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At least 1,279 records · Page 71Linked to original sources
PubMed1996

Three-dimensional head kinematics and cervical range of motion in the diagnosis of patients with neck trauma.

OBJECTIVE: To create a statistical model using three-dimensional (3D) head kinematics and range of motion (ROM) to distinguish between people with whiplash syndrome and asymptomatic controls. STUDY DESIGN: Cross-sectional study to estimate validity of diagnostic measures. METHODS: Fifty-one asymptomatic controls (most of whom were women), 18-35 yr old and 30 matched whiplash trauma patients seeking care from suburban outpatient clinics were sought. 3D kinematic parameters of head motion were obtained during tracking tasks (e.g., flexion, extension, etc.) and cervical ROM was measured via a head mounted inclinometer. Their level of pain and disability was assessed via a self-administered neck disability index questionnaire and visual analog pain scale (VAS). RESULTS: A scoring system of biomechanical abnormalities derived from the vertical piercing point, its second derivative and symmetry during oblique tasks. The scores ranged from a minimum of 0 to a maximum of 3. A cutoff of > or = 0.5 correctly identified the greatest number of subjects and minimized false positives (sensitivity 77%, specificity 82%, likelihood ratio 4.5). ROM performed similarly well at a cutoff of 1 SD below the normative mean (sensitivity 77%, specificity 84%, likelihood ratio 3.9). CONCLUSIONS: There is potential for biomechanical analysis to objectively detect abnormalities. The statistical model yielded moderate to high sensitivity and specificity using 3D helical-axis parameters of the head and standard ROM. The model development will continue via this process in future studies. These data could be a first step toward the creation of useful, noninvasive protocols for the diagnosis and management of soft tissue trauma of the neck.

Adolescent↗
PubMed2005

Comparison of linear weighting schemes for perfect match and mismatch gene expression levels from microarray data.

BACKGROUND: Data analytic approaches to Affymetrix microarray data include: (a) a covariate model, in which the observed signal is some estimated linear function of perfect match (PM) and mismatch (MM) signals; (b) a difference model [PM-MM]; and (c) a PM-only model, in which MM data is not utilized. METHODS: By decomposing the correlations among the variables in the statistical model and making certain assumptions, we theoretically derive the statistical model that reflects the actual gene expression level under a variety of conditions expected in microarray data. RESULTS AND CONCLUSION: When modeling non-systematic variation, the covariate model provides maximum flexibility and often reflects the actual gene expression levels better than the difference model. However, the PM-only model demonstrates superior power in an overwhelming majority of realistic situations, which provides theoretical support for the current trend to employ PM-only models in microarray data analyzes.

Analysis of Variance↗
PubMed2003

Non-rigid image registration using a statistical spline deformation model.

We propose a statistical spline deformation model (SSDM) as a method to solve non-rigid image registration. Within this model, the deformation is expressed using a statistically trained B-spline deformation mesh. The model is trained by principal component analysis of a training set. This approach allows to reduce the number of degrees of freedom needed for non-rigid registration by only retaining the most significant modes of variation observed in the training set. User-defined transformation components, like affine modes, are merged with the principal components into a unified framework. Optimization proceeds along the transformation components rather then along the individual spline coefficients. The concept of SSDM's is applied to the temporal registration of thorax CR-images using pattern intensity as the registration measure. Our results show that, using 30 training pairs, a reduction of 33% is possible in the number of degrees of freedom without deterioration of the result. The same accuracy as without SSDM's is still achieved after a reduction up to 66% of the degrees of freedom.

Algorithms↗
PubMed2002

Modelling the state of bone cysts by statistical analysis of intensities of x-ray images.

The subject of this article is the state of a benign bone disease. The principle aim of this study is the construction of a statistical model for estimating the otherwise unobservable variable for the state of the disease. The distribution of intensity of X-ray images of the affected part and the healthy part of the bone are evaluated. Quantiles of both distributions are used in the estimation of a multinomial logit model by which the variable for the state of the disease is quantified.

Bone Cysts↗
PubMed1992

Predicting the probability of malignancy of the neck mass with logistic regression model: a statistical analysis of excisional biopsy of neck masses.

