SEARCH · PubMed Health

Results for “parameter estimation”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77Linked to original sources

PubMed1995

Fitting nonlinear regression models with correlated errors to individual pharmacodynamic data using SAS software.

Nonlinear regression is widely used in pharmacokinetic and pharmacodynamic modeling by applying nonlinear ordinary least squares. Although the assumption of independent errors is frequently not fulfilled, this has received scant attention in the pharmacokinetic literature. As in linear regression, leaving correlation of errors out of account leads to an underestimation of the standard deviations of parameter estimates. On the other hand, the use of models that accommodate correlated errors requires more care and more computation. This paper describes a method to fit log-normal functions to individual response curves containing correlated errors by means of statistical software for time series. A sample computer program is given in which the SAS/ETS procedure MODEL is used. In particular, the problem of finding appropriate starting values for nonlinear iterative algorithms is considered. A linear weighted least squares approach for initial parameter estimation is developed. The adequacy of the method is investigated by means of Monte Carlo simulations. Furthermore, the statistical properties of nonlinear least squares with and without accommodating correlated errors are compared. Time action profiles of a long-acting insulin preparation injected subcutaneously in humans are analyzed to illustrate the usefulness of the method proposed.

Dose-Response Relationship, Drug↗

PubMed2003

Fixing the nonconvergence bug in logistic regression with SPLUS and SAS.

When analyzing clinical data with binary outcomes, the parameter estimates and consequently the odds ratio estimates of a logistic model sometimes do not converge to finite values. This phenomenon is due to special conditions in a data set and known as 'separation'. Statistical software packages for logistic regression using the maximum likelihood method cannot appropriately deal with this problem. A new procedure to solve the problem has been proposed by Heinze and Schemper (Stat. Med. 21 (2002) pp. 2409-3419). It has been shown that unlike the standard maximum likelihood method, this method always leads to finite parameter estimates. We developed a SAS macro and an SPLUS library to make this method available from within one of these widely used statistical software packages. Our programs are also capable of performing interval estimation based on profile penalized log likelihood (PPL) and of plotting the PPL function as was suggested by Heinze and Schemper (Stat. Med. 21 (2002) pp. 2409-3419).

Likelihood Functions↗

PubMed1998

Kinetic mechanism of glutathione conjugation to leukotriene A4 by leukotriene C4 synthase.

The kinetic mechanism for human leukotriene (LT) C4 synthase, a membrane-bound glutathione S-transferase, which catalyzes the conjugation of glutathione (GSH) to 5,6-oxido-7,9,11, 14-eicosatetraenoic acid (LTA4), to form 5(S)-hydroxy-6(R)-S-glutathionyl-7,9,trans-11, 14-cis-eicosatetraenoic acid (LTC4) was investigated by initial rate kinetic studies in which concentrations of both substrates and the reversible dead-end inhibitor, 2-[2-[1-(4-chlorobenzyl)-4-methyl-6-[(5-phenylpyridin-2-yl)- methoxy]- 4,5-dihydro-1H-thiopyrano[2,3,4-c,d]indol-2-yl]ethoxy]butanoic acid (L-699,333) were varied. Analysis of the initial velocities of LTC4 formation in the absence of the inhibitor using non-linear regression fits of various models to the data favoured a random, rapid equilibrium mechanism, with strong substrate inhibition by LTA4, over both a compulsory ordered mechanism and a ping-pong mechanism. The estimated parameters were calculated to be Vmax = 14 +/- 4 microM/min, KLTA4 = 40 +/- 18 microM, KGSH = 0.4 +/- 0.2 mM, and a KiLTA4 = 2.3 +/- 1.7 microM for the rapid equilibrium random model. Inhibition of enzymatic activity by L-699,333 was found to be reversible as assessed by the ability of the enzyme to restore its activity by 95% upon dilution. L-699,333 was found to be a competitive inhibitor against GSH and non-competitive against LTA4. Non-linear least squares regression analysis yielded estimated parameters of Km = 0.7 +/- 0.1 mM, Vmax = 2.5 +/- 0.1 microM/min, and Ki = 0.7 +/- 0.1 microM for GSH at a fixed LTA4 concentration of 20 microM, and Km = 45 +/- 3 microM, Vmax = 4.9 +/- 0.2 microM/min, and a Ki = 5.8+/-0.4 microM for LTA4 at a fixed GSH concentration of 2 mM. The rate equation for the random equilibrium mechanism accommodates the inhibition patterns observed for L-699,333 against both substrates as revealed by kinetic fits of the inhibition data to the overall rate equation.

