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PubMed2001

An evaluation of the accuracy of shallow water matched field inversion results.

In this article the accuracy of geo-acoustic and geometric parameter estimates obtained through matched field inversion (MFI) was assessed. Multi-frequency MFI was applied to multi-tone data (200-600 Hz) received at a 2-km source/receiver range. The acoustic source was fixed and the signals were received at a vertical array. Simultaneously with the acoustic transmissions, a CTD (conductivity, temperature and depth)-chain was towed along the acoustic track. A genetic algorithm was used for the global optimization, whereas a normal mode model was applied for the forward acoustic calculations. Acoustic data received at consecutive times were inverted and the stability of the inverted parameters was determined. Also, the parameter estimates were compared with independent measurements, such as multi-channel seismic surveys (for geo-acoustic parameters). The obtained uncertainty in the inversion results was assumed to have two distinct origins. The first origin is the inversion method itself, since each optimization will come up with some solution close to the exact optimum. Parameter coupling and the fact that some parameters hardly influence the acoustic propagation further contribute to this uncertainty. The second is due to oceanographic variability. Both contributions were evaluated through simulation. The contribution of oceanographic variability was evaluated through synthetic inversions that account for the actual sound speed variations as measured by the towed CTD-chain.

Journal Article↗

PubMed1999

A note on issues in meta-analysis for behavioral genetic studies using categorical phenotypes.

Meta-analysis of behavioral genetic studies would provide (i) tighter confidence intervals around parameter estimates, (ii) clarification of apparently discrepant study findings, and (iii) a mechanism for analyzing systematic causes of between-study differences. We examined some key issues that arise in the meta-analysis of categorical phenotypes. Data were simulated under a multifactorial threshold model that assumed an underlying normal liability distribution, and summary statistics (probandwise concordance rate, recurrence risk ratio, odds ratio, kappa) compared for given values of the liability correlation between relatives and given population prevalence. Although the odds ratio and kappa statistic performed well at moderate to high values of the population prevalence (15-50%), at low values all of these statistics were sensitive to overall prevalence. In cases where the assumption of a multifactorial threshold model is reasonable, direct estimation of genetic and environmental parameters from the summary statistics from all studies appears to be a preferable strategy. For cases where data from non-randomly ascertained samples are used, the impact of misspecification of the model for ascertainment was examined. For some parameter values, such misspecifications led to quite serious biases to estimates of genetic and environmental parameters. These biases varied in complex ways as a function of research design and of the true causes of variation in the population, so that the same misspecification could lead to an overestimate of the importance of genetic influences in twin data but an underestimation in adoption data or to an overestimate of the importance of genetic effects from twin data if shared environmental as well as genetic influences were simulated but an underestimate of genetic effects if shared environmental effects were assumed unimportant. These complexities emphasize the importance of being sensitive to the effects of misspecifying ascertainment corrections in any meta-analysis of behavioral genetic data.

Confidence Intervals↗

PubMed2002

A pharmacokinetic model for analysis of drug disposition profiles undergoing enterohepatic circulation.

A new and simple pharmacokinetic model that can explain enterohepatic circulation profiles for both single and repeated dosing was developed, and its applicability and usefulness were assessed by an actual published data set and simulation study. The model is basically a conventional compartment model, and the transfer rate from the bile compartment to the central compartment is assumed to change periodically, with the sine function being used to describe this periodical change. Using this model, the effect of the parameter values on plasma time-course profiles was examined by simulation, and the applicability of the model was tested by curve fitting to obtain the parameter estimates using an actual published data set. These studies confirmed that our model can simulate the periodical increase of the concentration due to re-absorption. By averaging the sine function in the transfer rate from the bile compartment to the central compartment, a smoothed time-course profile in the elimination phase that is independent of the enterohepatic cycle can be obtained. Also, the apparent half-life in the elimination phase can be estimated, whith is useful especially for evaluating drug accumulation during repeated dosing. It was suggested that the present model can be used to evaluate the drug disposition profile with enterohepatic circulation. The effects of sampling points and sampling time on parameter estimation are also discussed.

Absorption↗

PubMed1977

Pharmacokinetics of orally administered hydroflumethiazide in man.