One hundred and fifty-six patients with neck lesions were selected into this retrospective study between January, 1989 and December, 1990. All the patients visited OPD with the chief complaint of neck mass without other apparent symptoms and signs after initial work-up. They can be divided into 5 types according to the pathological reports: type a) metastatic lesion, type b) malignant lymphoma, type c) TB lymphadenitis, type d) miscellaneous benign lesion, and type e) inadequate specimen. They represent 24.4%, 12.8%, 9.6%, 52.6%, and 0.6% of the patients respectively. Type a) and type b) were classified as group of malignancy and the other three types were group of benignancy. Chi-square test and t-test were then used to evaluate the significant level of difference in each semiological factor between both groups. Several parameters were found to reach significant level, including tumor fixation, age of the patients, tenderness, location and size of the tumor, and history of cancer. Stepwise logistic regression was utilized to obtain a regression equation to predict the probability of malignancy in OPD patients with neck masses. The accuracy rate of prediction is 83.3%, if the cutpoint value is 0.5. A clinician can therefore avoid untimely excisional biopsy and delay in treatment planning.

Adolescent↗
PubMed2006

Modeling the statistics of elementary calcium release events.

Elementary Ca(2+) signals, such as "Ca(2+) puffs", which arise from the release of Ca(2+) from endoplasmic reticulum through small clusters of inositol 1,4,5-trisphosphate receptors, are the building blocks for intracellular Ca(2+) signaling. The small number of release channels involved during a Ca(2+) puff renders the puffs stochastic, with distributed amplitudes, durations, and frequency, well characterized experimentally. We present a stochastic model that accurately describes simultaneously the statistical properties of the duration, amplitudes, frequencies, and spatial spread with a single set of parameters.

Calcium↗
PubMed2006

Investigating indicators and determinants of asthma in young adults.

BACKGROUND: In epidemiological studies on asthma determinants an extreme variability in results exists, probably due to different criteria utilised for defining of an asthma 'case' and for measuring determinants. We aimed to assess multiple indicators and multiple determinants of asthma in young adults by applying latent variable mixture models (LVMMs), a novel statistical modelling with hidden (or latent) variables. METHODS: We consider the pooled data of 1103 subjects (aged 20-44 years) from the three Italian centres of the European Community Respiratory Health Survey (ECRHS 1), a standardised database. Underlying multiple asthma indicators (clinicians' diagnosis, self-report symptoms, respiratory trials) both a latent two-class of asthma syndrome, and three continuous latent variables (severity of diagnosed asthma, severity of asthma symptoms, and severity of respiratory function) were investigated. RESULTS: Family history was the more relevant predictor of the two-class of asthma syndrome with a risk increase of about 60% per 1 relative with early life events (OR = 1.60, 95% CI: 1.30-1.97). Smoking, active and passive, are predictive for the indicators of severity of asthma symptoms. On average the risk increase of about 10% (OR = 1.10, 95%CI: 1.01-1.20) either per 1 source point of environmental tobacco smoke (ETS) or per 1 packet a day per 10 years. While, the risk of the indicators of both severity of asthma symptoms (OR = 1.59, 95%CI: 1.23-2.06) and severity of respiratory function (OR = 1.37, 95%CI: 1.03-1.82) increase in women compared to men, the risk of the indicators of severity of diagnosed asthma (OR = 0.57, 95%CI: 0.35-0.91) decreases. CONCLUSIONS: Considering latent modelling perspective for formulating plausible hypotheses in asthma research, this study highlighted that the host (genetic) component measured as number of relatives with life-events of asthma and/or allergies seems to be the primary determinants of overall observed asthma indicators summarised by hidden two-class of asthma syndrome. Furthermore, a secondary (or trigger) role of smoking on the continuous latent variable of severity of asthma symptoms, and a gender reversal effect were suggested.

Adult↗
PubMed2001

Linking population-level risk assessment with landscape and habitat models.

Ecological risk assessment at the population level often involves predicting the effects of a particular change in the land-use patterns on the viability of native species. A common method of addressing such questions is modeling the metapopulation dynamics of the species in the landscape. However, the landscape and, as a result, the spatial structure of the metapopulation usually do not remain unchanged, thus the assessment of viability must incorporate the dynamic nature of the landscape. A new link being developed between a metapopulation modeling program (RAMAS) and a landscape dynamics program (LANDIS) will allow the transitional dynamics of the landscape to be incorporated into assessment of viability and threat. This approach combines methods of landscape prediction with those of metapopulation simulation. The link between the landscape model and metapopulation model is provided by statistical models of habitat suitability for the species in focus.

