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At least 721 records · Page 40Linked to original sources

PubMed2004

Simulation-based estimation of stochastic process parameters in tumor growth.

Models are generally developed at the micro level. Data are generally gathered at the macro level. Obtaining the macromodel which is the natural consequence of the underlying micro model is generally not feasible. SIMEST gives a means whereby the micromodel is used to generate, for a given assumed set of parameters, simulated sets of macro data. These data are compared with the actual clinical macro data. The parameters are then adjusted to obtain concordance with the clinical data. In this manner, simulation gives us a means of parameter estimation without the necessity of generating the macro model.

Algorithms↗

PubMed1998

Maximum-likelihood estimation of Rician distribution parameters.

The problem of parameter estimation from Rician distributed data (e.g., magnitude magnetic resonance images) is addressed. The properties of conventional estimation methods are discussed and compared to maximum-likelihood (ML) estimation which is known to yield optimal results asymptotically. In contrast to previously proposed methods, ML estimation is demonstrated to be unbiased for high signal-to-noise ratio (SNR) and to yield physical relevant results for low SNR.

Computer Simulation↗

PubMed2006

Bias in estimation of transporter kinetic parameters from overexpression systems: Interplay of transporter expression level and substrate affinity.

The objective was to investigate the interplay between transporter expression levels and substrate affinity in controlling the influence of aqueous boundary layer (ABL) resistance on transporter kinetics in an over-expression system. Taurocholate flux was measured across human apical sodium-dependent bile acid transporter (hASBT)-Madin-Darby canine kidney monolayers on different occasions and kinetic parameters estimated with and without considering ABL. In error-free simulation/regression studies, flux values were generated across a range of J max, Kt, and substrate concentrations. Similar evaluation was performed for transport inhibition studies. Additionally, simulation/regression studies were performed, incorporating 15% random error to estimate the probability of successfully estimating Kt. Across different occasions, experimental J max and Kt estimates for taurocholate were strongly associated (p < 0.001; r2 = 0.82) when ABL was not considered. Simulation/regression results indicate that not considering ABL caused this association, such that Kt estimates were highly positively biased at high hASBT expression. In reanalyzing taurocholate flux data using the ABL-present model, Kt was relatively constant across occasions (approximately 5 microM) and not associated with J max (p = 0.24; r2 = 0.13). Simulations suggest that J max and Kt collectively determined ABL influence, which is most prominent under conditions of low monolayer resistance. Additionally, not considering ABL lead to negatively biased Ki estimates, especially at high J max. Error-inclusive simulation/regression studies indicated that the probability of successfully estimating Kt depended on the contribution of ABL resistance to flux; when flux became increasingly ABL-limited, probability of success decreased. Results indicate that ABL resistance can bias Kt and Ki estimates from overexpression systems, where the extent of bias is determined by transporter expression level and substrate affinity.

Animals↗

PubMed1989

A compartmental model to quantitate in vivo glucose transport in the human forearm.

Glucose transport is a critical step in the control of glucose disposal that, until presently, has not been quantitated in vivo in humans. We have employed the perfused forearm and euglycemic insulin-clamp techniques in combination with a dual-tracer injection to measure basal and insulin-mediated glucose transport in six normal subjects. L-[3H]glucose, which is not transported, was used to trace extracellular glucose kinetics; 3-O-[14C]-methyl-D-glucose, transportable but not metabolizable, was used to monitor glucose movement across the cell membrane. After bolus intra-arterial injection of the two tracers, plasma samples were obtained every 15-30 s for 10 min from a deep forearm vein to determine the washout curves. A linear compartmental model was developed that accounts for blood flow heterogeneity. It consists of three parallel, two-compartment chains merging into the sampling compartment to which cellular compartments are appended. A priori identifiability analysis was performed. The uniquely identifiable parameterization includes the transport rate constants of glucose into and out of the cell. The model was identified using nonlinear least-squares parameter estimation. Transport parameters are estimated with very good precision, and their reproducibility is satisfactory. The model also allows the estimation of the mean arteriovenous transit times of both the extracellular and the transported tracer. The compartmental model provides a novel approach to investigate glucose transport in vivo in humans.

3-O-Methylglucose↗

PubMed1992

Pharmacokinetics of isometronidazole. Basic model and estimation of the kinetic parameters from experimental data.

