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At least 271 records · Page 15Linked to original sources

PubMed2002

Solubilization by cosolvents. Establishing useful constants for the log-linear model.

The purpose of this study was to develop constants for the log-linear cosolvent model, thereby allowing accurate prediction of solubilization in the most common pharmaceutical cosolvents: propylene glycol, ethanol, polyethylene glycol 400, and glycerin. The solubilization power (sigma) of each cosolvent was determined for a large number of organic compounds from the slope of their log-solubility vs. cosolvent volume fraction plots. The solubilization data at room temperature were either experimentally determined or obtained from the literature. The slopes of the nearly linear relationship between solubilization power and solute hydrophobicity (logK(ow)) were obtained by linear regression analysis for each considered cosolvent. Thus, knowing or calculating a compound's partition coefficient is all that is needed to predict solubilization.

Chemistry, Pharmaceutical↗

PubMed1999

Linear modeling of mRNA expression levels during CNS development and injury.

Large-scale gene expression data sets are revolutionizing the field of functional genomics. However, few data analysis techniques fully exploit this entirely new class of data. We present a linear modeling approach that allows one to infer interactions between all the genes included in the data set. The resulting model can be used to generate interesting hypotheses to direct further experiments.

Animals↗

PubMed2006

Genetic regulators of myelopoiesis and leukemic signaling identified by gene profiling and linear modeling.

Mechanisms controlling the balance between proliferation and self-renewal versus growth suppression and differentiation during normal and leukemic myelopoiesis are not understood. We have used the bi-potent FDB1 myeloid cell line model, which is responsive to myelopoietic cytokines and activated mutants of the granulocyte macrophage-colony stimulating factor (GM-CSF) receptor, having differential signaling and leukemogenic activity. This model is suited to large-scale gene-profiling, and we have used a factorial time-course design to generate a substantial and powerful data set. Linear modeling was used to identify gene-expression changes associated with continued proliferation, differentiation, or leukemic receptor signaling. We focused on the changing transcription factor profile, defined a set of novel genes with potential to regulate myeloid growth and differentiation, and demonstrated that the FDB1 cell line model is responsive to forced expression of oncogenes identified in this study. We also identified gene-expression changes associated specifically with the leukemic GM-CSF receptor mutant, V449E. Signaling from this receptor mutant down-regulates CCAAT/enhancer-binding protein alpha (C/EBPalpha) target genes and generates changes characteristic of a specific acute myeloid leukemia signature, defined previously by gene-expression profiling and associated with C/EBPalpha mutations.

Animals↗

PubMed2000

[Gibbs-Sampler approach for meta-analysis of multiple clinical trials using generalized linear model with random-effects].

OBJECTIVE: To investigate the use of the Gibbs-Sampler method in evaluating the relationship between clinic events and health risks in a meta-analysis of multiple clinical trials. METHODS: By using a generalized linear model with random-effects, Gibbs-Sampler technique was used in a meta-analysis of multiple clinical trials of angiotensin converting enzyme (ACE) inhibitors in patients with myocardial infarction (MI). RESULTS: When heterogeneity across different trials can not be ignored, compared with the classic method, the odds ratio of relative reinfarction risk estimated by the Gibbs-Sampler method would have less variation. The gain in the reduction of variation in estimate of the overall odds ratio was 9.52%. CONCLUSION: Implementation of the Gibbs-Sampler technique in meta-analysis of multiple clinical trials has the potential of reducing the inaccuracy caused by heterogeneity across trials.

Angiotensin-Converting Enzyme Inhibitors↗

PubMedDate not supplied

Analysis of incomplete multivariate data using linear models with structured covariance matrices.

Incomplete and unbalanced multivariate data often arise in longitudinal studies due to missing or unequally-timed repeated measurements and/or the presence of time-varying covariates. A general approach to analysing such data is through maximum likelihood analysis using a linear model for the expected responses, and structural models for the within-subject covariances. Two important advantages of this approach are: (1) the generality of the model allows the analyst to consider a wider range of models than were previously possible using classical methods developed for balanced and complete data, and (2) maximum likelihood estimates obtained from incomplete data are often preferable to other estimates such as those obtained from complete cases from the standpoint of bias and efficiency. A variety of applications of the model are discussed, including univariate and multivariate analysis of incomplete repeated measures data, analysis of growth curves with missing data using random effects and time-series models, and applications to unbalanced longitudinal data.

Humans↗

PubMed2001

Linear models of simple cells: correspondence to real cell responses and space spanning properties.