Hydroflumethiazide plasma and urine levels were measured fluorometrically following the oral administration of 100-mg doses (2 X 50 mg tablets) to each of 12 healthy, adult male subjects. The plasma level data as a function of time were fit to a one-compartment open model using a nonlinear regression digital computer program. The means of the individual apparent absorption and disposition rate apparent absorption and disposition rate constants were 0.770+/-0.46 hr(-1) (t1/2=0.9 hr) and 0.358+/-0.17 hr(-1) (t1/2=1.94 hr), respectively, with a 0.49+/-0.26 hr lag time in absorption. The same parameter estimates obtained by fitting the mean plasma concentration-time curve were 0.461+/-0.15 hr(-1), 0.357+/-0.13 hr(-1), and 0.39+/-0.09 hr for the apparent absorption and disposition rate constants and the lag time, respectively. The closeness of the parameter estimates and the lack of intravenous data preclude unequivocal assignment of the rate constants to the absorption or disposition processes. Nevertheless, the disposition of hydroflumethiazide is clearly a rapid process. The means of the individual peak plasma levels and times to peak were 0.301+/-0.94 microgram/ml and 2.12+/-0.59 hours, respectively. The mean area under the plasma level-time curve was 1.95+/-0.78 microgram-hr/ml and 46.7+/-20.0% of the administered dose was recovered in the 24-hour urine. The mean renal clearance of hydroflumethiazide was 453.4+/-256.6 ml/min.

Administration, Oral↗

PubMed1999

A novel parameter to estimate the minimum number of bound ligands needed to activate an ion channel.

We consider the Hill coefficient obtained from concentration-response curves and we stress the differences between the use of binding and the use of current for the response. We first show that in order to estimate the cooperativity of binding sites one must use the coefficient obtained from the binding curves and not that obtained from the current curves. For what concerns the estimation of the minimum number of bound ligands needed to activate the channel, while the coefficient obtained from the binding is simply not applicable, we show that the coefficient obtained from the current is not appropriate. We finally introduce a novel parameter that predicts the number of ligand molecules required to open the channel from current measurements at very low ligand concentration. The above considerations are exemplified by a few theoretical models.

Animals↗

PubMed1999

Robustness of the latent variable model for correlated binary data.

The marginal regression model offers a useful alternative to conditional approaches to analyzing binary data (Liang, Zeger, and Qaqish, 1992, Journal of the Royal Statistical Society, Series B 54, 3-40). Instead of modelling the binary data directly as do Liang and Zeger (1986, Biometrika 73, 13-22), the parametric marginal regression model developed by Qu et al. (1992, Biometrics 48, 1095-1102) assumes that there is an underlying multivariate normal vector that gives rise to the observed correlated binary outcomes. Although this parametric approach provides a flexible way to model different within-cluster correlation structures and does not restrict the parameter space, it is of interest to know how robust the parameter estimates are with respect to choices of the latent distribution. We first extend the latent modelling to include multivariate t-distributed latent vectors and assess the robustness in this class of distributions. Then we show through a simulation that the parameter estimates are robust with respect to the latent distribution even if latent distribution is skewed. In addtion to this empirical evidence for robustness, we show through the iterative algorithm that the robustness of the regression coefficents with respect to misspecifications of covariance structure in Liang and Zeger's model in fact indicates robustness with respect to underlying distributional assumptions of the latent vector in the latent variable model.

Algorithms↗

PubMed2000

The dose-effect relationship for morphine and vomiting after day-stay tonsillectomy in children.

A dose-response curve for intravenous morphine and vomiting was investigated in children having day-stay tonsillectomy. A retrospective chart review was performed for the 164 children fulfilling the inclusion criteria. Morphine (mean 0.09 mg/kg SD 0.05) was used in 108 children in the perioperative period and a further 56 children were given no opioid. Fifty-five of these 164 children vomited and 20 children required an overnight stay in hospital because of vomiting. The probability of vomiting or overnight stay in hospital was related to morphine dose (by logistic regression). The overall probability of vomiting after morphine 0.1 mg/kg was 50% and the probability of admission for vomiting with this dose was 10%. Pharmacodynamic parameter estimates for postoperative vomiting were P0 (the baseline probability of vomiting, with no opioid) 0.115, Pmax (the maximal probability of vomiting due to morphine) 0.997, ED50 (morphine dose that induces an effect equivalent to 50% of the logit Pmax) 0.18 mg/kg. Parameter estimates for overnight stay because of vomiting after morphine administration were P0 0.038, Pmax 0.999, ED50 0.369 mg/kg. Satisfactory postoperative analgesia in children has been reported with morphine 0.05 to 0.15 mg/kg. Doses above 0.1 mg/kg are associated with a greater than 50% incidence of vomiting. Our data suggests that lower doses of morphine are associated with a decreased incidence of emesis after tonsillectomy in children.

