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At least 127 records · Page 7Linked to original sources
PubMed2005

Dynamic HIV/AIDS parameter estimation with application to a vaccine readiness study in southern Africa.

This paper proposes a procedure of parameter estimation for all parameters of the three-dimensional HIV model. The least square based procedure uses standard optimization routines to allow parameter extraction for individual patients. It is shown how additional information from outside a measurement dataset can be included in the estimation routine to increase the reliability and accuracy of parameter estimates. A dataset from 44 patients of Southern Africa is analyzed to find the set point and the time until set point for these patients together with an estimate of the model parameters with confidence intervals for the cohort. The procedure is also applied to a long-term dataset of the HIV/AIDS progression to find possible variations in parameters.

Acquired Immunodeficiency Syndrome↗
PubMed2001

Nonlinear parameters estimation from sequential short time data series.

Procedures of nonlinear parameter estimation require large samples of data. In stationary physiological situations, usually short time series are available. The method of dynamics-dependent windowing and data aggregation procedure are proposed. This technique was tested on chaotic signal generated by Lorenz model and applied to investigate beat-to-beat control of the cardiovascular system in 10 healthy volunteers. Nonivasively recorded blood pressure, respiratory activity and blood oxygen saturation were digitized and saved for further off-line analysis. The experimental procedure consisted of 10 min control--C, 20 voluntary apneas 1 min each-A, interapnea 20 periods of 1 min spontaneous breathing--B, and 10 min free-breathing recovery--R. Respiration signal served as a reference for apnea and interapnea free-breathing identification period. Correlation dimension-CD, according to Grassberger and Procaccia, and recurrence plot strategy, according to Webber and Zbilut, were applied to check dynamical properties of the signals. Results of numerical experiment on Lorenz model, original and transformed by segmentation and aggregation, support our assumption of similarity of their dynamics. Error in CD and recurrence parameters estimation strongly depended on segment length and was about 5% for 600 to 1,200 data points. However, even for segments of 75 to 100 samples, it did not exceed 10% for all, but one, periodic testing signal. Segmentation and aggregation applied to interbeat interval (IBI) and total peripheral resistance (TPR) data showed that CD and recurrence variables estimated separately for apneic and interapneic period and those calculated for mixed (apneic and interapneic) intervals were different. Average CD and recurrence parameters of IBI and TPR for 10 subjects during apnea and interapnea intervals were significantly different than during control and recovery. The lowest CD (mean +/- S.D.) of 6.38 +/- 0.4, 5.62 +/- 0.2 and %recurrence 10.35 +/- 0.8, 6.62 +/- 0.6 (highest ratio 4.95 +/- 0.2, 5.13 +/- 0.3) were observed in apnea for IBI and TPR, respectively. Low values of the estimates computed for mixed periods may suggest the influence of slowly varying, quasiperiodic driving force due to experimental procedure regime. Signal dynamics-dependent windowing and data aggregation regardless of the sequence of data could be a practical solution for nonlinear analysis of very short repeatable time series.

Adult↗
PubMed2002

Parameter estimation in longitudinal studies with outcome-dependent follow-up.

In many observational studies, individuals are measured repeatedly over time, although not necessarily at a set of prespecified occasions. Instead, individuals may be measured at irregular intervals, with those having a history of poorer health outcomes being measured with somewhat greater frequency and regularity; i.e., those individuals with poorer health outcomes may have more frequent follow-up measurements and the intervals between their repeated measurements may be shorter. In this article, we consider estimation of regression parameters in models for longitudinal data where the follow-up times are not fixed by design but can depend on previous outcomes. In particular, we focus on general linear models for longitudinal data where the repeated measures are assumed to have a multivariate Gaussian distribution. We consider assumptions regarding the follow-up time process that result in the likelihood function separating into two components: one for the follow-up time process, the other for the outcome process. The practical implication of this separation is that the former process can be ignored when making likelihood-based inferences about the latter; i.e., maximum likelihood (ML) estimation of the regression parameters relating the mean of the longitudinal outcomes to covariates does not require that a model for the distribution of follow-up times be specified. As a result, standard statistical software, e.g., SAS PROC MIXED (Littell et al., 1996, SAS System for Mixed Models), can be used to analyze the data. However, we also demonstrate that misspecification of the model for the covariance among the repeated measures will, in general, result in regression parameter estimates that are biased. Furthermore, results of a simulation study indicate that the potential bias due to misspecification of the covariance can be quite considerable in this setting. Finally, we illustrate these results using data from a longitudinal observational study (Lipshultz et al., 1995, New England Journal of Medicine 332, 1738-1743) that explored the cardiotoxic effects of doxorubicin chemotherapy for the treatment of acute lymphoblastic leukemia in children.