Despite their limitations, linear filter models continue to be used to simulate the receptive field properties of cortical simple cells. For theoreticians interested in large scale models of visual cortex, a family of self-similar filters represents a convenient way in which to characterise simple cells in one basic model. This paper reviews research on the suitability of such models, and goes on to advance biologically motivated reasons for adopting a particular group of models in preference to all others. In particular, the paper describes why the Gabor model, so often used in network simulations, should be dropped in favour of a Cauchy model, both on the grounds of frequency response and mutual filter orthogonality.

Animals↗

PubMedDate not supplied

Polymicrobial sepsis: an analysis of 184 cases using log linear models.

Polymicrobial sepsis is a common and frequently fatal clinical condition that has received relatively little attention in published reports. Retrospectively, we reviewed the case records of 184 patients with polymicrobial sepsis seen at three Dallas hospitals between 1972 and 1977. Analysis of clinical data using log linear models enabled us to identify significant positive correlations (p < 0.05) between mortality resulting from polymicrobial sepsis and underlying disease category, failure to manifest fever, a pulmonary portal of entry, hypotension, and hospital-associated sepsis. No significant correlation with outcome could be demonstrated for age, hospital service, species of infecting microorganisms, number of microorganisms isolated from blood, WBC count, or antimicrobial therapy. In spite of indirect evidence for synergistic relationships between microorganisms responsible for polymicrobial sepsis in man, we could not resolve whether antimicrobial regimens that are effective against all of the microorganisms participating in polymicrobial infections are required to insure a favorable outcome.

Adolescent↗

PubMed1989

Linearized model for error-compensated kinetic determinations without prior knowledge of reaction order or rate constant.

This paper describes a new algorithm for calculation of reaction orders, rate constants, and initial and final values of detector signal from several signal vs time data points. The algorithm utilizes a linearized version of the rate equation and is intended primarily to provide initial estimates of these kinetic parameters for other curve-fitting methods. However, under some circumstances, the linearized model can provide sufficiently reliable results that subsequent processing by other methods is not needed. Simulated data with different levels of superimposed noise, data densities, reaction orders, rate constants, and signal change are used to evaluate the algorithm both for its primary purpose of providing initial estimates for other curve-fitting methods and as an independent method. Results are compared with those obtained with a nonlinear least-squares method and two initial-rate methods. The new algorithm provides less reliable results than those obtained by the nonlinear curve-fitting method for some situations (e.g. reaction orders greater than two, low data densities) but has the advantage that it is applicable to reaction orders at and near unity where the nonlinear method to which it is compared fails.

Kinetics↗

PubMed2005

A linear model for managing the risk of antimicrobial resistance originating in food animals.

A linear population risk model used by the U.S. Food and Drug Administration (FDA) Center for Veterinary Medicine (CVM) estimates the risk of human cases of campylobacteriosis caused by fluoroquinolone-resistant Campylobacter. Among the cases of campylobacteriosis attributed to domestically produced chicken, the fluoroquinolone resistance is assumed to result from the use of fluoroquinolones in poultry in the United States. Properties of the linear population risk model are contrasted with those of a farm-to-fork model commonly used for microbial risk assessments. The utility of the linear population model for the purpose for which it was used by CVM is discussed.

Animals↗

PubMed1977

Stability of Wilkinson's linear model of prism adaptation over time for various targets.

Prism adaptation as measured by negative aftereffects (NA), proprioceptive shifts (PS), and visual shifts (VS) was assessed as a function of amount of exposure time and target specificity, whether an exposure and a test target background were the same or different, to determine the validity of Wilkinson's linear model (NA = PS + VS). With few exceptions the model was found to hold well up to 40 min of prism viewing regardless of type of exposure background. In addition target specificity affected magnitude of the NA component of adapation but not the PS and the VS components.

Adaptation, Ocular↗

PubMed1999

[Logistic regression vs other generalized linear models to estimate prevalence rate ratios].

In cross-sectional studies, to quantify the association between a risk factor and a disease (possibly adjusted for confounders), in the framework of the multiplicative model, the more obvious effect measure is a prevalence rate ratio with an associated confidence interval. The validity of this confidence interval requires an unbiased estimator and an appropriate estimate of the variance. In numerous epidemiological studies however, routine use is made of odds ratios and logistic regression. As the odds ratio per se is difficult to understand, prevalence odds ratios are often interpreted as prevalence rate ratios. But this latter approximation is valid only under the rare disease assumption. Moreover, in the logistic regression model, the variance of the estimates is based on the assumption of binomial variability, which is not always supported by the data; in the frequent case of overdispersion, this leads to under-estimation of the type I error rate. Yet, within the generalized linear model, it is easy to choose a link function other than the logit. For example, the log link (log-binomial model) is appropriate to directly estimate adjusted prevalence rate ratios. In case of overdispersion, it is also possible to achieve a better fit of the model, either by choosing another distribution in the exponential family or by estimating a dispersion parameter for the binomial distribution. Thus, there are no valid reasons for the systematic choice of odds ratio and of the logistic regression model to estimate prevalence rate ratios, unless the type of study imperatively requires their use.

