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At least 1,153 records · Page 64Linked to original sources

PubMed2005

Nursing diagnoses, diagnosis-related group, and hospital outcomes.

BACKGROUND AND OBJECTIVE: There are no nursing centric data in the hospital discharge abstract. This study investigates whether adding nursing data in the form of nursing diagnoses to medical diagnostic data in the discharge abstract can improve overall explanation of variance in commonly studied hospital outcomes. METHOD: A retrospective analyses of 123,241 sequential patient admissions to a university hospital in a Midwestern city was performed. Two data sets were combined: (1) a daily collection of patient assessments by nurses using nursing diagnosis terminology (NDX); and (2) the summary discharge information from the hospital discharge abstract including diagnosis-related group (DRG) and all payer refined DRG (APR-DRG). Each of 61 daily NDX observations were collapsed as frequency of occurrence for the hospital stay and inserted into the discharge abstract. NDX was then compared to both DRG and APR-DRG across 5 hospital outcome variables using multivariate regression or logistic regression. RESULTS AND CONCLUSIONS: In all statistical models, DRG, APR-DRG, and NDX were significantly associated with the 5 hospital outcome variables (P <.0001). When NDX was added to models containing either the DRG or the APR-DRG, explanatory power (R2) and model discrimination (c statistic) improved by 30% to 146% across the outcome variables of hospital length of stay, ICU length of stay, total charges, probably of death, and discharge to a nursing home (P <.0001). The findings support the contention that nursing care is an independent predictor of patient hospital outcomes. These nursing data are not redundant with the medical diagnosis, in particular, the DRG. The findings support the argument for including nursing care data in the hospital discharge abstract. Further study is needed to clarify which nursing data are the best fit for the current hospital discharge abstract data collection scheme.

Diagnosis-Related Groups↗

PubMed1977

[Statistical aspects in planning psychotropic drug trials (author's transl)].

It has become virtually unthinkable to objectively judge trials of comparisons without statistical inference. For studies involving psychopharmaceuticals the requirements have especially grown, since variations of efficacy are difficult to differentiate and objectivate. The basic principles of statistical planning must be applied to studies with psychopharmaceuticals. On the basis of these general principles the relevant points of the clinical and statistical models and their relationship to one another are discussed. Special aspects of the study objectives, the clinical model, the medical trial plan, the statistical model and design, the execution of the trials, statistical evaluation and its interpretation are presented. The setting up of hypotheses, balancing and maintenance of factors of disturbances, sample sizes, randomizing techniques and the confrontation of clinical relevance with statistical significance are some of the important points discussed.

Clinical Trials as Topic↗

PubMed1999

Some state space models of HIV pathogenesis under treatment by anti-viral drugs in HIV-infected individuals.

In this paper we have extended the model of HIV pathogenesis under treatment by anti-viral drugs given by Perelson et al. [A.S. Perelson et al., Science 271 (1999) 1582] to a stochastic model. By using this stochastic model as the stochastic system model, we have developed a state space model for the HIV pathogenesis under treatment by anti-viral drugs. In this state space model, the observation model is a statistical model based on the observed numbers of RNA virus copies over different times. For this model we have developed procedures for estimating and predicting the numbers of infectious free HIV and non-infectious free HIV as well as the numbers of different types of T cells through extended Kalman filter method. As an illustration, we have applied the method of this paper to the data of patient Nos. 104, 105 and 107 given by Perelson et al. [A.S. Perelson et al., Science 271 (1999) 1582] under treatment by Ritonavir. For these individuals, it is shown that within two weeks since treatment, most of the free HIV are non-infectious, indicating the usefulness of the treatment. Furthermore, the Kalman filter method revealed a much stronger effect of the treatment within the first 10 to 20 h than that predicted by the deterministic model.

CD4 Lymphocyte Count↗

PubMed1990

Risk assessment in urgent/emergent coronary artery surgery.

A statistical model has been developed to allow for prediction of individual patient prognosis following urgent/emergent coronary artery bypass grafting (CABG). None of the models previously described for use in coronary artery surgery has been tested with a prospective patient series that confirms the true predictive capacity of the model. Ideally, the predictive ability of such models should be validated with prospective trials. To examine the feasibility of statistical modeling in this clinical context, a computerized model based on the theorem of Bayes was developed to predict operative mortality for urgent coronary artery surgery. The presence or absence of 20 risk factors was determined for each of 405 consecutive patients undergoing urgent coronary artery surgery from January 1984 to January 1989. The first 100 patients were used to develop a database for the model, which was then used to prospectively evaluate the remaining 305 patients. There was good agreement between predicted and observed results. Models of this kind are particularly advantageous because of the ability to (1) accommodate multiple risk factors, (2) become tailored to a specific practice, and (3) determine individual rather than group prognosis. Validation with a prospective trial confirms the practical utility of this approach. This model has reliably predicted the risk associated with urgent coronary artery surgery and may provide important clinical information for the management of patients being evaluated for urgent revascularization.