Adolescent↗
PubMed1987

Reliability of parameter estimates from models applied to respiratory impedance data.

Many previous studies have fit lumped parameter models to respiratory input (Zin) and transfer (Ztr) impedance data. For frequency ranges higher than 4-32 Hz, a six-element model may be required in which an airway branch (with a resistance and inertance) is separated from a tissue branch (with a resistance, inertance, and compliance) by a shunt compliance. A sensitivity analysis is applied to predict the effects of frequency range on the accuracy of parameter estimates in this model obtained from Zin or Ztr data. Using a parameter set estimated from experimental data between 4 and 64 Hz in dogs, both Zin and Ztr were simulated from 4 to 200 Hz. Impedance sensitivity to each parameter was also calculated over this frequency range. The simulation predicted that for Zin a second resonance occurs near 80 Hz and that the impedance is considerably more sensitive to several of the parameters at frequencies surrounding this resonance than at any other frequencies. Also, unless data is obtained at very high frequencies (where the model is suspect), Zin data provides more accurate estimates than Ztr data. After adding random noise to the simulated Zin data, we attempted to extract the original parameters by using a nonlinear regression applied to three frequency ranges: 4-32, 4-64, and 4-110 Hz. Estimated parameters were substantially incorrect when using only 4- to 32-Hz or 4- to 64-Hz data, but nearly correct when fitting 4- to 110-Hz data. These results indicate that respiratory system parameters can be more accurately extracted from Zin than Ztr, and to make physiological inferences from parameter estimates based on Zin impedance data in dogs, the data must include frequencies surrounding the second resonance.

Airway Resistance↗
PubMed1991

Population parameter estimates for performance and reproductive traits in Polish Large White nucleus herds.

Performance test records from on-farm tests of young Polish Large White boars and reproductive records of Polish Large White sows from 94 nucleus farms during 1978 to 1987 were used to estimate population parameters for the measured traits. The number of boar performance records after editing was 114,347 from 3,932 sires, 21,543 dams, 44,493 litters and 1,075 herd-year-seasons. Reproductive performance records of sows involved 41,080 litters from 2,348 sires, 18,683 dams and 1,520 herd-year-seasons. Both data sets were analyzed by using restricted maximum-likelihood programs. The model used for the performance records included fixed herd-year-seasons, random sires, dams and error effects, and covariances for the year of birth of sire and year of birth of dam. The model used for the reproduction data set was the same as the performance data with parity as an additional fixed effect. Estimated heritabilities were .27, .29, .26, .07, .06, .06 for average daily gain standardized to 180 d (ADG), backfat thickness standardized to 110 kg BW (BF), days to 110 kg (DAYS), litter size at birth born alive (NBA), litter size at 21 d (N21) and litter weight at 21 d (W21), respectively. Estimated common environmental effects for the same traits were .09, .10, .09, .06, .07 and .08, respectively. Genetic correlations were .25 (ADG and BF), -.99 (ADG and DAYS), -.21 (BF and DAYS), .91 (NBA and N21), .68 (NBA and W21) and .80 (N21 and W21). The respective phenotypic correlations were .23, -.99, -.20, .88, .75, .86. These population parameters for Polish Large White pigs are similar to those for breeds in other countries.

Animals↗
PubMed2005

Genetic parameter estimates of meat quality traits in Duroc pigs selected for average daily gain, longissimus muscle area, backfat thickness, and intramuscular fat content.