Enzyme Inhibitors↗

PubMed2006

Variability of the BOLD response over time: an examination of within-session differences.

Model-based analysis methods for fMRI data assume a priori knowledge of the time course of the hemodynamic response (HR) in reaction to experimental stimuli or events. This knowledge is incorporated into the hemodynamic response function (HRF), which is a common model of the HR. Although it is already known that the HR varies across individuals and brain regions, few studies have investigated how variations within one session affect the results of statistical analysis using the general linear model (GLM). In this study, we formally tested for a possible variation of the BOLD response during prolonged functional measurement (120 min). To provoke performance of simple visual, motor, and cognitive tasks, we opted for a combination of a variant of the Stroop task and rotating L's. In selected regions of interest, time courses were extracted and compared with regard to mean and maximum amplitudes throughout the time of functional measurement. Additionally, parameter estimates derived from the GLM were tested for differences over time. Although differences between conditions were found to be significant, results did not show significant variance due to a within-factor time. Similarly, a temporal change in the relation between conditions, in terms of an interaction between the within-factor time and the within-factor condition, was not detectable by a repeated measures ANOVA. Similar results were obtained for analysis of mean and maximum amplitudes as well as for the analyses of parameter estimates.

Adult↗

PubMed2006

Fingerprint matching based on global comprehensive similarity.

This paper introduces a novel algorithm based on global comprehensive similarity with three steps. To describe the Euclidean space-based relative features among minutiae, we first build a minutia-simplex that contains a pair of minutiae as well as their associated textures, with its transformation-variant and invariant relative features employed for the comprehensive similarity measurement and parameter estimation, respectively. By the second step, we use the ridge-based nearest neighborhood among minutiae to represent the ridge-based relative features among minutiae. With these ridge-based relative features, minutiae are grouped according to their affinity with a ridge. The Euclidean space-based and ridge-based relative features among minutiae reinforce each other in the representation of a fingerprint. Finally, we model the relationship between transformation and the comprehensive similarity between two fingerprints in terms of histogram for initial parameter estimation. Through these steps, our experiment shows that the method mentioned above is both effective and suitable for limited memory AFIS owing to its less than 1k byte template size.

Algorithms↗

PubMed2005

Model comparison for genetic evaluation of milk yield in crossbred Holsteins in the tropics.

The objective of this study was to compare models for appropriate genetic parameter estimation for milk yield (305-day) in crossbred Holsteins in the tropics, where only records from crossbred cows were available. Eleven models with different effects of contemporary group (CG) at calving (herd-year-season or herd-year-month as fixed, and herd-year-month as random), age at calving (as linear or quadratic covariates, age-class, and age-class x lactation), and dominance were considered. On-farm records from small herds (n < 50) were included or excluded to validate the parameter estimates. Average Information Restricted Maximum Likelihood (AIREML) and Best Linear Unbiased Prediction (BLUP) were used to estimate variance components and breeding values. R-square (R2) and standard error of heritability (h2) were used to determine the appropriate model. The estimates of heritability from most models ranged from 0.18 to 0.22. CG formation of herd-year-month as a random effect slightly lowered the additive genetic variance but considerably decreased the permanent environmental variance. The model with age-class x lactation gave better R2 than other age adjustments. The models including records from smallholders gave similar estimates of heritability and a lower standard error than the models excluding them. The estimate of dominance variance as a proportion of total variance was close to zero. The low ratio of dominance to additive genetic variance suggested that the inclusion of dominance effects in the model was unjustified. In conclusion, the model including the effects of herd-year-month, age-class x lactation, as well as additive genetic, permanent environmental and residual effects, was the most appropriate for genetic evaluation in crossbred Holsteins, where records from smallholders could be included.

