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At least 235 records · Page 13Linked to original sources

PubMed2005

137Cs in freshwater fish in Finland since 1986--a statistical analysis with multivariate linear regression models.

The accident at the Chernobyl nuclear power plant in 1986 significantly elevated the 137Cs levels of fish in Finnish lakes. About 6200 fish samples from 390 lakes comprising 20 species have been analysed for 137Cs since 1986. The sizes of the lakes varied from a few hectares to about 1000 km2. Activity concentrations of 137Cs in fish still varied widely in 2003, from 16 to 6400 Bq/kg fresh weight. This paper presents the results of statistical analyses with multivariate linear regression models carried out on the empirical data collected since 1986. The statistical analysis resulted in separate models for two time periods describing temporal changes of 137Cs in fish. The explanatory variables were fish species with various feeding habits, the size class of the lake, municipal division, drainage area, time since the deposition and deposition level of the municipality. The calculated values for 137Cs in fish did not differ statistically significantly from the observed values in the validation data. The explanatory variables explained 58% (the first time period) and 72% (the second time period) of the total variability of 137Cs in fish.

Animal Feed↗

PubMed2006

Automated single-trial measurement of amplitude and latency of laser-evoked potentials (LEPs) using multiple linear regression.

OBJECTIVE: Laser stimulation of Adelta-fibre nociceptors in the skin evokes nociceptive-specific brain responses (laser-evoked potentials, LEPs). The largest vertex complex (N2-P2) is widely used to assess nociceptive pathways in physiological and clinical studies. The aim of this study was to develop an automated method to measure amplitudes and latencies of the N2 and P2 peaks on a single-trial basis. METHODS: LEPs were recorded after Nd:YAP laser stimulation of the left hand dorsum in 7 normal volunteers. For each subject, a basis set of 4 regressors (the N2 and P2 waveforms and their respective temporal derivatives) was derived from the time-averaged data and regressed against every single-trial LEP response. This provided a separate quantitative estimate of amplitude and latency for the N2 and P2 components of each trial. RESULTS: All estimates of LEP parameters correlated significantly with the corresponding measurements performed by a human expert (N2 amplitude: R2=0.70; P2 amplitude: R2=0.70; N2 latency: R2=0.81; P2 latency: R2=0.59. All P<0.0001). Furthermore, regression analysis was able to extract an LEP response from a subset of the trials that had been classified by the human expert as without response. CONCLUSIONS: This method provides a simple, fast and unbiased measurement of different components of single-trial LEP responses. SIGNIFICANCE: This method is particularly desirable in several experimental conditions (e.g. drug studies, correlations with experimental variables, simultaneous EEG/fMRI and low signal-to-noise ratio data) and in clinical practice. The described multiple linear regression approach can be easily implemented for measuring evoked potentials in other sensory modalities.

Adult↗

PubMed2004

Non-adult dental age assessment: correspondence analysis and linear regression versus Bayesian predictions.

This study focuses on the evaluation of factors influencing the quality (accuracy and reliability) of non-adult dental age assessment from radiographic stages of permanent teeth (excluding the third molar). We used four distinct cross-sectional samples of 1,528 healthy children: 3 of known geographic origin (Ivory Coast, Iran and France) and 1 additional sample of children whose grandparents originated from a different continent. Two different methods of calculations are compared: the correspondence analysis combined with linear regression (CAR) and Bayesian predictions (with no independence assumption). Our results indicate that the quality of age assessment does not seem to depend predominantly on the use of geographic-specific standards. In the case of Bayesian predictions, we observed a clear trend in favour of significantly higher accuracy and reliability levels when using non-geographic-specific standards. One of the main advantage of Bayesian predictions over maximum likelihood methods of estimation is an overall increase in accuracy with high levels of reliability on a fraction of the test sample and, importantly, across all age categories (contrary to methods based on regression analysis). Importantly, in the case of Bayesian non-adult predictions, and contrary to age estimation techniques based on regression, a better quality does not depend on age.

Adolescent↗

PubMed2006

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel.

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.

Electronic Data Processing↗

PubMed2000

Factors influencing efficacy of nitrate therapy for stable angina pectoris: a multiple linear regression analysis.