For the optimal timing of application of radiosensitizers in a course of radiotherapy it is important to know the sensitizer concentration at the time of irradiation. We have studied the pharmacokinetics of the hypoxic cell sensitizer isometronidazole in man and mouse and analyzed the data on the basis of an open two-compartment model after extravasal application. The parameter estimation is performed directly to avoid estimation biasing and data points from blood and tissue compartments are approximated simultaneously. The values obtained differ significantly from the estimations calculated by other authors for the same data.

Animals↗

PubMed2004

Estimating model parameters by chaos synchronization.

Using chaos synchronization and a proposed iterative method of parameter adaptation, we precisely estimate the model parameters of chaotic systems and synchronize two chaotic systems with originally mismatching model parameters. This parameter adaptation method can be applied to a spatiotemporal chaotic system with a one-way-coupled map lattice. As a biomedical application, this method is capable of estimating the asymmetric tension parameter of a vocal fold model.

Biophysical Phenomena↗

PubMed2005

A note on variance estimation in random effects meta-regression.

For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.

Analysis of Variance↗

PubMed1998

Modeling hemodynamic response for analysis of functional MRI time-series.

The standard Gaussian function is proposed for the hemodynamic modulation function (HDMF) of functional magnetic resonance imaging (fMRI) time-series. Unlike previously proposed parametric models, the Gaussian model accounts independently for the delay and dispersion of the hemodynamic responses and provides a more flexible and mathematically convenient model. A suboptimal noniterative scheme to estimate the hemodynamic parameters is presented. The ability of the Gaussian function to represent the HDMF of brain activation is compared with Poisson and Gamma models. The proposed model seems valid because the lag and dispersion values of hemodynamic responses rendered by the Gaussian model are in the ranges of their previously reported values in recent optical and fMR imaging studies. An extension of multiple regression analysis to incorporate the HDMF is presented. The detected activity patterns exhibit improvements with hemodynamic correction. The proposed model and efficient parameter estimation scheme facilitated the investigation of variability of hemodynamic parameters of human brain activation. The hemodynamic parameters estimated over different brain regions and across different stimuli showed significant differences. Measurement of hemodynamic parameters over the brain during sensory or cognitive stimulation may reveal vital information on physiological events accompanying neuronal activation and functional variability of the human brain, and should lead to the investigation of more accurate and complex models.

Brain↗

PubMed2005

A pragmatic approach to the design of population pharmacokinetic studies.

The publication of a seminal article on nonlinear mixed-effect modeling led to a revolution in pharmacokinetics (PKs) with the introduction of the population approach. Since then, interest in obtaining accurate and precise estimates of population PK parameters has led to work on population PK study design that extended previous work on optimal sampling designs for individual PK parameter estimation. The issues and developments in the design of population PK studies are reviewed as a prelude to investigating, via simulation, the performance of 2 approaches (population Fisher information matrix D-optimal design and informative block [profile] randomized [IBR] design) for designing population PK studies. The results of our simulation study indicate that the designs based on the 2 approaches yielded efficient parameter estimates. The designs based on the 2 approaches performed similarly, and in some cases designs based on the IBR approach were slightly better. The ease with which the IBR designs can be generated makes them preferable in drug development, where pragmatism and time are of great consideration. We, therefore, refer to the IBR designs as pragmatic designs. Pragmatic designs that achieve high efficiency in the estimation parameters should be used in the design of population PK studies, and simulation should be used to determine the efficiency of the designs.

Animals↗

PubMedDate not supplied

Use of magnitude estimation for estimating the parameters of dyspnea.

Magnitude estimation, a psychophysic method, allows examination of variables associated with the respiratory sensation of loaded breaths. The subject's perceptual sensitivity can be quantified by magnitude-scaling exponents which describe the rate of growth of the dyspnea sensation associated with increases in the magnitude of the stimulus. Measurement of the perception of dyspnea by magnitude estimation can complement other measurements of variables that contribute to this complex sensation. Use of magnitude estimation techniques can enhance the study of similar clinical problems such as pain and fatigue.

Dyspnea↗

PubMed2005

Optimal experiment design for cardinal values estimation: guidelines for data collection.

