). The result showed that the heritability of IMP and IMF contents in BJY breast meat was moderate or low (h2=0.23, 0.10), whereas these parameters were higher for abdominal fat weight (AFW), breast meat yield (BMY), ratio of BMY to carcass weight (BMR), leg muscle yield (LMY), body weight (BW), comb weight(CW) and comb weight percentage (CWB) (h2=0.56-0.79). The heritability of abdominal fat percentage (AFP), leg meat yield (LMY), testicle weight (TW) and testicle weight percentage (TWP) were 0.24, 0.32, 0.39 and 0.35, respectively. IMP exhibited low phenotypic correlations with BMY, LMY and SFT and no significant phenotypic correlations with other traits. IMF, to some extent, exhibited positive phenotypic correlation with BW, AFP, SFT and FSW (rP=0.11-0.33). In terms of genetic correlation, IMP was moderately or significantly negatively correlated with BW and CWP (rA=-0.38,-0.62), and a high level of positive correlation was observed with BMY (rA=0.57). Moreover, IMF was highly correlated with BW and AFW (rA=0.75,0.66), and moderately correlated with AFP and CWP (rA=0.32, 0.40). A low level of positive correlation was observed between IMP and IMF (rA =0.27). We propose that IMP and IMF contents in chicken meat could be increased with selection through line-breeding.

Abdominal Fat↗
PubMedDate not supplied

Model parameter estimation and analysis: understanding parametric structure.

We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

Algorithms↗
PubMed1997

Estimating parameters for psychometric functions using the four-point sampling method.

Although a psychometric function describing a subject's responses to some physical stimuli is of considerable value, characterizing such functions is time consuming and, hence, is not carried out routinely in psychophysical experiments. A principal reason for the lack of efficiency in characterizing a psychometric function is the use of sampling methods that either converge on a single point on the psychometric function, such as the PEST method, or which distribute observations uniformly over a wide range, such as the constant stimuli method. As an alternative, a multimodal four-point sampling method has been proposed [C. F. Lam, J. H. Mills, and J. R. Dubno, J. Acoust. Soc. Am. 99, 3689-3693 (1996)]. A psychometric function is then fitted to the four points (each with several trials) to estimate the threshold and slope parameters of the psychometric function. Adaptive methods, such as the up-down methods [H. Levitt, J. Acoust. Soc. Am. 49, 467-477 (1971)], can be used to provide good initial estimates of the threshold and spread parameters of a psychometric function described by a logistic function. In ongoing studies of age-related changes in auditory masking and discrimination, this new four-point sampling method has been applied to determine psychometric functions for absolute thresholds as a function of duration, thresholds in simultaneous and forward masking, frequency discrimination, and intensity discrimination in both young and aged human subjects. Results indicate that a reduction in data collection time of about 50% with no increase in variance can be achieved. This increase in efficiency applies to simple detection tasks by normal hearing subjects as well as to complex discrimination tasks by older subjects with hearing loss.

Adult↗