In an open-label self-controlled study of 1,350 patients with stable angina pectoris (SAP), we previously demonstrated that 50 mg of isosorbide mononitrate (ISMN) slow release formulation once daily not only provided a better antianginal effect but also a better quality of life (QOL) than did the daily administration of multiple small doses of the compound. It is unknown whether certain patient characteristics contribute to this benefit. The objective of this article was to determine what independent factors contribute to this benefit. Multiple linear regression analysis was performed on the data from these 1,350 patients. Quality of life was assessed by the Marquis QOL-questionnaire for patients with angina and included the domains of immobility, pain, and psychological distress. For the purpose of this study, overall QOL was calculated as the pooled sums of the domain scores and expressed as mean scores on an ordinal scale of 10. Age did not influence the beneficial effect of nitrate therapy on QOL. Neither did gender, rhythmic disturbances, peripheral artery disease, or the concomitant use of calcium channel blockers or beta blockers. New York Heart Association (NYHA) angina classification was an independent variable: patients with a NYHA class I or II benefited less than did patients with NYHA III or IV (p = 0.02). Obese patients as well as hypertensive patients benefited less (p = 0.04 and 0.02), and smokers tended to benefit less also (p = 0.08). In contrast, hypercholesterolemia and diabetes mellitus improved the beneficial effect of nitrates on QOL (p = 0.03 and 0.05). The authors conclude that patients with coronary artery disease (CAD) and concomitant diabetes mellitus or hypercholesterolemia, a category particularly prone to early endothelial dysfunction and thus dysfunctional endogenous nitric oxide (NO) production, may benefit more from NO-donor therapy than patients without such concomitant conditions.

Angina Pectoris↗

PubMed2007

Quantification of diacylglycerol species from cellular extracts by electrospray ionization mass spectrometry using a linear regression algorithm.

Diacylglycerols (DAGs) play significant roles in both intermediate metabolism and signal transduction. These lipid species are second messengers involved in modulating a plethora of cellular processes. Evaluation of DAG species concentrations has been hampered by the lack of a reliable method for molecular species analysis within a complex mixture of cellular lipids. We describe a new method for quantitative analysis of DAG species from complex biological extracts based on positive mode electrospray ionization mass spectrometry without prior derivatization. Quantification is achieved using internal standards and calibration curves constructed by spiking cell extracts with different concentrations of DAG species containing various acyl chain lengths and degrees of unsaturation. The new mass spectral data processing algorithm incorporates a multiple linear regression model including a factor accountable for possible interactions between experimental preparations and the slope of the curve for the standards, allowing the examinations of the effects of sample origin conditions (such as cell types, phenotypes, etc.) and instrument variability on this slope. Internal standards provide a basis for quantification of 28 DAG molecular species detected in RAW 264.7 cells after stimulation of a G-protein coupled receptor with platelet activating factor. This method displays excellent reproducibility over the established range of concentrations with variations of < or =10% and is highly sensitive with a detection limit of 0.1-0.4 pmol/microL depending upon acyl chain composition. We have shown differential effects on various DAGs in response to a ligand which illustrates the importance of examining lipids at the molecular species level rather than as a single homogeneous entity.

Algorithms↗

PubMed1987

Simulation of plasma bentazepam levels in multiple dosage regimens from parameters established by non-linear regression and bayesian estimation.

In the present study, a simulation was made of the time-course of the plasma levels of bentazepam, administered orally at a dose of 25 mg with dosage intervals of 8, 12 and 24 h over 5 days of treatment. The pharmacokinetic parameters corresponding to a single-compartment model were calculated in a previous study with 10 patients who received the drug in a multiple dosage regimen, using all the data relating to plasma levels even though they corresponded to different administrations, by non-linear regression employing programs based on homoscedastic, heteroscedastic and bayesian estimation methods. The mean values of the kinetic parameters obtained previously and used in the present study were as follows: for the absorption constant, mean values of 2.33, 2.18 and 2.75 h-1 were used; for the elimination constant, the values used were 0.10, 0.09 and 0.22 h-1 and for the apparent distribution volume, the values used were 1.89, 2.89 and 0.80 l/kg, with each of the above-mentioned calculation programs, respectively. The highest value for the maximum concentration at steady state proved to be 313.2 ng/ml for a dosage interval of 8 h according to the parameters established with the program using homoscedastic estimation. In the same case (homoscedastic estimation) the highest value of the minimum at steady state was 168.7 ng/ml. By contrast, the lowest value of the maximum value at steady state--129.3 ng/ml--was obtained with a dosage interval of 24 h using the parameters of the heteroscedastic estimation method, while the lowest value of the corresponding minimum was also observed for an interval of 24 h but using the parameters of the bayesian estimation; this was 2.8 ng/ml.