Using a multitrait animal model BLUP, selection was conducted over seven generations for growth rate (ADG), real-time ultrasound LM area (LMA), backfat thickness (BF), and intramuscular fat content (IMF) to develop a new line of purebred Duroc pigs with enhanced meat production and meat quality. This selection experiment examined 543 slaughtered pigs (394 barrows and 153 gilts) from the first to the seventh generation for meat quality traits. Further, electric impedance and collagen content of loin meat were measured from the fourth to sixth generation. The present study was intended to estimate genetic parameters of the correlated traits of tenderness (TEND), meat color (pork color standard: PCS; lightness = L*), drip loss (DL), cooking loss (CL), pH (PH), electric impedance (IMP), and collagen (COL) of the LM, and the genetic trends of these traits. Respective heritability estimates for IMF, TEND, DL, CL, PCS, L*, PH, IMP, and COL were 0.39, 0.45, 0.14, 0.09, 0.18, 0.16, 0.07, 0.22, and 0.23. Genetic correlations of IMF with ADG and BF were low and positive, but low and negative with LMA. Tenderness was correlated negatively with ADG (-0.44) and BF (-0.59), but positively correlated with LMA (0.32). The genetic correlation between LMA and DL was positive and high (0.64). The genetic correlations of TEND with IMF and COL were low (-0.09 and 0.26, respectively), but a moderate genetic correlation (0.43) between COL and IMF was estimated, suggesting related increases of IMF and connective tissue. Genetic correlations among meat quality traits suggested that when IMF increases, the water holding capacity improves. Genetic trends of meat quality traits showed increased IMF and lighter meat color.

Adipose Tissue↗
PubMed1990

Real-time system for robust spectral parameter estimation in Doppler signal analysis.

In assessing the level of stenosis in extracranial Doppler analysis, spectral analysis has until now been used qualitatively, for the most part. Owing to the many variables affecting the measurements (mainly noise level and instrument setting made subjectively by the operator), the reliability of the inferences on the degree of stenosis is not clearly definable. Under such conditions the need arises for algorithms and systems that can estimate spectral parameters with a higher degree of accuracy, to verify whether reliable inferences can indeed by made or if this technique is only a qualitative one. In the paper a real-time spectral analysis system is described. The system relies on a new spectral estimation algorithm which gives estimates with good robustness with respect to noise. Moreover, a clear measurement procedure which eliminates the many subjective factors affecting the estimates has also been proposed and used. The system has been evaluated with simulated signals and in clinical trials and has shown better performance than the commonly used commercial analysers.

Adult↗
PubMed1989

Parameter estimation for nonlinear frequency dependent attenuation in soft tissue.

Most current attenuation estimation methods are based on an assumption that tissue attenuation is linearly frequency dependent. For tissues exhibiting nonlinear attenuation, such methods could produce biased results. This paper describes a new time-domain, amplitude method for estimating two parameters, beta and n, in nonlinear attenuation. In this method, echo signal of each A-line is first filtered in parallel by a bank of narrow-bandpass filters. Attenuation values at filter center frequencies can be calculated from the envelope peaks of each filtered narrowband signal, and beta and n are then estimated from these attenuation values. A quantitative study of the estimation uncertainty indicates that there is a significant increase in the variance of beta estimate when the attenuation model is changed from linear to nonlinear. The analysis yields insights into the tradeoff between estimation accuracy and estimation precision when tissue attenuation is only slightly deviated from linearity.

Connective Tissue↗
PubMed1984

Population pharmacokinetic data and parameter estimation based on their first two statistical moments.

A statistical model is set forth that can be taken as a very general description of most data sets that arise from population pharmacokinetic studies. A (nontraditional) method for estimating the parameters of the model--called the NONMEM method in previously published papers--is described. This method involves a linearization of the model. An investigation of the effect of this linearization is reported. This investigation is an empirical one, based on the simulation of data from special cases of the model and the application of a few different estimation methods to these data. From the limited evidence in this investigation it appears that the linearization per se does not significantly adversely affect the estimates.

Humans↗
PubMed2001

Genetic parameter estimates from joint evaluation of purebreds and crossbreds in swine using the crossbred model.

Records on lifetime daily gain and backfat from two purebred lines A (n = 6,022), B (n = 24,170), and their reciprocal crosses C (n = 6,135) were used to estimate genetic parameters using within-line and terminal-cross models. The models that were fitted included fixed (contemporary group and sex), random additive A and(or) random additive B, random dominance, and random litter effects. Model for purebreds included only one additive effect, whereas the model for crossbreds included two additive effects. End weight was included as a covariable for backfat. Heritability estimates for lifetime daily gain were 0.26, 0.28, and 0.23 with within-line models for lines A, B, and C, respectively, and 0.26, 0.30, and 0.27 with the crossbred model, respectively. Heritability estimates for backfat were 0.52, 0.35, and 0.29 with within-line models for lines A, B, and C, respectively, and 0.51, 0.38, and 0.29 with the crossbred model, respectively. The genetic correlations between purebreds and crossbreds (r(pc)) for lifetime daily gain were 0.99 (A-C) and 0.62 (B-C); for backfat the correlations were 0.32 (A-C) and 0.70 (B-C). The amount of dominance variance from the crossbred model expressed as a proportion of phenotypic variance for lifetime daily gain was 0.39, 0.16, and 0.29 for lines A, B, and C respectively. Dominance variance for backfat was estimated as 0. A joint evaluation of purebreds and crossbreds would be most efficient with the crossbred model. The dominance variation should be accounted for lifetime daily gain.