Animals↗
PubMedDate not supplied

[Comparison of two methods in the analysis of the short-term effect of air pollution on health].

OBJECTIVE: We compare two methods to analyse the relationship between air pollution and health. One of them (ME) is based on a generalized linear model, while the other one (MN) incorporates a generalized additive model (GAM). Besides the statistical model used, both methods schow additional discrepancies for the type and number of variables used in the control of confounding. METHOD: An analysis was carried out for each lag of black smoke (0 to 5) and several health indicators from the city of Valencia (Spain) following both methods. Results were compared examining the sequence and the lag of the higher coefficient. The ratio between confidence intervals width and the percent difference in the estimates were also obtained. RESULTS: The discrepancies in results according to the methodology were small. The sequence shaped and the lag of higher coefficient generally remained invariant. Coefficients were similar (percent difference of MN over ME was not greater than 10%). Confidence intervals were more precise for MN, although the reduction was not very strong (the width ratio was around 90%). CONCLUSIONS: The short variation of the results guarantees the applicability of both MN and ME methods, although the systematic reduction of the standard error of MN supports its use as a more efficient procedure to control for confounding.

Air Pollution↗
PubMed2000

Use of the K-distribution for classification of breast masses.

The K-distribution had been introduced as a valid model to represent the statistics of the envelope of the backscattered echo from phantom and tissue. This paper investigates the efficacy of the parameters of this statistical model; namely, the effective number and the effective cross-section, to characterize breast lesions as benign or malignant. Based on the normalized values of the effective number and the effective scattering cross-section, images containing benign and malignant masses were classified for a data set from 52 patients having breast masses/lesions. The receiver operating characteristic (ROC) curves were then obtained to test the classification based on these two parameters. The results indicate that the parameters of the K-distribution may be useful in classification of the breast lesions as benign and malignant.

Breast Neoplasms↗
PubMed1979

Topography of a flexible ribonucleoprotein helix: protein-protein contacts in Sendai virus nucleocapsids.

Contacts among the three polypeptide species in the flexible helical nucleocapsids of a paramyxovirus were examined with bifunctional protein cross-linking reagents. Polypeptides L and P, minor components of Sendai virus nucleocapsids implicated in viral RNA polymerase activity, were efficiently cross-linked into large complexes, indicating that they enjoy abundant contacts with neighboring protein molecules in the helix. Less reactivity was found in the case of the major structural polypeptide, NP; about half of all molecules of NP formed large cross-linked complexes, most of the rest remaining as monomers along with a small proportion of homodimers and low-order oligomers. Marked heterogeneity in the cross-linking reactivity of NP molecules, which may reflect the conformational quasi-equivalence inherent in a flexible helix, was indicated by the production of several conformers of homodimers and other low-order oligomers of NP, and by failure of the kinetics of NP cross-linking to conform to a simple statistical model of random polmerization. The validity of the statistical model was shown by cross-linking experiments with the rigid helical virus, tobacco mosaic virus.

Capsid↗
PubMed2005

Lipid adjustment in the analysis of environmental contaminants and human health risks.

The literature on exposure to lipophilic agents such as polychlorinated biphenyls (PCBs) is conflicting, posing challenges for the interpretation of potential human health risks. Laboratory variation in quantifying PCBs may account for some of the conflicting study results. For example, for quantification purposes, blood is often used as a proxy for adipose tissue, which makes it necessary to model serum lipids when assessing health risks of PCBs. Using a simulation study, we evaluated four statistical models (unadjusted, standardized, adjusted, and two-stage) for the analysis of PCB exposure, serum lipids, and health outcome risk (breast cancer). We applied eight candidate true causal scenarios, depicted by directed acyclic graphs, to illustrate the ramifications of misspecification of underlying assumptions when interpreting results. Statistical models that deviated from underlying causal assumptions generated biased results. Lipid standardization, or the division of serum concentrations by serum lipids, was observed to be highly prone to bias. We conclude that investigators must consider biology, biologic medium (e.g., nonfasting blood samples), laboratory measurement, and other underlying modeling assumptions when devising a statistical plan for assessing health outcomes in relation to environmental exposures.

