917 resultados para Gaussian prior variance
Resumo:
OBJECTIVES: This paper examines four different levels of possible variation in symptom reporting: occasion, day, person and family. DESIGN: In order to rule out effects of retrospection, concurrent symptom reporting was assessed prospectively using a computer-assisted self-report method. METHODS: A decomposition of variance in symptom reporting was conducted using diary data from families with adolescent children. We used palmtop computers to assess concurrent somatic complaints from parents and children six times a day for seven consecutive days. In two separate studies, 314 and 254 participants from 96 and 77 families, respectively, participated. A generalized multilevel linear models approach was used to analyze the data. Symptom reports were modelled using a logistic response function, and random effects were allowed at the family, person and day level, with extra-binomial variation allowed for on the occasion level. RESULTS: Substantial variability was observed at the person, day and occasion level but not at the family level. CONCLUSIONS: To explain symptom reporting in normally healthy individuals, situational as well as person characteristics should be taken into account. Family characteristics, however, would not help to clarify symptom reporting in all family members.
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Generalized linear mixed models (GLMM) are generalized linear models with normally distributed random effects in the linear predictor. Penalized quasi-likelihood (PQL), an approximate method of inference in GLMMs, involves repeated fitting of linear mixed models with “working” dependent variables and iterative weights that depend on parameter estimates from the previous cycle of iteration. The generality of PQL, and its implementation in commercially available software, has encouraged the application of GLMMs in many scientific fields. Caution is needed, however, since PQL may sometimes yield badly biased estimates of variance components, especially with binary outcomes. Recent developments in numerical integration, including adaptive Gaussian quadrature, higher order Laplace expansions, stochastic integration and Markov chain Monte Carlo (MCMC) algorithms, provide attractive alternatives to PQL for approximate likelihood inference in GLMMs. Analyses of some well known datasets, and simulations based on these analyses, suggest that PQL still performs remarkably well in comparison with more elaborate procedures in many practical situations. Adaptive Gaussian quadrature is a viable alternative for nested designs where the numerical integration is limited to a small number of dimensions. Higher order Laplace approximations hold the promise of accurate inference more generally. MCMC is likely the method of choice for the most complex problems that involve high dimensional integrals.
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In Malani and Neilsen (1992) we have proposed alternative estimates of survival function (for time to disease) using a simple marker that describes time to some intermediate stage in a disease process. In this paper we derive the asymptotic variance of one such proposed estimator using two different methods and compare terms of order 1/n when there is no censoring. In the absence of censoring the asymptotic variance obtained using the Greenwood type approach converges to exact variance up to terms involving 1/n. But the asymptotic variance obtained using the theory of the counting process and results from Voelkel and Crowley (1984) on semi-Markov processes has a different term of order 1/n. It is not clear to us at this point why the variance formulae using the latter approach give different results.
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We propose a novel class of models for functional data exhibiting skewness or other shape characteristics that vary with spatial or temporal location. We use copulas so that the marginal distributions and the dependence structure can be modeled independently. Dependence is modeled with a Gaussian or t-copula, so that there is an underlying latent Gaussian process. We model the marginal distributions using the skew t family. The mean, variance, and shape parameters are modeled nonparametrically as functions of location. A computationally tractable inferential framework for estimating heterogeneous asymmetric or heavy-tailed marginal distributions is introduced. This framework provides a new set of tools for increasingly complex data collected in medical and public health studies. Our methods were motivated by and are illustrated with a state-of-the-art study of neuronal tracts in multiple sclerosis patients and healthy controls. Using the tools we have developed, we were able to find those locations along the tract most affected by the disease. However, our methods are general and highly relevant to many functional data sets. In addition to the application to one-dimensional tract profiles illustrated here, higher-dimensional extensions of the methodology could have direct applications to other biological data including functional and structural MRI.
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Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which is commonly used to quantify changes in blood oxygenation and flow coupled to neuronal activation. One of the primary goals of fMRI studies is to identify localized brain regions where neuronal activation levels vary between groups. Single voxel t-tests have been commonly used to determine whether activation related to the protocol differs across groups. Due to the generally limited number of subjects within each study, accurate estimation of variance at each voxel is difficult. Thus, combining information across voxels in the statistical analysis of fMRI data is desirable in order to improve efficiency. Here we construct a hierarchical model and apply an Empirical Bayes framework on the analysis of group fMRI data, employing techniques used in high throughput genomic studies. The key idea is to shrink residual variances by combining information across voxels, and subsequently to construct an improved test statistic in lieu of the classical t-statistic. This hierarchical model results in a shrinkage of voxel-wise residual sample variances towards a common value. The shrunken estimator for voxelspecific variance components on the group analyses outperforms the classical residual error estimator in terms of mean squared error. Moreover, the shrunken test-statistic decreases false positive rate when testing differences in brain contrast maps across a wide range of simulation studies. This methodology was also applied to experimental data regarding a cognitive activation task.