Adult↗

PubMedDate not supplied

Determination of the acid dissociation constant of bromocresol green and cresol red in water/AOT/isooctane reverse micelles by multiple linear regression and extended principal component analysis.

The pKa of 3',3",5',5"tetrabromo-m-cresolsulfonephtalein (Bromocresol Green) and o-cresolsulphonephtalein (Cresol Red) was spectrophotometrically measured in a water/AOT/isooctane microemulsion in the presence of a series of buffers carrying different charges at different water/surfactant ratios. Extended Principal Component Analysis was used for a precise determination of the apparent pKa and of the spectra of the acid and base forms of the dye. The apparent pKa of dyes in water-in-oil microemulsions depends on the charge of the acid and base forms of the buffers present in the water pool. Combination with multiple linear regression increases the precision. Results are discussed taking into account the profile of the electrostatic potential in the water pool and the possible partition of the indicator between the aqueous core and the surfactant. The pKa corrected for these effects are independent of w0 and are close to the value of the pKa in bulk water. On the basis of a tentative hypothesis it is possible to calculate the true pKa of the buffer in the pool.

Bromcresol Green↗

PubMed1997

A detailed evaluation of adjustment methods for multiplicative measurement error in linear regression with applications in occupational epidemiology.

It is often appropriately assumed, based on both theoretical and empirical considerations, that airborne exposures in the workplace are lognormally distributed, and that a worker's mean exposure over a reference time period is a key predictor of subsequent adverse health effects for that worker. Unfortunately, it is generally impossible to accurately measure a worker's true mean exposure. We begin by introducing a familiar model for exposure that views this true mean, as well as logical surrogates for it, as lognormal random variables. In a more general context, we then consider the linear regression of a continuous health outcome on a lognormal predictor measured with multiplicative error. We discuss several candidate methods of adjusting for the measurement error to obtain consistent estimators of the true regression parameters. These methods include a simple correction of the ordinary least squares estimator based on the surrogate regression, the regression of the outcome on the covariates and on the conditional expectation of the true predictor given the observed surrogate, and a quasi-likelihood approach. By means of a simulation study, we compare the various methods for practical sample sizes and discuss important issues relevant to both estimation and inference. Finally, we illustrate promising adjustment strategies using actual lung function and dust exposure data on workers in the Dutch animal feed industry.

Analysis of Variance↗

PubMed2002

A statistical method for the determination of absorption rate constant estimated using the rat single pass intestinal perfusion model and multiple linear regression.

The guide "Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate Release Solid Dosage Forms Containing Certain Active Moieties/Active Ingredients Based on a Biopharmaceutical Classification System" (Rockville, MD: CDER, 2000) outlined non-in vivo tests of permeability that may satisfy the classification of a compound in the biopharmaceutical classification system. However, absent from that document were specific statistical methods to legitimatize the non-in vivo tests. This report describes the appropriate statistical treatment of absorption data, and recommends its adoption in the estimation of absorption and/or permeability measurements. The calculation of the absorption rate constants (k(a)) of ten compounds by a new multiple linear regression (MLR) method was completed after the separate perfusion of each compound through the rat single pass intestinal perfusion model (n = 3 rats per compound). Studentized residuals were evaluated to determine whether any statistically significant outliers were present in the data. The standard error of k(a) was estimated using variance components from the random effects model. The results were compared with the "traditional method" for k(a) calculations. Although both methods produced similar values of k(a), the MLR method's error estimate included multiple components of variability, which was largely ignored by the traditional method. The MLR method provided objective tests for outliers and achievement of steady-state. A preferred method for the statistical analysis of absorption data was demonstrated. These methods should be applied to all forms of permeability measurements, especially the non-in vivo measurements that classify a compound in the biopharmaceutical classification system.

Absorption↗

PubMed2001

Linear regression and computational neural network prediction of tetrahymena acute toxicity for aromatic compounds from molecular structure.

