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Results for “Correlation of Data”

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At least 127 records · Page 7Linked to original sources

PubMed1982

[Carotid artery stenosis and cerebral circulatory disorders (clinico-angiographic correlation)].

Data on the clinical and angiographic examinations of 192 patients with stenoses of the extracranial part of the carotid artery are compared. The state of the intracranial branches of the median cerebral artery was evaluated with the use of Ring-Waddington's grid. Seven neurological syndromes resulting from the pathology of the intracranial branches of the carotid artery system are specified. In 70% of the cases structural changes of the intracranial vessels were revealed, these changes being the most likely due to thromboembolism from the stenotic carotid artery. The anterior parietal branch was involved less frequently, and the posterior branches of the median cerebral and the orbitofrontal arteries were affected extremely rarely.

Adult↗

PubMed2004

Cloned human 5-HT1A receptor pharmacology determined using agonist binding and measurement of cAMP accumulation.

Twenty agonists and nine antagonists were evaluated for their ability to compete for [3H]-8-hydroxy-2-(di-n-propylamino)tetralin ([3H]-8-OH-DPAT) binding to the cloned human serotonin-1A (ch-5-HT1A) receptor expressed in Chinese hamster ovary cells and for their ability to alter adenylyl cyclase activity in the same cells. The most potent full agonists of high affinity included N,N-dipropyl-5-carboxamidotryptamine (pEC50=9.6 +/- 0.1), MDL 73005EF (pEC50=9.3 +/- 0.2), 5-methyl-urapidil (pEC50=9.2 +/- 0.1), 5-carboxamidotryptamine (pEC50=9.1 +/- 0.2), R(+)-8-OH-DPAT (pEC50=8.6 +/- 0.1) and BMY-7378 (pEC50=8.6 +/- 0.1). WB-4101 (pEC50=8.3 +/- 0.2; IA=79%), clozapine (pEC50=8.1 +/- 0.3; IA=29%), (buspirone (pEC50=7.6 +/- 0.2; IA=79%), quipazine (pEC50 <5; IA=45%) and R-DOI (pEC50 < 5; IA=31%) were weaker agonists with partial agonist properties. The most potent antagonists were WAY-100,635 (pKi=10.2 +/- 0.1), methiothepin (pKi=8.8 +/- 0.2), spiperone (pKi=8.7 +/- 0.2) and NAN-190 (pKi=8.5 +/- 0.2). The receptor affinities and functional potencies were well correlated (r=0.88; P <0.0001). Our binding data correlated well with the pharmacology of endogenous 5-HT1A receptors in the rabbit iris-ciliary body (r=0.91; P <0.001) and rat hippocampus (r=0.93, P <0.0001). Our functional cAMP data correlated well with other cAMP accumulation data (r=0.8, P <0.01 vs calf hippocampus) but less so with [35S]-GTPgammaS binding to the ch-5-HT(1A) receptor as a functional activity read-out (r=0.58, P <0.05). The present study provides a detailed pharmacological characterization of the ch-5-HT1A receptor using binding and functional assays.

Adenylyl Cyclase Inhibitors↗

PubMed2006

Multi-level zero-inflated poisson regression modelling of correlated count data with excess zeros.

Count data with excess zeros relative to a Poisson distribution are common in many biomedical applications. A popular approach to the analysis of such data is to use a zero-inflated Poisson (ZIP) regression model. Often, because of the hierarchical study design or the data collection procedure, zero-inflation and lack of independence may occur simultaneously, which render the standard ZIP model inadequate. To account for the preponderance of zero counts and the inherent correlation of observations, a class of multi-level ZIP regression model with random effects is presented. Model fitting is facilitated using an expectation-maximization algorithm, whereas variance components are estimated via residual maximum likelihood estimating equations. A score test for zero-inflation is also presented. The multi-level ZIP model is then generalized to cope with a more complex correlation structure. Application to the analysis of correlated count data from a longitudinal infant feeding study illustrates the usefulness of the approach.