Adolescent↗

PubMed2006

Combining multistate capture-recapture data with tag recoveries to estimate demographic parameters.

Matrix population models that allow an animal to occupy more than one state over time are important tools for population and evolutionary ecologists. Definition of state can vary, including location for metapopulation models and breeding state for life history models. For populations whose members can be marked and subsequently reencountered, multistate mark-recapture models are available to estimate the survival and transition probabilities needed to construct population models. Multistate models have proved extremely useful in this context, but they often require a substantial amount of data and restrict estimation of transition probabilities to those areas or states subjected to formal sampling effort. At the same time, for many species, there are considerable tag recovery data provided by the public that could be modeled in order to increase precision and to extend inference to a greater number of areas or states. Here we present a statistical model for combining multistate capture-recapture data (e.g., from a breeding ground study) with multistate tag recovery data (e.g., from wintering grounds). We use this method to analyze data from a study of Canada Geese (Branta canadensis) in the Atlantic Flyway of North America. Our analysis produced marginal improvement in precision, due to relatively few recoveries, but we demonstrate how precision could be further improved with increases in the probability that a retrieved tag is reported.

Animal Migration↗

PubMed1996

Application of a biologically-based RFD estimation method to tetrachlorodibenzo-p-dioxin (TCDD) mediated immune suppression and enzyme induction.

The current methods for a reference dose (RfD) determination can be enhanced through the use of biologically-based dose response analysis. Methods developed here utilizes information from tetrachlorodibenzo-p-dioxin (TCDD) to focus on noncancer endpoints, specifically TCDD mediated immune system alterations and enzyme induction. Dose-response analysis, using the Sigmoid-Emax (EMAX) function, is applied to multiple studies to determine consistency of response. Through the use of multiple studies and statistical comparison of parameter estimates, it was demonstrated that the slope estimates across studies were very consistent. This adds confidence to the subsequent effect dose estimates. This study also compares traditional methods of risk assessment such as the NOAEL/safety factor to a modified benchmark dose approach which is introduced here. Confidence in the estimation of an effect dose (ED10) was improved through the use of multiple datasets. This is key to adding confidence to the benchmark dose estimates. In addition, the Sigmoid-Emax function when applied to dose-response data using nonlinear regression analysis provides a significantly improved fit to data increasing confidence in parameter estimates which subsequently improve effect dose estimates.

Animals↗

PubMed1999

Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.

In the randomized clinical trial setting, controlling for covariates is expected to produce variance reduction for the treatment parameter estimate and to adjust for random imbalances of covariates between the treatment groups. However, for the logistic regression model, variance reduction is not obviously obtained. This can lead to concerns about the assumptions of the logistic model. We introduce a complementary nonparametric method for covariate adjustment. It provides results that are usually compatible with expectations for analysis of covariance. The only assumptions required are based on randomization and sampling arguments. The resulting treatment parameter is a (unconditional) population average log-odds ratio that has been adjusted for random imbalance of covariates. Data from a randomized clinical trial are used to compare results from the traditional maximum likelihood logistic method with those from the nonparametric logistic method. We examine treatment parameter estimates, corresponding standard errors, and significance levels in models with and without covariate adjustment. In addition, we discuss differences between unconditional population average treatment parameters and conditional subpopulation average treatment parameters. Additional features of the nonparametric method, including stratified (multicenter) and multivariate (multivisit) analyses, are illustrated. Extensions of this methodology to the proportional odds model are also made.

Humans↗

PubMed1989

New signal processing techniques for improved precision of noninvasive impedance cardiography.