Bias↗
PubMed1997

Statistical approach to segmentation of single-channel cerebral MR images.

A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities.

Adolescent↗
PubMed1991

Statistical issues in the study of temporal data: daily experiences.

This article reviews statistical issues that arise in temporal data, particularly with respect to daily experience data. Issues related to nonindependence of observations, the nature of data structures, and claims of causality are considered. Through the analysis of data from a single subject, we illustrate concomitant time-series analysis, a general method of examining relationships between two or more series having 50 or more observations. We also discuss detection of and remedies for the problems of trend, cycles, and serial dependency that frequently plague temporal data, and present methods of combining the results of concomitant time series across subjects. Issues that arise in pooling cross-sectional and time-series data and statistical models for addressing these issues are considered for the case in which there are appreciably fewer than 50 observations and a moderate number of subjects. We discuss the possibility of using structural equation modeling to analyze data structures in which there are a large number (e.g., 200) of subjects, but relatively few time points, emphasizing the different causal status of synchronous and lagged effects and the types of models that can be specified for longitudinal data structures. Our conclusion highlights some of the issues raised by temporal data for statistical models, notably the important roles of substantive theory, the question being addressed, the properties of the data, and the assumptions underlying each technique in determining the optimal approach to statistical analysis.

Adaptation, Psychological↗
PubMed2003

Modeling variability in cortical representations of human complex sound perception.

This study investigated methodological (task, stimulus) and intersubject variability in the cortical representation of auditory processing of complex sounds, including speech. Subjects were adult seizure patients undergoing left hemisphere electrocortical mapping (ECM). We tested auditory discrimination of complex sounds, including frequency-modulated tones and speech syllables (digitized, synthesized) contrasted by phonetic features and lexical status. To measure task effects, auditory comprehension was also tested. Within- and across-patient differences in the distribution of deficits induced by ECM were modeled statistically using the recently developed method of Template Mixture Modeling. Cortical representations of auditory discrimination were smaller, more localized, and less variable across subjects than auditory comprehension. Stimulus effects were observed only for speech-tone contrasts. When tasks and stimuli were held constant, two auditory discrimination centers were identified in the posterior temporal lobe. There was also an interaction between task and intersubject effects, with more intersubject variability in cortical maps of auditory comprehension than auditory discrimination. These results demonstrate the utility of using the statistical modeling approach of Template Mixture Modeling to quantify sources of variability in cortical functional organization.

Acoustic Stimulation↗
PubMed2006

Genetic parameters for calving rate and calf survival from linear, threshold, and logistic models in a multibreed beef cattle population.

Generalized mixed linear, threshold, and logistic sire models and Markov chain, Monte Carlo simulation procedures were used to estimate genetic parameters for calving rate and calf survival in a multibreed beef cattle population. Data were obtained from a 5-generation rotational crossbreeding study involving Angus, Brahman, Charolais, and Hereford (1969 to 1995). Gelbvieh and Simmental bulls sired terminal-cross calves from a sample of generation 5 cows. A total of 1,458 cows sired by 158 bulls had a mean calving rate of 78% based on 4,808 calving records. Ninety-one percent of 5,015 calves sired by 260 bulls survived to weaning. Mean heritability estimates and standard deviations for daughter calving rate from posterior distributions were 0.063 +/- 0.024, 0.150 +/- 0.049, and 0.130 +/- 0.047 for linear, threshold, and logistic models, respectively. For calf survival, mean heritability estimates and standard deviations from posterior distributions were 0.049 +/- 0.022, 0.160 +/- 0.058, and 0.190 +/- 0.078 from linear, threshold, and logistic models, respectively. When transformed to an underlying normal scale, linear sire, mixed model, heritability estimates were similar to threshold and logistic sire mixed model estimates. Posterior density distributions of estimated heritabilities from all models were normal. Spearman rank correlations between sire EPD across statistical models were greater than 0.97 for daughter calving rate and for calf survival. Sire EPD had similar ranges across statistical models for daughter calving rate and for calf survival.

Animals↗
PubMedDate not supplied

A two-level cross-sectional model using grafted polynomials.

A new statistical model is proposed for the analysis of hierarchically structured cross-sectional growth data, especially for where measurements are made over long age ranges. The model combines a two-level model with grafted piecewise polynomials, to make efficient use of available data.

Child↗