Optimal experiment design for parameter estimation (OED/PE) is an interesting technique for modelling practices when aiming for maximum parameter estimation accuracy. Nowadays, experimental designs for secondary modelling within the field of predictive microbiology are mostly arbitrary or based on factorial design. The latter type of design is common practice in response surface modelling approaches. A number of levels of the factor(s) under study are selected and all possible treatment combinations are performed. It is however not always clear which levels and treatment combinations are most relevant. An answer to this question can be obtained from optimal experiment design for-in this particular case-parameter estimation. This technique is based on the extremisation of a scalar function of the Fisher information matrix. The type of scalar function determines the final focus of the optimised design. In this paper, optimal experiment designs are computed for the cardinal temperature model with inflection point (CTMI) and the cardinal pH model (CPM). A model output sensitivity analysis (depicting the sensitivity of the model output to a small change in the model parameters) yields a first indication of relevant temperature or pH treatments. Performed designs are: D-optimal design aiming for a maximum global parameter estimation accuracy (by minimising the determinant of the Fisher information matrix), and E-optimal design improving the confidence in the most uncertain model parameter (by maximising the smallest eigenvalue of the Fisher information matrix). Although lowering the information content of a set of experiments, boundary values on the design region need to be imposed during optimisation to exclude unworkable experiments and partly account for incorrect nominal parameter values. Opposed to the frequently applied equidistant or arbitrary treatment placement, optimal design results show that typically four informative temperature or pH levels are selected and replicate experiments are to be performed at these points. Informative experiments are typically placed at points with an extreme model output sensitivity.

Bacteria↗

PubMed2001

Estimated genetic parameters for growth traits of German shepherd dog and Labrador retriever dog guides.

A desirable dog guide weighs 18 to 32 kg and measures 53 to 64 cm in height at the withers as an adult. Heritabilities and genetic correlations were estimated for birth weight, 42-d weight, mature weight, and mature height for 2,334 German shepherd dogs and 2,028 Labrador retrievers raised by the Seeing Eye, Inc., Morristown, NJ, from 1979 to 1997. Data included 5,006 observations for German shepherd dogs from 113 dams and 33 sires and 4,123 observations for Labrador retrievers from 89 dams and 29 sires. A mixed effects model was considered with sex and birth year as fixed effects. Random effects were animal, maternal, and litter incorporating all pedigree information available. A derivative-free REML method was used to estimate parameters. The maternal component was higher than the additive genetic component for birth weight. Heritability of mature weight was estimated as 0.57 +/- 0.07 for German shepherd dogs and 0.44 +/- 0.07 for Labrador retrievers. Mature height heritability was estimated as 0.35 +/- 0.08 for German shepherd dogs and 0.46 +/- 0.08 for Labrador retrievers. Selection for lighter dogs at maturity is predicted to decrease the average mature height, and selection for taller dogs at maturity is predicted to increase the average mature weight. The estimated genetic parameters will aid in the development of strategies to increase the probability of breeding dogs for optimum mature size.

Animals↗

PubMed1999

Likelihood methods for incomplete longitudinal binary responses with incomplete categorical covariates.

We consider longitudinal studies in which the outcome observed over time is binary and the covariates of interest are categorical. With no missing responses or covariates, one specifies a multinomial model for the responses given the covariates and uses maximum likelihood to estimate the parameters. Unfortunately, incomplete data in the responses and covariates are a common occurrence in longitudinal studies. Here we assume the missing data are missing at random (Rubin, 1976, Biometrika 63, 581-592). Since all of the missing data (responses and covariates) are categorical, a useful technique for obtaining maximum likelihood parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). In using the EM algorithm with missing responses and covariates, one specifies the joint distribution of the responses and covariates. Here we consider the parameters of the covariate distribution as a nuisance. In data sets where the percentage of missing data is high, the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest. We propose a conditional model for the covariate distribution that has several modeling advantages for the EM algorithm and provides a reduction in the number of nuisance parameters, thus providing more stable estimates in finite samples.

Affect↗

PubMed2005

Limitations of a TCP model incorporating population heterogeneity.

The variation between individuals in their dose-response characteristics complicates attempts to extract estimates of radiobiological parameters (e.g. alpha, beta, etc) from fits to clinical dose-response data. The use of 'population' dose-response models that explicitly account for this variability is necessary to avoid obtaining skewed parameter estimates. In this work, we evaluated an example of a 'population' tumour control probability (TCP) model in terms of its ability to provide reliable parameter estimates. This was accomplished by performing fits of this population model to 'pseudo' data sets, which were generated with Monte Carlo techniques and based on preset values for the various radiobiological parameters. The fitting exercises illustrated considerable correlations between the model parameters. Especially significant was the large correlation observed between the parameter mu=alpha/sigmaalpha used to characterize the level of population heterogeneity in radiosensitivity and the alpha/beta parameter typically used to describe the response to fractionation. The results imply that fits to clinical data may not be able to distinguish between tumours exhibiting a high degree of heterogeneity and a strong beta-mechanism and those containing little heterogeneity and having a weak beta-mechanism. One implication is that basing the design of optimal fractionation regimes on such fitting results may be error-prone. If in vitro assays are to be used to independently determine biologically reasonable ranges for parameter values, an accurate knowledge of the relationship between in vitro and in vivo dose-response characteristics is required.