Age Factors↗

PubMed1994

Logistic regression analysis of twin data: estimation of parameters of the multifactorial liability-threshold model.

We extend the DeFries-Fulker regression model for the analysis of quantitative twin data to cover binary traits and genetic dominance. In the proposed logistic regression model, the cotwin's trait status, C, is the response variable, while the proband's trait status, P, is a predictor variable coded as k (affected) and 0 (unaffected). Additive genetic effects are modeled by the predictor variable PR, which equals P for monozygotic (MZ) and P/2 for dizygotic (DZ) twins; and dominant genetic effects, by PD, which equals P for MZ and P/4 for DZ twins. By setting an appropriate scale for P (i.e., the value of k), the regression coefficients of P, PR, and PD are estimates of the proportion of variance in liability due to common family environment, additive genetic effects, and dominant genetic effects, respectively, for a multifactorial liability-threshold model. This model was applied to data on lifetime depression from the Virginia Twin Registry and produced results similar to those from structural equation modeling.

Adult↗

PubMed2002

A predictive model that evaluates the effect of growth conditions on the thermal resistance of Listeria monocytogenes.

A predictive model for Listeria monocytogenes was developed using cells grown in different pH and milkfat levels before subsequent thermal inactivation in identical pH and milkfat conditions. Inactivation of the cells used combinations of temperature (55, 60, 65 degrees C), pH (5.0, 6.0, 7.0), and milkfat (0%, 2.5%, 5.0%) in a complete 3 x 3 x 3 factorial design with each test done in triplicate. A modified Gompertz equation was used to model nonlinear survival curves with the following three parameter estimates: A for the shouldering region, B for the maximum death rate, and C for the tailing region. All treatment sets were analyzed together in a regression model using the modified Gompertz equation. There was good confidence in the overall model when it was used to predict values for the entire data set. The correlation of determination, R2, between the observed log surviving fraction (LSF) of cells from each of the conditions studied in the experiment, for the overall model was 0.811. For the A and B parameter estimates, temperature or milkfat alone, and the interaction of temperature and milkfat significantly (p < 0.05) affected the shouldering region and maximum death rate of a survival curve, respectively. These results were compared to a previously published predictive model, generated for cells grown under optimum conditions (pH 7.0, 0% milkfat), where pH was the only significant (p < 0.05) factor affecting the shoulder region. These results suggested that the conditions of the growth environment had an important impact on survival curve shape and the estimates of the predictive model. Specifically, there were more factor interactions involving temperature and milkfat level. These growth factors affected the shoulder region and maximum rate of death of the survival curve when cells were grown in identical medium conditions to which they were heated. Differences related to shouldering and inactivation rates for cells grown in different conditions may have important and practical importance for estimating inactivation of L. monocytogenes. This study provides some evidence on the importance of growing conditions when evaluating microbial heat resistance.

Animals↗

PubMed2006

Quantitative trait linkage analysis using Gaussian copulas.

Mapping and identifying variants that influence quantitative traits is an important problem for genetic studies. Traditional QTL mapping relies on a variance-components (VC) approach with the key assumption that the trait values in a family follow a multivariate normal distribution. Violation of this assumption can lead to inflated type I error, reduced power, and biased parameter estimates. To accommodate nonnormally distributed data, we developed and implemented a modified VC method, which we call the "copula VC method," that directly models the nonnormal distribution using Gaussian copulas. The copula VC method allows the analysis of continuous, discrete, and censored trait data, and the standard VC method is a special case when the data are distributed as multivariate normal. Through the use of link functions, the copula VC method can easily incorporate covariates. We use computer simulations to show that the proposed method yields unbiased parameter estimates, correct type I error rates, and improved power for testing linkage with a variety of nonnormal traits as compared with the standard VC and the regression-based methods.