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BACKGROUND: Noninvasive intraocular pressure (IOP) measurement in mice is critically important for understanding the pathophysiology of glaucoma. Rebound tonometry is one of the methods that can be used for obtaining such measurements. We evaluated the ability of the rebound tonometer (RT) to determine IOP differences among various mouse strains and whether differences in corneal thickness may affect IOP measurements in these animals. MATERIALS AND METHODS: Five different commonly used mouse strains (BALB/C, CBA/CAHN, AKR/J, CBA/J, and 129P3/J) were used. IOP was measured in eyes from 12 nonsedated animals (6 male and 6 female) from each strain at 2 to 3 months of age using the RT. IOPs were measured in all animals, on 2 different days between 10 AM and 12 PM. Subsequently, a number of eyes from each strain were cannulated to provide a calibration curve specific for that strain. Tonometer readings for all strains were converted to apparent IOP values using the calibration data obtained from the calibration curve of the respective strain. For comparison purposes, IOP values were also obtained using the C57BL/6 calibration data previously reported. IOP for the 5 strains, male and female animals, and the different occasion of measurement were compared using repeat measures analysis of variance. The central corneal thickness (CCT) of another group of 8 male animals from each of the 5 strains was also measured using an optical low coherence reflectometry (OLCR) pachymeter modified for use with mice. CCT values were correlated to mean IOPs of male animals and to the slopes and intercept of individual strain calibration curves. RESULTS: Noninvasive IOP measurements confirm that the BALB/C strain has lower and the CBA/CAHN has higher relative IOPs than other mouse strains while the AKR/J, the CBA/J, and the 129P3/J strains have intermediate IOPs. There is a very good correlation of apparent IOP values obtained by RT with previously reported true IOPs obtained by cannulation. There was a small but statistically significant difference in IOP between male and female animals in 2 strains (129P3/J and AKR/J) with female mice having higher relative IOPs. No correlation between CCT and IOP was detected. CCT did not correlate with any of the constants describing the calibration curves in the various strains. CONCLUSIONS: Noninvasive IOP measurement in mice using the RT can be used to help elucidate IOP phenotype, after prior calibration of the tonometer. CCT has no effect on mouse IOP measurements using the RT.
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Radiation dose delivered from the SCANORA radiography unit during the cross-sectional mode for dentotangential projections was determined. With regard to oral implantology, patient situations of an edentulous maxilla and mandible as well as a single tooth gap in regions 16 and 46 were simulated. Radiation doses were measured between 0.2 and 22.5 mGy to organs and tissues in the head and neck region when the complete maxilla or mandible was examined. When examining a single tooth gap, only 8% to 40% of that radiation dose was generally observed. Based on these results, the mortality risk was estimated according to a calculation model recommended by the Committee on the Biological Effects of Ionizing Radiations. The mortality risk ranged from 31.4 x 10(-6) for 20-year-old men to 4.8 x 10(-6) for 65-year-old women when cross-sectional imaging of the complete maxilla was performed. The values decreased by 70% when a single tooth gap in the molar region of the maxilla was radiographed. The figures for the mortality risk for examinations of the complete mandible were similar to those for the complete maxilla, but the mortality risk decreased by 80% if only a single tooth gap in the molar region of the mandible was examined. Calculations according to the International Commission on Radiological Protection carried out for comparison did not reveal the decrease of the mortality risk with age and resulted in a higher risk value in comparison to the group of 35-year old individuals in calculations according to the Committee on the Biological Effects of Ionizing Radiations.
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PURPOSE: The clinical role of CAD systems to detect breast cancer, which have not been on cancer containing mammograms not detected by the radiologist was proven retrospectively. METHODS: All patients from 1992 to 2005 with a histologically verified malignant breast lesion and a mammogram at our department, were analyzed in retrospect focussing on the time of detection of the malignant lesion. All prior mammograms were analyzed by CAD (CADx, USA). The resulting CAD printout was matched with the cancer containing images yielding to the radiological diagnosis of breast cancer. CAD performance, sensitivity as well as the association of CAD and radiological features were analyzed. RESULTS: 278 mammograms fulfilled the inclusion criteria. 111 cases showed a retrospectively visible lesion (71 masses, 23 single microcalcification clusters, 16 masses with microcalcifications, in one case two microcalcification clusters). 54/87 masses and 34/41 microcalcifications were detected by CAD. Detection rates varied from 9/20 (ACR 1) to 5/7 (ACR 4) (45% vs. 71%). The detection of microcalcifications was not influenced by breast tissue density. CONCLUSION: CAD might be useful in an earlier detection of subtle breast cancer cases, which might remain otherwise undetected.
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BACKGROUND: We investigated clinical predictors of appropriate prophylaxis prior to the onset of venous thromboembolism (VTE). METHODS: In 14 Swiss hospitals, 567 consecutive patients (306 medical, 261 surgical) with acute VTE and hospitalization < 30 days prior to the VTE event were enrolled. RESULTS: Prophylaxis was used in 329 (58%) patients within 30 days prior to the VTE event. Among the medical patients, 146 (48%) received prophylaxis, and among the surgical patients, 183 (70%) received prophylaxis (P < 0.001). The indication for prophylaxis was present in 262 (86%) medical patients and in 217 (83%) surgical patients. Among the patients with an indication for prophylaxis, 135 (52%) of the medical patients and 165 (76%) of the surgical patients received prophylaxis (P < 0.001). Admission to the intensive care unit [odds ratio (OR) 3.28, 95% confidence interval (CI) 1.94-5.57], recent surgery (OR 2.28, 95% CI 1.51-3.44), bed rest > 3 days (OR 2.12, 95% CI 1.45-3.09), obesity (OR 2.01, 95% CI 1.03-3.90), prior deep vein thrombosis (OR 1.71, 95% CI 1.31-2.24) and prior pulmonary embolism (OR 1.54, 95% CI 1.05-2.26) were independent predictors of prophylaxis. In contrast, cancer (OR 1.06, 95% CI 0.89-1.25), age (OR 0.99, 95% CI 0.98-1.01), acute heart failure (OR 1.13, 95% CI 0.79-1.63) and acute respiratory failure (OR 1.19, 95% CI 0.89-1.59) were not predictive of prophylaxis. CONCLUSIONS: Although an indication for prophylaxis was present in most patients who suffered acute VTE, almost half did not receive any form of prophylaxis. Future efforts should focus on the improvement of prophylaxis for hospitalized patients, particularly in patients with cancer, acute heart or respiratory failure, and in the elderly.