Impedance cardiographic determination of clinically important cardiac parameters such as systolic time intervals, stroke volume, and related cardiovascular parameters has not yet found adequate application in clinical practice, since its theoretical basis remains controversial, and the precision of beat-to-beat parameter estimation has until recently suffered under severe shortcomings of available signal processing techniques. High levels of noise and motion artifacts deteriorate signal quality and result in poor event detection. To improve the precision of impedance cardiography, new techniques for event detection and parameter estimation have been developed. Specifically, matched filtering and various signal segmentation and decomposition techniques have been tested on impedance signals with various levels of artificially superimposed noise and on actual recordings from subjects in a laboratory study of cardiovascular response to a cognitive challenge. Substantial improvement in the precision of impedance cardiography was obtained using the newly developed signal processing techniques. In addition, some preliminary evidence from comparisons of the impedance cardiogram with invasive aortic electromagnetic flow measurement in anesthetized rabbits is presented to address questions relating to the origin of the impedance signal.

Animals↗

PubMed1979

Limitations of the Eadie-Hofstee plot to estimate kinetic parameters of intestinal transport in the presence of an unstirred water layer.

In the presence of an intestinal unstirred water layer, the relationship between substrate concentration (C1) and unidirectional flux (Jd) is not described by the equation for a rectangular hyperbole. Accordingly, transformations of the Michaelis-Menten equation may not necessarily be linear and may lead to serious errors in the estimation of the affinity constant (Km) and maximal transport rate (Jdm) of carrier-mediated processes. An equation has previously been derived which described Jd under conditions of varying effective thickness or surface area of the unstirred water layer, the free diffusion coefficient of the probe molecule, and the distribution of transport sites along the villus. These theoretical curves have been analyzed by using the Eadie-Hofstee transformation (Jd vs. Jd/C1) of the Michaelis-Menten equation. Use of this plot leads to serious discrepancies between the true and apparent affinity constants and between true and apparent maximal transport rates. These differences are further magnified by failure to correct for the contribution of passive permeation. The Eadie-Hofstee plot is of use, however, to infer certain qualitative characteristics of active transport processes, such as the variation in affinity constants and overlying resistance of the unstirred water layer at different sites along the villus and to predict the adequacy of the correction for the contribution of passive permeation.

Animals↗

PubMed1997

Improved common line-based icosahedral particle image orientation estimation algorithms.

Modifications are described for the center and angular parameter estimation algorithms of common line-based particle image orientation determination which is an essential step in the three-dimensional reconstruction of icosahedral virus particles. The modifications incorporate a variety of image processing, pattern recognition, and statistical tools resulting in objective and automated orientation estimation algorithms. The modified algorithms were tested using electron cryo-microscopic particle images of three different virus specimens, with sizes 400-1250 A in diameter, covering a broad range of defocus values. Evaluation of these modified algorithms shows significant improvement over the previous algorithms. The center and angular parameters were estimated with higher accuracy allowing the identification of a larger number of particle orientations. Usage of the modified estimation algorithms resulted in the identification of particle orientations which could not to be identified using the algorithms before modification. Furthermore, these improvements have resulted in the determination of a better quality and a higher resolution three-dimensional reconstruction. The improved algorithms have been developed into a software package which can be obtained via the world wide web at http://ncmi.bioch.bcm.tmc.edu/pthuman.

Algorithms↗

PubMed2000

Key Concepts in Model Selection: Performance and Generalizability.

What is model selection? What are the goals of model selection? What are the methods of model selection and how do they work? Which methods perform better than others and in what circumstances? These questions rest on a number of key concepts in a relatively underdeveloped field. The aim of this paper is to explain some background concepts, to highlight some of the results in this special issue, and to add my own. The standard methods of model selection include classical hypothesis testing, maximum likelihood, Bayes method, minimum description length, cross-validation, and Akaike's information criterion. They all provide an implementation of Occam's razor, in which parsimony or simplicity is balanced against goodness-of-fit. These methods primarily take account of the sampling errors in parameter estimation, although their relative success at this task depends on the circumstances. However, the aim of model selection should also include the ability of a model to generalize to predictions in a different domain. Errors of extrapolation, or generalization, are different from errors of parameter estimation. So, it seems that simplicity and parsimony may be an additional factor in managing these errors, in which case the standard methods of model selection are incomplete implementations of Occam's razor. Copyright 2000 Academic Press.