Adult↗

PubMed1997

Sample size calculations for studies with correlated observations.

Correlated data occur frequently in biomedical research. Examples include longitudinal studies, family studies, and ophthalmologic studies. In this paper, we present a method to compute sample sizes and statistical powers for studies involving correlated observations. This is a multivariate extension of the work by Self and Mauritsen (1988, Biometrics 44, 79-86), who derived a sample size and power formula for generalized linear models based on the score statistic. For correlated data, we appeal to a statistic based on the generalized estimating equation method (Liang and Zeger, 1986, Biometrika 73, 13-22). We highlight the additional assumptions needed to deal with correlated data. Some special cases that are commonly seen in practice are discussed, followed by simulation studies.

Biometry↗

PubMed2003

Randomizing patients by family practice: sample size estimation, intracluster correlation and data analysis.

BACKGROUND: Cluster randomized controlled trials increasingly are used to evaluate health interventions where patients are nested within larger clusters such as practices, hospitals or communities. Patients within a cluster may be similar to each other relative to patients in other clusters on key variables; therefore, sample size calculations and analyses of results require special statistical methods. OBJECTIVE: The purpose of this study was to illustrate the calculations used for sample size estimation and data analysis and to provide estimates of the intraclass correlation coefficients (ICCs) for several variables using data from the Seniors Medication Assessment Research Trial (SMART), a community-based trial of pharmacists consulting to family physicians to optimize the drug therapy of older patients. METHODS: The study was a paired cluster randomized trial, where the family physician's practice was the cluster. The sample size calculation was based on a hypothesized reduction of 15% in mean daily units of medication in the intervention group compared with the control group, using an alpha of 0.05 (one-tailed) with 80% power, and an ICC from pilot data of 0.08. ICCs were estimated from the data for several variables. The analyses comparing the two groups used a random effects model for a meta-analysis over pairs. RESULTS: The design effect due to clustering was 2.12, resulting in an inflation in sample size from 340 patients required using individual randomization, to 720 patients using randomization of practices, with 15 patients from each of 48 practices. ICCs for medication use, health care utilization and general health were <0.1; however, the ICC for mean systolic blood pressure over the trial period was 0.199. CONCLUSIONS: Compared with individual randomization, cluster randomization may substantially increase the sample size required to maintain adequate statistical power. The differences in ICCs among potential outcome variables reinforce the need for valid estimates to ensure proper study design.

Aged↗

PubMed2003

Maximum-entropy decomposition of fluorescence correlation spectroscopy data: application to liposome-human serum albumin association.

Fluorescence correlation spectroscopy was used to measure the diffusion behavior of a mixture of DMPC or DMPC/DMPG liposomes with human serum albumin (HSA) and mesoporphyrin (MP), which was used as the fluorescent label for liposomes and HSA as well. For decomposing the fluorescence intensity autocorrelation function (ACF) into components corresponding to a liposome population, HSA and MP, we used a maximum entropy procedure that computes a distribution of diffusion times consistent with the ACF data. We found that a simple parametric non-linear fit with a discrete set of decay components did not converge to a stable parameter set. The distribution calculated with the maximum entropy method was stable and the average size of the particles calculated from the effective diffusion time was in good agreement with the data determined using the discrete-component fit.

Biophysical Phenomena↗

PubMed2006

Detecting correlation changes in electrophysiological data.

A correlation multi-variate analysis of variance (MANOVA) test to statistically analyze changing patterns of multi-electrode array (MEA) electrophysiology data is developed. The approach enables us not only to detect significant mean changes, but also significant correlation changes in response to external stimuli. Furthermore, a method to single out hot-spot variables in the MEA data both for the mean and correlation is provided. Our methods have been validated using both simulated spike data and recordings from sheep inferotemporal cortex.

Action Potentials↗

PubMed2000

GEE with Gaussian estimation of the correlations when data are incomplete.