Analysis of Variance↗

PubMed1997

Post-hoc Rasch analysis of optimal categorization of an ordered-response scale.

The purpose of this study was to determine the optimal categorization of a self-efficacy ordered-response scale using the Rasch analysis and compare the performance of the Rasch statistics and parameter estimates with conventional statistics. A 50-item scale to measure psychomotor self-efficacy was administered to a total of 2,022 children, including 1,009 boys and 1,013 girls. The data analysis started by collapsing the original five adjacent categories into two, three, and four categories, and a total of 14 data sets were derived. Each of these data sets, including the original one, was analyzed using the Rasch rating scale model, and a set of Rasch model-data fit, category, and separation statistics and parameter estimates, as well as three conventional statistics, were computed and compared. It was found that, instead of the five-category construct designed, the best order of category meanings of the scale in respondents' perceptions was a three-category construct. The Rasch threshold estimates were sensitive indexes in determining the order of the categorization, and that item separation statistics were useful in determining the optimal categorization after its order was confirmed. The commonly used coefficient alpha was found not helpful at all in determining the optimal categorization. The Rasch analysis was demonstrated to be a useful post-hoc analytic approach in determining the optimal categorization of an ordered-response scale.

Adolescent↗

PubMed2003

Population pharmacokinetics and metabolism of midazolam in pediatric intensive care patients.

OBJECTIVE: To determine the pharmacokinetics and metabolism of midazolam in pediatric intensive care patients. DESIGN: Prospective population pharmacokinetic study. SETTING: Pediatric intensive care unit. PATIENTS: Twenty-one pediatric intensive care patients aged between 2 days and 17 yrs. INTERVENTIONS: The pharmacokinetics of midazolam and metabolites were determined during and after a continuous infusion of midazolam (0.05-0.4 mg/kg/hr) for 3.8 hrs to 25 days administered for conscious sedation. MEASUREMENTS AND MAIN RESULTS: Blood samples were taken at different times during and after midazolam infusion for determination of midazolam, 1-OH-midazolam, and 1-OH-midazolam-glucuronide concentrations via high-performance liquid chromatography-ultraviolet detection. A population analysis was conducted via a two-compartment pharmacokinetic model by the NPEM program. The final population model was used to generate individual Bayesian posterior pharmacokinetic parameter estimates. Total body clearance, apparent volume distribution in terminal phase, and plasma elimination half-life were (mean +/- sd, n = 18): 5.0 +/- 3.9 mL/kg/min, 1.7 +/- 1.1 L/kg, and 5.5 +/- 3.5 hrs, respectively. The mean 1-OH-midazolam/midazolam ratio and (1-OH-midazolam + 1-OH-midazolam-glucuronide)/midazolam ratio were 0.14 +/- 0.21 and 1.4 +/- 1.1, respectively. Data from three patients with renal failure, hepatic failure, and concomitant erythromycin-fentanyl therapy were excluded from the final pharmacokinetic analysis. CONCLUSIONS: We describe population and individual midazolam pharmacokinetic parameter estimates in pediatric intensive care patients by using a population modeling approach. Lower midazolam elimination was observed in comparison to other studies in pediatric intensive care patients, probably as a result of differences in study design and patient differences such as age and disease state. Covariates such as renal failure, hepatic failure, and concomitant administration of CYP3A inhibitors are important predictors of altered midazolam and metabolite pharmacokinetics in pediatric intensive care patients. The derived population model can be useful for future dose optimization and Bayesian individualization.

Adolescent↗

PubMed1998

Reproducibility and comparability of insulin sensitivity indices measured by stable-label intravenous glucose tolerance test.