Journal Article↗

PubMed2003

Efficient and accurate experimental design for enzyme kinetics: Bayesian studies reveal a systematic approach.

In areas such as drug development, clinical diagnosis and biotechnology research, acquiring details about the kinetic parameters of enzymes is crucial. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. We demonstrate that a Bayesian approach (the use of prior knowledge) can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian Utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of K(M) and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated.

Bayes Theorem↗

PubMed1987

Embryonic trunk circumference: a new biometric parameter for estimation of gestational age.

One hundred thirty-one uncomplicated, singleton pregnancies between 7 and 12 weeks of gestation were ultrasonically examined. Evaluation included confirmation of cardiac activity and measurements of fetal crown-rump length and trunk circumference. Both crown-rump length and trunk circumference were curvilinearly correlated with gestational age. A second- and third-degree polynomial equation best described this curvilinear relationship between trunk circumference and gestational age and between crown-rump length and gestational age. The crown-rump length and trunk circumference were similar in their prediction of gestational age, but the combination of the two parameters did not improve the prediction of gestational age. A nomogram of gestational age as predicted by trunk circumference was generated. These data provide an additional measurement for the estimation of gestational age in the first trimester of pregnancy.

Biometry↗

PubMed1989

Phenytoin prodrug 3-phosphoryloxymethyl phenytoin (ACC-9653): pharmacokinetics in patients following intravenous and intramuscular administration.

A phenytoin prodrug, 3-phosphoryloxymethyl phenytoin (ACC-9653; 1), has been developed with more favorable physicochemical properties than phenytoin for parenteral administration. The purpose of this study was to evaluate the pharmacokinetic profile of 1 following iv and im administration in adult patients receiving chronic oral phenytoin monotherapy. Each patient (9 males, 1 female) received a single iv dose of undiluted 1 equivalent to their twice daily phenytoin dose (100-200 mg). An equivalent dose of im 1 was administered in the gluteus maximus muscle one week later. Serial blood samples were obtained after each dose. Phenytoin and 1 concentrations were measured using HPLC. Compartmental analysis using weighted nonlinear least squares, and noncompartmental pharmacokinetic analysis were performed on each patient's concentration-time data. Data following iv 1 in eight of ten patients were best described using a two-compartment model. Mean pharmacokinetic parameter estimates for iv 1 in these patients were central volume of distribution (Vdc) of 0.040 +/- 0.0084 L/kg and plasma disappearance half-life (t1/2 alpha) of 8.0 +/- 2.9 min ("conversion" t1/2). Overall mean clearance (CL) was 0.24 +/- 0.080 L/kg/h in the 10 patients. Mean pharmacokinetic parameter estimates for im 1 were a rate constant (ka) of 2.47 +/- 1.41 h-1 and an absolute bioavailability (F) of 100.5 +/- 20.3%. Mean observed tmax values for phenytoin were 0.57 +/- 0.26 and 1.46 +/- 0.76 h following iv and im 1, respectively. Model-independent estimates of clearance agreed well with the compartmental analyses. Steady-state predose phenytoin concentrations did not significantly vary from the comparable concentrations following iv 1 administration (p = 0.22).(ABSTRACT TRUNCATED AT 250 WORDS)

Chromatography, High Pressure Liquid↗

PubMed2005

Modelling bivariate count series with excess zeros.

Bivariate time series of counts with excess zeros relative to the Poisson process are common in many bioscience applications. Failure to account for the extra zeros in the analysis may result in biased parameter estimates and misleading inferences. A class of bivariate zero-inflated Poisson autoregression models is presented to accommodate the zero-inflation and the inherent serial dependency between successive observations. An autoregressive correlation structure is assumed in the random component of the compound regression model. Parameter estimation is achieved via an EM algorithm, by maximizing an appropriate log-likelihood function to obtain residual maximum likelihood estimates. The proposed method is applied to analyze a bivariate series from an occupational health study, in which the zero-inflated injury count events are classified as either musculoskeletal or non-musculoskeletal in nature. The approach enables the evaluation of the effectiveness of a participatory ergonomics intervention at the population level, in terms of reducing the overall incidence of lost-time injury and a simultaneous decline in the two mean injury rates.

Accidents, Occupational↗