This paper considers a modification of generalized estimating equations (GEE) for handling missing binary response data. The proposed method uses Gaussian estimation of the correlation parameters, i.e., the estimating function that yields an estimate of the correlation parameters is obtained from the multivariate normal likelihood. The proposed method yields consistent estimates of the regression parameters when data are missing completely at random (MCAR). However, when data are missing at random (MAR), consistency may not hold. In a simulation study with repeated binary outcomes that are missing at random, the magnitude of the potential bias that can arise is examined. The results of the simulation study indicate that, when the working correlation matrix is correctly specified, the bias is almost negligible for the modified GEE. In the simulation study, the proposed modification of GEE is also compared to the standard GEE, multiple imputation, and weighted estimating equations approaches. Finally, the proposed method is illustrated using data from a longitudinal clinical trial comparing two therapeutic treatments, zidovudine (AZT) and didanosine (ddI), in patients with HIV.

Anti-HIV Agents↗

PubMed2005

frailtypack: a computer program for the analysis of correlated failure time data using penalized likelihood estimation.

Correlated survival outcomes occur quite frequently in the biomedical research. Available software is limited, particularly if we wish to obtain smoothed estimate of the baseline hazard function in the context of random effects model for correlated data. The main objective of this paper is to describe an R package called frailtypack that can be used for estimating the parameters in a shared gamma frailty model with possibly right-censored, left-truncated stratified survival data using penalized likelihood estimation. Time-dependent structure for the explanatory variables and/or extension of the Cox regression model to recurrent events are also allowed. This program can also be used simply to obtain directly a smooth estimate of the baseline hazard function. To illustrate the program we used two data sets, one with clustered survival times, the other one with recurrent events, i.e., the rehospitalizations of patients diagnosed with colorectal cancer. We show how to fit the model with recurrent events and time-dependent covariates using Andersen-Gill approach.

Cluster Analysis↗

PubMed1991

Computerised vascular data management: a flexible modular registry suitable for the evaluation of long-term results in patients subjected to multiple interventions.

We have designed a computerised vascular registry (CVR) combining storage of complete patient histories in minute detail, including reoperations and long-term follow-up, with clinical applicability. The basic concept of this registry is the storage of data in a structure of cycles (one cycle per treatment episode), modules (clusters of logistically correlated data) and data-chapters (clusters of clinically correlated data). The registry was designed to minimally interfere with routine clinical practice, for instance by collecting the data step-by-step at the wards and out-patient clinics, quite similar to traditional record keeping. The CVR enables production of inventories of all stored data. More importantly, and in addition to other registries, the structure of our registry adequately enables analyses of data of patients with multiple interventions and patients with long-term follow-up. A microcomputer was used for the input of data, which were stored in a structure enabling effortless transportation of the data to a mainframe computer. Standard software programs were used. Simple inventories and analyses were performed on a microcomputer, and a mainframe computer was used for more complex analyses. The performance and applicability of the newly designed CVR was thoroughly tested in comprehensive retrospective studies. On the basis of these experiences several adjustments were carried out after which the CVR was introduced into clinical practice.

Computer Systems↗

PubMed2003

Nonparametric quantile estimation with correlated failure time data.

In biomedical studies, correlated failure time data arise often. Although point and confidence interval estimation for quantiles with independent censored failure time data have been extensively studied, estimation for quantiles with correlated failure time data has not been developed. In this article, we propose a nonparametric estimation method for quantiles with correlated failure time data. We derive the asymptotic properties of the quantile estimator and propose confidence interval estimators based on the bootstrap and kernel smoothing methods. Simulation studies are carried out to investigate the finite sample properties of the proposed estimators. Finally, we illustrate the proposed method with a data set from a study of patients with otitis media.

Child, Preschool↗

PubMed1998

Statistical analysis of correlated binary data in ophthalmology: a weighted logistic regression approach.