Adipose Tissue↗
PubMed1988

[[On the parameter estimation problems in the multiregional demographic growth model]].

"In this paper, we critically prove the formulas of Ledent-Rogers-Willekens.... Our procedure does not depend on multi-regional life table technique but on direct discretization of the continuous-time model. Here our main purpose is not to provide new results but to clear the conditions under which these methods for estimating the parameters can be applied. Finally, we give another method of calculation of parameters, which makes it possible to do a consistent disaggregated projection when we have already had an aggregated population projection." (SUMMARY IN ENG)

Demography↗
PubMed1989

Genetic parameter estimates for postweaning traits of beef cattle in a stressful environment.

Postweaning growth data, collected from a Hereford herd located in the Southwest, were used to estimate genetic parameters for weights and gains. The herd was maintained on unsupplemented range forage, and average weight losses from weaning to yearling age were 9% for bulls and 12% for heifers. Data were grouped into years with poor and good environments based on contemporary group means for gain from 8 to 12 mo. Postweaning growth data (12- and 20-mo weights, 8- to 12-mo gain and 12- to 20-mo gain) were analyzed by least squares methods with a model that included year of birth, sire within year of birth, age of dam and a covariate of age for 12- and 20-mo weights. Heritability estimates of 12- and 20-mo weights for bulls were .58 +/- .15 and .55 +/- .22 in good environments vs .32 +/- .11 and 1.09 +/- .15 in poor environments; for heifers these estimates were .19 +/- .08 and .35 +/- .12 in good environments vs .38 +/- .07 and .47 +/- .09 in poor environments. Heritability estimates of 8- to 12-mo and 12- to 20-mo gain for bulls were .32 +/- .14 and .51 +/- .24 in good environments vs .16 +/- .11 and .09 +/- .14 in poor environments; for heifers these estimates were .21 +/- .08 and .14 +/- .10 in good environments vs .10 +/- .06 and .44 +/- .10 in poor environments. Genetic correlations among the preweaning traits of birth and weaning weight and postweaning weight traits were positive and of a moderate to large magnitude, with the exception of birth and 12-mo weight in a poor environment (-.06 +/- .49). Genetic correlations between 8- to 12-mo gain and birth weight in poor environment and weaning weight in all environments were negative (range from -.06 +/- .33 to -.53 +/- .41). Genetic correlations among 12- and 20-mo weights were large and positive in all environments. Relationships among gains were more variable.

Age Factors↗
PubMed2005

A stochastic formulation of the gompertzian growth model for in vitro bactericidal kinetics: parameter estimation and extinction probability.

Time-kill curves have frequently been employed to study the antimicrobial effects of antibiotics. The relevance of pharmacodynamic modeling to these investigations has been emphasized in many studies of bactericidal kinetics. Stochastic models are needed that take into account the randomness of the mechanisms of both bacterial growth and bacteria-drug interactions. However, most of the models currently used to describe antibiotic activity against microorganisms are deterministic. In this paper we examine a stochastic differential equation representing a stochastic version of a pharmacodynamic model of bacterial growth undergoing random fluctuations, and derive its solution, mean value and covariance structure. An explicit likelihood function is obtained both when the process is observed continuously over a period of time and when data is sampled at time points, as is the custom in these experimental conditions. Some asymptotic properties of the maximum likelihood estimators for the model parameters are discussed. The model is applied to analyze in vitro time-kill data and to estimate model parameters; the probability of the bacterial population size dropping below some critical threshold is also evaluated. Finally, the relationship between bacterial extinction probability and the pharmacodynamic parameters estimated is discussed.

Anti-Bacterial Agents↗
PubMed2005

The utility of prior information and stratification for parameter estimation with two screening tests but no gold standard.