A quantitative structure toxicity relationship (QSTR) has been derived for a diverse set of 448 industrially important aromatic solvents. Toxicity was expressed as the 50% growth impairment concentration (ICG(50)) for the ciliated protozoa Tetrahymena and spans the range -1.46 to 3.36 log units. Molecular descriptors that encode topological, geometrical, electronic, and hybrid geometrical-electronic structural features were calculated for each compound. Subsets of molecular descriptors were selected via a simulated annealing technique and a genetic algorithm. From this reduced pool of descriptors, multiple linear regression models and nonlinear models using computational neural networks (CNNs) were derived and then used to predict the ICG(50) values for an external set of representative compounds. An average of 10 nonlinear CNN models with 11-5-1 architecture was found to best describe the system with root-mean-square errors of 0.28, 0.29, and 0.34 log units for the training, cross validation, and prediction sets, respectively.

Animals↗

PubMed1982

Grouping and linear regression.

With a large number of observations, the method of grouping is often employed to provide simpler graphs or tables. When one investigates the relationship between two variables, one usually groups based on the magnitude of the independent variable, and then plots the dependent variable averages against independent variable averages to get a clearer graph. If grouping is based on the magnitude of the dependent variable, the plot of group means as indicated above does not appropriately describe the relationship of the dependent variable to the independent variable. These results are demonstrated theoretically for the special case of bivariate normality (and thus linear regression), but would be expected to be similar for other distribution assumptions. An example is given from an epidemiological study.

Blood Pressure↗

PubMed1994

Bootstrap validation of pharmacodynamic models defined via stepwise linear regression.

When a pharmacodynamic model is to be considered as the basis for individualized drug dosing, validation of the model is clearly warranted. Rigorous validation is problematic when the training data set to be modeled has too few data points and no independent test data set exists. A simulation method known as the bootstrap lends itself particularly well to this dilemma. Bootstrap sampling allows simulation of needed test data sets that mimic the initial data set. Model validation is then undertaken by repeating the model formulation procedure on the bootstrap samples. For illustration, a pharmacodynamic model for leukopenia was constructed by stepwise linear regression from data of 41 patients with cancer treated with the drug amonafide. Stepwise regression analyses were then repeated for 100 bootstrap samples, which verified the initial selection of covariates for the model. Next the regression parameters and residual error standard deviation of the model were repeatedly estimated for 200 additional bootstrap samples. The bootstrap results confirmed the initial formulation of the pharmacodynamic model from the training data set.

Acetylation↗

PubMed1998

In vivo estimation of the glenohumeral joint rotation center from scapular bony landmarks by linear regression.

In this paper, a method is described for in vivo prediction of the glenohumeral joint rotation center (GH-r), necessary for the construction of a humerus local coordinate system in shoulder kinematic studies. The three-dimensional positions of five scapula bony landmarks as well as a large number of data points on the surface of the glenoid and humeral head were collected at 36 sets of cadaver scapulae and adjacent humeri. The position of GH-r in each scapula was estimated by mathematically fitting spheres to the glenoid and humeral head. GH-r prediction from scapula geometry parameters by linear regression resulted in a RMSE between measured and predicted GH-r of 2.32 mm for the x-coordinate, 2.69 mm for the y-coordinate and 3.04 mm for the z-coordinate. Application in vivo revealed a random humerus orientation error due to measurement inaccuracies of 1.35, 0.29 and 1.26 degrees standard deviation per rotation angle. The estimated total humerus orientation error including the offset error due to the regression model inaccuracy was 2.86, 0.84 and 2.69 degrees standard deviation. As these errors were about 15 and 20% of, respectively, the intra- and inter-subject variability of the humerus orientations measured, it is concluded that the method described in this paper allows for an adequate construction of a humerus local coordinate system.

Cadaver↗

PubMedDate not supplied

Prediction of peptide retention at different HPLC conditions from multiple linear regression models.