Ophthalmological studies often deal with correlated binary outcome variables. We propose a weighted logistic regression method to account for the intraclass correlations between eyes. Using simulation studies, we compared this method with two standard logistic regression approaches: a) based on eyes as the unit of analysis and b) treating individuals classified as cases if at least one eye is affected. The considered approaches were evaluated in terms of type I error, power and estimation properties. The simulation results reveal that the subject-based approach can lead to substantial bias in regression coefficient estimates when the correlation between eyes is heterogeneous across groups or when it is low, and that power is directly affected by this bias. Furthermore, the standard eye-based approach, which ignores intrasubject correlations, leads to inflated type I error rates. The proposed weighted approach performed well in all of the situations considered. This is a simple method which can be implemented using any current statistical or epidemiological package that includes logistic regression analysis.

Aged↗

PubMed1997

Effect of omitted confounders on the analysis of correlated binary data.

Marginal analysis using the generalized estimating equation approach is widely applied to correlated observations, as occur in studies with clusters and in longitudinal follow-up of individuals. In this article, we investigate the effect of confounding in such models. We assume that a risk factor x and a confounder z are related by a generalized linear model to the outcome y, which can be binary or ordinal. In order to investigate confounding arising from the omission of z, a joint structure for x and z must be specified. Modeling normally distributed (x,z) as sums of between- and within-individual (or cluster) components allows us to incorporate different degrees of between- and within-individual correlation. Such a structure includes, as special cases, cohort and period effects in longitudinal settings and random intercept models. The latter situation corresponds to allowing z to vary only on the between-individual (or cluster) level and to be uncorrelated with x, and leads to attenuation of the coefficient of x in marginal models with the logit and probit links. More complex situations occur when z is allowed to also vary on the within-individual (or cluster) level and when z is correlated with x. We examine the model specification and the expected bias when fitting a marginal model in the presence of the omitted confounder z. We derive general formulas and interpret the parameters and results in an ongoing cohort study. Testing for omitted covariates is also discussed.

Adult↗

PubMed2006

Markov models for repeated ordinal data.

OBJECTIVES: To demonstrate the application of Markov models, especially for ordinal outcomes, within the context of regression models for correlated data. METHODS: A brief review of regression methods for correlated data is given. A proportional odds model and a continuation ratio model is applied to repeated measurements of macular pigment density, obtained in an intervention study on the supplementation of macular carotenoids. The correlation between repeated assessments is assumed to follow a first-order Markov model. The models are implemented with standard statistical software. RESULTS: Both models, though not directly comparable, provide a similar conclusion. The application of these models with standard statistical software is straightforward. CONCLUSIONS: Markov models can be valuable alternatives to random effects modes or procedures based on generalized estimation equations.

Carotenoids↗

PubMed2006

Antifungal susceptibility testing.

Antifungal susceptibility testing has been in routine use now for more than 15 years and has become a useful tool for clinicians who are faced with difficult treatment decision. Although most clinicians order susceptibility testing, much confusion still exists regarding the use of the results. Sufficient data have been generated to determine susceptibility trends for specific fungi against specific agents, but correlation data are minimal. Despite the lack of correlation data, antifungal susceptibility testing continues to provide useful information to assist with patient care.

Antifungal Agents↗

PubMed2006

Multisubject activation detection in fMRI by testing correlation of data with a signal subspace.

In this article, a generalized likelihood ratio test is proposed to assess the correlation between multisubject functional MRI (fMRI) time series and bases of a signal subspace for detecting the existence of group activation in each voxel of the brain. The signal subspace is generated by a design matrix using the time series of the desired effects. The proposed method leads to testing the product of eigenvalues of a specific matrix. The eigenvector corresponding to the largest eigenvalue is the weighting vector for the linear combination of time series of various subjects that has the maximum correlation with the signal subspace. In another method, namely, canonical correlation analysis, the largest eigenvalue of the above matrix is tested for activation detection. Surrogate data on resting state (no activation) are generated by randomization and used to estimate the statistical distribution of these parameters under the null hypothesis condition. A postprocessing step is applied to prevent false detection of voxels that are not sufficiently active (among subjects) by defining a minimum ratio for the active population. The proposed methods are applied on simulated and experimental fMRI data, and the results are compared with those of the general linear model (GLM; using the SPM and FMRISTAT toolboxes). The proposed methods showed higher detection sensitivity as compared with the GLM for activation detection in simulated data. Similarly, they detected more activated regions than did the GLM from multisubject experimental fMRI data on a visual (sensorimotor) event-related task.