When a gold standard screening or diagnostic test is not routinely available, it is common to apply two different imperfect tests to subjects from a study population. There is a considerable literature on estimating relevant parameters from the resultant data. In the situation that test sensitivities and specificities are unknown, several inferential strategies have been proposed. One suggestion is to use rough knowledge about the unknown test characteristics as prior information in a Bayesian analysis. Another suggestion is to obtain the statistical advantage of an identified model by splitting the population into two strata with differing disease prevalences. There is some division of opinion in the epidemiological literature on the relative merits of these two approaches. This article aims to shed light on the issue, by applying some recently developed theory on the performance of Bayesian inference in non-identified statistical models.

Bayes Theorem↗
PubMed1994

Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis.

We present a mathematical model of the cytotoxic T lymphocyte response to the growth of an immunogenic tumor. The model exhibits a number of phenomena that are seen in vivo, including immunostimulation of tumor growth, "sneaking through" of the tumor, and formation of a tumor "dormant state". The model is used to describe the kinetics of growth and regression of the B-lymphoma BCL1 in the spleen of mice. By comparing the model with experimental data, numerical estimates of parameters describing processes that cannot be measured in vivo are derived. Local and global bifurcations are calculated for realistic values of the parameters. For a large set of parameters we predict that the course of tumor growth and its clinical manifestation have a recurrent profile with a 3- to 4-month cycle, similar to patterns seen in certain leukemias.

Animals↗
PubMed2006

Virtual parameter-estimation experiments in Bioprocess-Engineering education.

Cell growth kinetics and reactor concepts constitute essential knowledge for Bioprocess-Engineering students. Traditional learning of these concepts is supported by lectures, tutorials, and practicals: ICT offers opportunities for improvement. A virtual-experiment environment was developed that supports both model-related and experimenting-related learning objectives. Students have to design experiments to estimate model parameters: they choose initial conditions and 'measure' output variables. The results contain experimental error, which is an important constraint for experimental design. Students learn from these results and use the new knowledge to re-design their experiment. Within a couple of hours, students design and run many experiments that would take weeks in reality. Usage was evaluated in two courses with questionnaires and in the final exam. The faculties involved in the two courses are convinced that the experiment environment supports essential learning objectives well.

Biomedical Engineering↗
PubMed2005

Optimal blood sampling time windows for parameter estimation using a population approach: design of a phase II clinical trial.

The objective of this paper is to determine optimal blood sampling time windows for the estimation of pharmacokinetic (PK) parameters by a population approach within the clinical constraints. A population PK model was developed to describe a reference phase II PK dataset. Using this model and the parameter estimates, D-optimal sampling times were determined by optimising the determinant of the population Fisher information matrix (PFIM) using PFIM_ _M 1.2 and the modified Fedorov exchange algorithm. Optimal sampling time windows were then determined by allowing the D-optimal windows design to result in a specified level of efficiency when compared to the fixed-times D-optimal design. The best results were obtained when K(a) and IIV on K(a) were fixed. Windows were determined using this approach assuming 90% level of efficiency and uniform sample distribution. Four optimal sampling time windows were determined as follow: at trough between 22 h and new drug administration; between 2 and 4 h after dose for all patients; and for 1/3 of the patients only 2 sampling time windows between 4 and 10 h after dose, equal to [4 h-5 h 05] and [9 h 10-10 h]. This work permitted the determination of an optimal design, with suitable sampling time windows which was then evaluated by simulations. The sampling time windows will be used to define the sampling schedule in a prospective phase II study.

Administration, Oral↗
PubMed2005

Water quality parameter estimation in a distribution system under dynamic state.

Chlorine maintenance in distribution systems is an issue for water suppliers. The complex pipe geometry in distribution systems, the dynamic flow conditions experienced within them, and the varied nature of chlorine's reactivity make it difficult to predict chlorine levels throughout a water system. Computer-based mathematical models of water quality transport and fate within distribution systems offer a promising tool for predicting chlorine in a cost-effective manner. Nevertheless, the use of water quality models can only be effective and reliable when both hydraulics and the mechanisms of chlorine dissipation within the water system are properly defined. Bulk water decay can be measured experimentally. However, wall reaction rates are more complex to determine and must be deduced from field measurement by comparison with simulation results. The simulation-optimization model presented in this paper provides an effective tool to simplify the chlorine decay model calibration process that is often tedious. The optimization tool is based on the weighted-least-squares method solved by Gauss-Newton technique. Application of the model onto a real-life system shows that quantity, quality and location of measurement nodes play an important role in estimation of parameters.

Calibration↗