Animals↗

PubMed1996

Comparison of some practical sampling strategies for population pharmacokinetic studies.

Using population analysis, sparsely sampled Phase 3 clinical data can be utilized to determine the pharmacokinetic characteristics of the target population. Data arising from such studies are likely to be constrained to certain sampling windows, i.e., the visiting hours at the study clinic. When the sampling window is narrow compared to the half-life of the drug, the advantage of taking more than one sample is not obvious. Study designs with one or two samples per visit have been compared with respect to (i) precision and bias of the population parameter estimates, (ii) the ability to identify the underlying pharmacokinetic model, and (iii) the estimation of individual parameter values. The first point was assessed using simulated data while the latter two were studied using a real data set. Results show: (i) Parameter estimates are more biased and imprecise when only one sample is taken compared to when two samples are obtained, this is true irrespective of the time span between the two samples. (ii) Ability to identify a more complex model is increased if two samples are taken. Specifically, the variability between occasions can be quantified. (iii) Two-sample designs are generally better with respect to prediction of individual parameter values. Even minor changes to commonly employed study designs, in this case the addition of one sample at each study occasion, can improve quality and quantity of the information obtained.

Computer Simulation↗

PubMed1982

Multiplication noise in the human visual system at threshold: 2. Probit estimation of parameters.

A mathematical technique is described that relates detection model parameters to stimulus magnitude and experimental probability of detection. The normalizing transform is used to make the response statistics approximately Gaussian. Conventional probit analysis is then applied. From measurements at M stimulus levels, a system of M equations is solved and estimates of M unknown parameters of the detection model are obtained. The technique is applied to a threshold vision model based on additive and multiplicative Poisson noise. Results are obtained for the parameter estimates for individual subjects, and for the standard deviation of the estimates, for various values of the stimulus energy and number of trials. A frequency-of-seeing experiment is performed using a point-source stimulus that randomly assumes 3 energy levels with 200 trials per level. With a central efficiency of 50%, the estimated ocular quantum efficiency for our four subjects lies between 12% and 23%, the average dark count at the retina lies between 8 and 36 counts, and the threshold count for our (low false-report rate) data lies between 11 and 32. The theoretical results reduce to those obtained by Barlow (J. Physiol. London 160, 155-168, 1962), in the absence of dark light and multiplication noise.

Humans↗

PubMed1998

Evaluation of model-based processing algorithms for averaged transmitral spectral Doppler images.

In an effort to characterize more fully diastolic function using Doppler echocardiography, we have previously developed an automated method of model-based image processing for spectral Doppler images of transmitral blood flow. In this method, maximum velocity envelopes (MVEs) extracted from individual Doppler images are aligned and averaged over several cardiac cycles. The averaged waveform is fit by the solution of a kinematic model of diastolic filling. The results are estimates of the model parameters. As expected, the mean and standard deviation of the model parameter estimates depend on many factors such as noise, the number of cardiac cycles averaged, beat-to-beat variation, waveform shape, observation time and the processing methods used, among others. A comprehensive evaluation of these effects has not been performed to date. A simulation was developed to evaluate the performance of three automated processing methods and to measure the influence of noise, beat-to-beat variation and observation time on the model parameter estimates. The simulation's design and a description and analysis of the three automated processing methods are presented. Of the three methods evaluated, using the inflection point in the acceleration portion of the velocity contour as the first data point to be fit was found to be the most robust method for processing averaged E-wave MVE waveforms. Using this method under nominal conditions, the average bias was measured to be < 3% for each of the model parameters. As expected, the biases and standard deviations of the estimates increased as a result of increased noise levels, increased beat-to-beat variation and decreased observation time. Another important finding was that the effects of noise, beat-to-beat variation and waveform observation time on the parameter estimates are dependent on the location in model parameter space.

Algorithms↗

PubMed1980

Confidence interval estimators for parameters associated with quantitative structure-activity relationships.

Use of the "jackknife" as a statistical tool for construction of both point and confidence interval estimators for parameters which are functions of regression coefficients and/or iteratively estimated parameters is described. Examples are presented demonstrating its utility in constructing estimators for log P0 and pi 0 using the parabolic and bilinear quantitative structure-activity relationship (QSAR) models relating nonlinear dependence of activity on hydrophobicity and for log [1/Ki(app)] assuming competitive enzyme inhibition.

Chemical Phenomena↗