Humans↗

PubMed2001

Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology.

One goal of a public health surveillance system is to provide a reliable forecast of epidemiological time series. This paper describes a study that used data collected through a national public health surveillance system in the United States to evaluate and compare the performances of a seasonal autoregressive integrated moving average (SARIMA) and a dynamic linear model (DLM) for estimating case occurrence of two notifiable diseases. The comparison uses reported cases of malaria and hepatitis A from January 1980 to June 1995 for the United States. The residuals for both predictor models show that they were adequate tools for use in epidemiological surveillance. Qualitative aspects were considered for both models to improve the comparison of their usefulness in public health. Our comparison found that the two forecasting modelling techniques (SARIMA and DLM) are comparable when long historical data are available (at least 52 reporting periods). However, the DLM approach has some advantages, such as being more easily applied to different types of time series and not requiring a new cycle of identification and modelling when new data become available.

Communicable Disease Control↗

PubMed2002

Experimental validation of a linear model for data reduction in chirp-pulse microwave CT.

Chirp-pulse microwave computerized tomography (CP-MCT) is an imaging modality developed at the Department of Biocybernetics, University of Niigata (Niigata, Japan), which intends to reduce the microwave-tomography problem to an X-ray-like situation. We have recently shown that data acquisition in CP-MCT can be described in terms of a linear model derived from scattering theory. In this paper, we validate this model by showing that the theoretically computed response function is in good agreement with the one obtained from a regularized multiple deconvolution of three data sets measured with the prototype of CP-MCT. Furthermore, the reliability of the model as far as image restoration in concerned, is tested in the case of space-invariant conditions by considering the reconstruction of simple on-axis cylindrical phantoms.

Computer Simulation↗

PubMed2000

A useful monotonic non-linear model with applications in medicine and epidemiology.

In medicine and epidemiology monotonic curves are important as models for relations which prior knowledge or scientific reasoning dictate should increase or decrease consistently with the predictor value. An example is the monotonically increasing relation between cigarette consumption and the risk of coronary heart disease. In this paper I propose a new class of monotonic non-linear models which generalizes the well-known power and exponential transformations of a covariate. The models are cousins of the Gompertz family of growth curves and include non-sigmoid and asymmetric sigmoid curves. I explore their properties and illustrate their usefulness in three substantial medical and epidemiological data sets.

Adolescent↗

PubMedDate not supplied

Log-linear model selections in a rural dental health study.

This field study sought to measure the effects of dental delivery and school-based, dental health education on use of dental health care by children in grades K-6. We attempted to control for two potential confounding factors by an approximate randomization of children into treatment groups with stratification on grade and initial oral disease levels. A backward elimination log-linear model selection procedure for the 5-factor classification permitted tests for higher-order interaction, namely effect-modification, confounding and collapsibility. We found that the effect of dental health education on use of dental care depended on the mode of dental delivery.

Child↗

PubMed1997

Generalized linear modelling for parasitologists.

Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical provlem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell show that this method is particularly prone to type I errors, and highlight a much more powerful and flexible alternative: generalized linear modelling.

Journal Article↗

PubMed1985

Application of the log-linear model in the prediction of the antinuclear antibody test in the dog.

To find possible associations between antinuclear antibody (ANA) pattern, ANA titer, and certain clinical changes and clinical laboratory test results in dogs, the veterinary medical records of 111 ANA-positive and 126 ANA-negative dogs were examined. Variables could not be found that had significant associations with ANA pattern (unlike the results in persons), because of the predominance of 2 patterns. A log-linear model for ANA titer adequately fit the observed frequency and included 2-way interactions between titer and polyarthritis, titer and hematologic disorders, and polyarthritis and lymphadenopathy.

Animals↗

PubMed2000

Individual toluene exposure in rotary printing: increasing accuracy of estimation by linear models based on protocols of daily activity and other measures.

Industrial exposure varies distinctly both between persons and for each person over time. It is often not possible to measure individual exposure repeatedly due to high costs. Therefore, a method for assessment of exposure is needed that accounts for inter- and intraindividual variability. We consider a strategy suggested by Preller et al. (1995, Scandinavian Journal of Work, Environment, and Health 21, 504-512), the idea of which is to predict exposure on several days via a linear model using additional variables as regressors. Those additional variables are easier to obtain than exposure measurements and are assumed to influence exposure. The paper gives a theoretical proof of the use of this method. An example is given using toluene exposure data from a study in a rotogravure printing plant.

Biometry↗