Algorithms↗

PubMed2001

Quantitative assessment of DNA microarrays--comparison with Northern blot analyses.

DNA microarray is a powerful technology that provides the expression profile of thousands of genes. However, less attention has been paid to its quantitative aspect. In this study, we constructed a small-scale DNA microarray that contains 84 genes and characterized its quantitative aspect. Analyses with this microarray showed that 17 genes were induced, whereas 8 genes were suppressed at least twofold during the differentiation of mouse embryonic stem cells. When repeated with the same combination of fluorescent dyes for probe labeling, the microarray produced consistent data (correlation coefficient = 0.991). In contrast, data were less consistent when repeated with the reverse combination of dyes (correlation coefficient = 0.945). The effect of dye combination was particularly evident in several genes. Total RNA (15 microg) and poly(A) RNA (0.5 microg) showed comparable sensitivity and produced essentially identical data (correlation coefficient = 0.983). The sensitivity of the DNA microarrays was slightly inferior to that of Northern blot analyses. In most genes, data obtained with the two methods were consistent. However, in 4 of 46 genes compared, DNA microarrays failed to detect the expression changes that were revealed by Northern blot. These data demonstrated that DNA microarrays provide quantitative data comparable to Northern blot in general, but a few issues must be considered when analyzing data.

Animals↗

PubMed1998

Ethionine toxicity in vitro: the correlation of data from rat hepatocyte suspensions and monolayers with in vivo observations.

The hepato-steatogenic compound ethionine has been used to investigate the correlations between in vivo and in vitro toxicity data. The aim was to find a suitable model of toxicity in hepatocyte suspensions or monolayers in vitro, which could predict the known toxicity of ethionine in vivo and which could be implemented in screening compounds of unknown toxicity. Thus a variety of markers of cytotoxicity, metabolic competence and liver-specific functions were investigated in rat hepatocyte suspensions and monolayers and compared with in vivo data in the rat. The following markers were measured in the appropriate system: (1) Neutral red uptake; 3-(4,5 dimethyl)thiazol-2-yl,-2,5-diphenyl tetrazolium bromide (MTT) reduction; lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and alanine aminotransferase (ALT) leakage (cytotoxicity). (2) ATP levels, protein synthesis and glutathione (GSH) levels (metabolic competence). (3) Urea and triglyceride synthesis and beta-oxidation (liver specific functions). Ethionine (0-30 mM) did not affect the markers of direct cytotoxicity, except neutral red uptake, which was reduced by 18 and 30 mM ethionine after 20 h in culture. ATP and GSH depletion occurred in hepatocyte suspensions at the highest concentrations of ethionine (20 and 30 mM) after 1 h. In monolayers, GSH levels were reduced after 4 h, but not 20 h. Urea synthesis was increased in hepatocyte suspensions from 1 to 3 h by 10-30 mM ethionine and reduced after 20 h in cultured hepatocytes (18-30 mM). Protein synthesis was reduced and beta-oxidation was increased in ethionine-treated hepatocyte suspensions. Unfortunately, there was no measurable effect on triglyceride accumulation within cells (the major biochemical change in vivo) in either system. Ethionine treated hepatocytes in suspension showed the same rate of triglyceride synthesis and transportation out of cells as control cells. Thus, hepatocyte suspensions were able to mimic the early biochemical effects of ethionine in vivo (ATP and GSH depletion, inhibition of protein synthesis) and some effects on urea synthesis, but monolayer cultures appeared to be less sensitive to the toxicity of ethionine. However, neither in vitro system was able to model the effects of ethionine on the accumulation of triglycerides in vivo.

Adenosine Triphosphate↗