To quantitatively characterize the structure of a peptide and to predict its gradient retention time at given HPLC conditions three structural descriptors are used: (i) logarithm of the sum of retention times of the amino acids composing the peptide, log SumAA, (ii) logarithm of the van der Waals volume of the peptide, log VDW(Vol), (iii) and the logarithm of the peptide's calculated n-octanol-water partition coefficient, clog P. The log SumAA descriptor is obtained from empirical data for 20 natural amino acids, determined in a given HPLC system. The two other descriptors are calculated from the peptides' structural formulas using molecular modeling methods. The quantitative structure-retention relationships (QSRR), build by multiple linear regression, describe HPLC retention of peptide on a given chromatographic system on which the retention of the 20 amino acids was predetermined. A structurally diversified series of 98 peptides was employed. The predicted gradient retention times on several chromatographic systems were in good agreement with the experimental data. The QSRR equations, derived for a given system operated at variable gradient times and temperatures allowed for the prediction of peptide retention in that system. Matching the experimental HPLC retention to the theoretically predicted for a presumed peptide could facilitate original protein identification in proteomics. In conjunction with MS data, prediction of the retention time for a given peptide might be used to improve the confidence of peptide identifications and to increase the number of correctly identified peptides.

Amino Acid Sequence↗

PubMed2003

Detection of suppressor T lymphocytes and estimation of their frequency in limiting dilution assays by generalized linear regression modeling.

The estimate of the frequency of suppressor T lymphocytes in unfractionated cell populations remains challenging, mainly because these regulatory cells do not display specific immunophenotypic markers. In this paper, we describe a novel theoretical approach for quantifying the frequency of suppressor cells. This method is based on limiting dilution data modeling, and allows the simultaneous estimation of the frequencies of both proliferating and suppressor cells. We used previously published biological data, characterizing the inhibiting activity of suppressor T cell clones. Starting from these data, we propose a mathematical model describing the interaction between suppressor and proliferating T cells, and applied to a Poisson process. Limiting dilution data corresponding to this non-single-hit, suppressor two-target Poisson model were artificially generated, then modeled according to a generalized linear regression procedure. Deviation from the single-hit Poisson model was revealed by a statistical slope test, and a stepwise analysis of the regression appeared to be an efficient method that strongly argued in favor of the presence of suppressor cells. By using the frequency of proliferating T cells calculated in the first step of the regression, we demonstrated the possibility to provide a reasonable estimate of the frequency of suppressor T cells. Based on these findings, a practical decision-making procedure is given to perform standard analyses of limiting dilution data.

Animals↗

PubMed2001

Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing.

Real-time parametric statistical analysis of functional MRI (fMRI) data would potentially enlarge the scope of experimentation and facilitate its application to clinical populations. A system is described that addresses the need for rapid analysis of fMRI data and lays the foundation for dealing with problems that impede the application of fMRI to clinical populations. The system, I/OWA (Input/Output time-aWare Architecture), combines a general architecture for sampling and time-stamping relevant information channels in fMRI (image acquisition, stimulation, subject responses, cardiac and respiratory monitors, etc.) and an efficient approach to manipulating these data, featuring incremental subsecond multiple linear regression. The advantages of the system are the simplification of event timing and efficient and unified data formatting. Substantial parametric analysis can be performed and displayed in real-time. Immediate (replay) and delayed off-line analysis can also be performed with the same interface. The capabilities of the system are demonstrated in normal subjects using a polar visual angle phase mapping paradigm. The system provides a time-accounting infrastructure that readily supports standard and innovative approaches to fMRI. Magn Reson Med 45:289-298, 2001.

Adult↗

PubMed1985

Estimation of the initial velocity of enzyme-catalysed reactions by non-linear regression analysis of progress curves.

Most methods for studying the kinetic properties of an enzyme involve the determination of initial velocities. When the reaction progress curve shows significant curvature due to depletion of the substrate, accumulation of inhibitory products or instability of the enzyme, estimation of the initial velocity is a subjective and inexact process. Two methods have been suggested [Cornish-Bowden (1975) Biochem. J. 144, 305-312; Boeker (1982) Biochem J. 203, 117-123] that attempt to eliminate this subjective element. The present study offers a third alternative, which is based on fitting a reparameterized form of the integrated Michaelis-Menten equation to the progress curves by non-linear regression. This method yields estimates and standard errors of the initial velocity and of the time to reach 50% reaction. No prior knowledge of the apparent product concentration at zero time or infinite time is required, since both of these quantities are also estimated from the data. It is shown that this method yields reliable estimates of the initial velocity under a wide range of circumstances, including those where the two previously published methods perform poorly.

Enzyme Activation↗