We have investigated the reproducibility of (1) insulin sensitivity (S*I) and glucose effectiveness (S*G) as measured by the stable-label (one compartment) minimal model, and (2) insulin sensitivity (S*Ib), plasma clearance rate (PCR), basal hepatic output (HGOb), and total hepatic glucose output (HGO0-240) as measured by the novel stable-label two compartment model of glucose disappearance during labelled intravenous glucose tolerance test (IVGTT) using 6,6-(2)H-glucose. Ten normal male subjects were studied on two occasions one week apart. Both models provided estimates of all indices with acceptable precision (CV of parameter estimates < or =50%). The within subject CVs of S*I and S*Ib were comparable (17% vs 19%) as were the within subject CVs of S*G and PCR (13% vs 16%). A highly significant linear relationship was observed between S*Ib and S*I (0.303 +/- 0.046 ml kg(-1) min(-1) per mU l(-1) vs 13.04 +/- 1.89 10(-4) min(-1) per mU l(-1), y = 0.0037 x + 0.0002, r = 0.90, p < 0.001; mean +/- SE), but not between PCR and S*G (1.98 +/- 0.15 ml kg(-1) min(-1) vs 0.0089 +/- 0.0005 min(-1), rs = 0.34, NS). The two compartment model provided a plausible time-profile of hepatic glucose output during IVGTT, reproducible estimates of HGOb (1.96 +/- 0.18 mg kg(-1) min(-1), 15%; mean +/- SE, within subject CV), and a highly reproducible HGO0-240 (7%; within subject CV). We conclude that the stable-label (one compartment) minimal model and the stable-label two compartment model provide reproducible estimates of parameters of glucose kinetics in normal subjects. Insulin sensitivity indices estimated by the two models are strongly linearly related.

Adult↗

PubMed2004

Asymptotic biases of the unrotated/rotated solutions in principal component analysis.

Asymptotic biases of the parameter estimates in principal component analysis with substantial misspecification are derived. The solutions for unstandardized and standardized observed variables are considered with and without orthogonal and oblique rotations. The distribution of observed variables can be non-normal as long as the finite fourth-order moments of the observed variables exist. When multivariate normality holds for the observed variables, substantial reduction of the amount of computation can be achieved. Numerical examples with simulations are given, with some discussion on the tendency of the biases to reduce the absolute values of parameter estimates.

Bias↗

PubMed2002

SAS and SPLUS programs to perform Cox regression without convergence problems.

When analyzing survival data, the parameter estimates and consequently the relative risk estimates of a Cox model sometimes do not converge to finite values. This phenomenon is due to special conditions in a data set and is known as 'monotone likelihood'. Statistical software packages for Cox regression using the maximum likelihood method cannot appropriately deal with this problem. A new procedure to solve the problem has been proposed by G. Heinze, M. Schemper, A solution to the problem of monotone likelihood in Cox regression, Biometrics 57 (2001). It has been shown that unlike the standard maximum likelihood method, this method always leads to finite parameter estimates. We developed a SAS macro and an SPLUS library to make this method available from within one of these widely used statistical software packages. Our programs are also capable of performing interval estimation based on profile penalized log likelihood (PPL) and of plotting the PPL function as was suggested by G. Heinze, M. Schemper, A solution to the problem of monotone likelihood in Cox regression, Biometrics 57 (2001).

Proportional Hazards Models↗

PubMed2000

Estimating recombinational parameters in Streptococcus pneumoniae from multilocus sequence typing data.

Multilocus sequence typing (MLST) is a highly discriminatory molecular typing method that defines isolates of bacterial pathogens using the sequences of approximately 450-bp internal fragments of seven housekeeping genes. This technique has been applied to 575 isolates of Streptococcus pneumoniae and identifies a number of discrete clonal complexes. These clonal complexes are typically represented by a single group of isolates sharing identical alleles at all seven loci, plus single-locus variants that differ from this group at only one out of the seven loci. As MLST is highly discriminatory, the members of each clonal complex can be assumed to have a recent common ancestor, and the molecular events that give rise to the single-locus variants can be used to estimate the relative contributions of recombination and mutation to clonal divergence. By comparing the sequences of the variant alleles within each clonal complex with the allele typically found within that clonal complex, we estimate that recombination has generated new alleles at a frequency approximately 10-fold higher than mutation, and that a single nucleotide site is approximately 50 times more likely to change through recombination than mutation. We also demonstrate how to estimate the average length of recombinational replacements from MLST data.

Alleles↗

PubMed2004

Use of the covariance matrix in directly fitting kinetic parameters: application to GABAA receptors.

A new method of analysis is described that begins to explore the relationship between the phases of ion channel desensitization and the underlying states of the channel. The method, referred to as covariance fitting (CVF), couples Q-matrix calculations with a maximum likelihood algorithm to fit macroscopic desensitization data directly to kinetic models. Unlike conventional sum-of-squares minimization, CVF fits both the magnitude of the recorded current and the strength of the correlations between different time points. When applied to simulated data generated using various kinetic models with up to 11 free parameters, CVF leads to reasonable parameter estimates. Coupled with the likelihood ratio test, it accurately discriminates between models with different numbers of states, discriminates between most models with the same number but a different arrangement of states, and extracts meaningful information on the relationship between the desensitized states and the phases of macroscopic desensitization. When applied to GABA(A) receptor traces (outside out patches, alpha 1 beta 2 gamma 2S, 1 mM GABA, >2.5 s), a model with two open states and three desensitized states is favored. When applied to simulated data generated using a consensus model, CVF leads to reasonable parameter estimates and accurately discriminates between this and other models.

Algorithms↗

PubMed2000

Estimation of sojourn time in chronic disease screening without data on interval cases.

Estimation of the sojourn time on the preclinical detectable period in disease screening or transition rates for the natural history of chronic disease usually rely on interval cases (diagnosed between screens). However, to ascertain such cases might be difficult in developing countries due to incomplete registration systems and difficulties in follow-up. To overcome this problem, we propose three Markov models to estimate parameters without using interval cases. A three-state Markov model, a five-state Markov model related to regional lymph node spread, and a five-state Markov model pertaining to tumor size are applied to data on breast cancer screening in female relatives of breast cancer cases in Taiwan. Results based on a three-state Markov model give mean sojourn time (MST) 1.90 (95% CI: 1.18-4.86) years for this high-risk group. Validation of these models on the basis of data on breast cancer screening in the age groups 50-59 and 60-69 years from the Swedish Two-County Trial shows the estimates from a three-state Markov model that does not use interval cases are very close to those from previous Markov models taking interval cancers into account. For the five-state Markov model, a reparameterized procedure using auxiliary information on clinically detected cancers is performed to estimate relevant parameters. A good fit of internal and external validation demonstrates the feasibility of using these models to estimate parameters that have previously required interval cancers. This method can be applied to other screening data in which there are no data on interval cases.

Aged↗

PubMed1993

Determination of cell dose-survival relationships from endpoint dilution assays.

Methods for fitting radiation survival curves to data obtained from endpoint-dilution assays are described. It is shown that for functional forms such as the linear-quadratic model the problem can be recast as a generalized linear model (GLM) and the data fitted using standard software. For functional forms which are not capable of being linearized, such as the multitarget model, the direct maximum likelihood (DML) techniques of Thames et al. (1986) can be used. Both these techniques produce exact maximum likelihood parameter estimates. Compared with the weighted least-squares (WLS) approach traditionally employed, these approaches avoid the need to approximate the binomial distribution of the number of negative wells by a normal distribution, and avoid the biases introduced by the need for arbitrary treatment of data points with 0 or 100% negative wells. The results of fittings using the novel GLM and DML approaches are compared with those obtained using the WLS method on a large series of datasets. For most datasets the WLS method performs well, compared with the exact method, but in a small number of cases the WLS predicted parameter estimates can be in error by as much as their estimated standard errors. A method for the use of a concurrent control to correct for interexperimental variation is outlined. The methods have been implemented in a Fortran computer program using the NAG subroutine library.

Cell Survival↗