Algorithms↗

PubMed2002

Cognitive modeling with spreadsheets.

Spreadsheet implementations of two different types of cognitive models--a neural network model and a statistical model--are presented. The two examples illustrate how to employ the facilities of spreadsheets, the spreadsheet data structure, array functions, the built-in function library, and the integrated optimizer, for building cognitive models. The two presented models are new extensions of existing models. They are used for simulating data from experiments illustrating that the extended versions are able to explain experimental results that could not be simulated by the original models. The whole simulation study demonstrates that spreadsheets are a handy tool, especially for researchers without programming knowledge who want to build cognitive models and for instructors teaching cognitive modeling.

Algorithms↗

PubMed1991

Prognosis in primary biliary cirrhosis. A review.

Recent progress has been made in estimating prognosis in primary biliary cirrhosis using Cox models. These models have also demonstrated the therapeutic value of liver transplantation by comparing the observed survival for a group after transplantation with the expected survival without transplantation calculated from the Cox prognostic model. However, good risk patients and those not transplanted principally for hepatocellular failure may not have a survival advantage for many years. Cox models have several limitations: the selection criteria for the patient populations used to derive the models, the selection of the time at which the patients are evaluated, the poor prognostic accuracy for individual patients rather than patient groups and lastly the fact that they use variables derived at only one time point-time independent Cox models. Thus new statistical tools must be used to improve prediction of survival in individual patients with PBC in order to optimize timing of liver transplantation. In addition a more precise definition of the natural history of both symptomatic and asymptomatic forms of this disease is needed to evaluate the efficacy of therapeutic agents in randomized clinical trials. However, although use and timing of therapeutic intervention, including liver transplantation, still requires good clinical experience and judgement, statistical modelling does give some objective measurement of prognosis, which is useful for the clinician treating patients with PBC. At the same time that new treatments are being evaluated, there is an obvious need to improve prognostic tools for application to individual patients with PBC. This may be achieved by using serial data in a different form of modelling-time dependent Cox models.

Humans↗

PubMed2005

Statistical mechanics model for the emergence of consensus.

The statistical properties of pairwise majority voting over S alternatives are analyzed in an infinite random population. We first compute the probability that the majority is transitive (i.e., that if it prefers A to B to C , then it prefers A to C) and then study the case of an interacting population. This is described by a constrained multicomponent random field Ising model whose ferromagnetic phase describes the emergence of a strong transitive majority. We derive the phase diagram, which is characterized by a tricritical point and show that, contrary to intuition, it may be more likely for an interacting population to reach consensus on a number S of alternatives when S increases. This effect is due to the constraint imposed by transitivity on voting behavior. Indeed if agents are allowed to express nontransitive votes, the agents' interaction may decrease considerably the probability of a transitive majority.

Journal Article↗

PubMed2002

Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain.

A method for automated segmentation of major cortical sulci on the outer brain boundary is presented, with emphasis on automatically determining point correspondence and on labeling cortical regions. The method is formulated in a general optimization framework defined on the unit sphere, which serves as parametric domain for convoluted surfaces of spherical topology. A statistical shape model, which includes a network of deformable curves on the unit sphere, seeks geometric features such as high curvature regions and labels such features via a deformation process that is confined within a spherical map of the outer brain boundary. The limitations of the customary spherical coordinate system, which include discontinuities at the poles and nonuniform sampling, are overcome by defining the statistical prior of shape variation in terms of projections of landmark points onto corresponding tangent planes of the sphere. The method is tested against and shown to be as accurate as manually defined segmentations.

Algorithms↗

PubMed1994

Introduction to the special section: structural equation modeling in clinical research.

Clinical research hypotheses are becoming increasingly more complex and specific. As a result, clinical research studies often include multiple independent, intervening, and dependent variables in a single study. Nevertheless, a sampling of studies from 3 decades of research published in the Journal of Consulting and Clinical Psychology reveals that the statistical models adopted by clinical researchers have failed to keep pace with the increasing complexity and specificity of hypotheses and research designs. This article introduces a special section on structural equation modeling, a statistical model well suited for complex and specific hypothesis tests in clinical research studies that include many variables.

Humans↗

PubMed2006

Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

There has been considerable research conducted on the development of statistical models for predicting crashes on highway facilities. Despite numerous advancements made for improving the estimation tools of statistical models, the most common probabilistic structure used for modeling motor vehicle crashes remains the traditional Poisson and Poisson-gamma (or Negative Binomial) distribution; when crash data exhibit over-dispersion, the Poisson-gamma model is usually the model of choice most favored by transportation safety modelers. Crash data collected for safety studies often have the unusual attributes of being characterized by low sample mean values. Studies have shown that the goodness-of-fit of statistical models produced from such datasets can be significantly affected. This issue has been defined as the "low mean problem" (LMP). Despite recent developments on methods to circumvent the LMP and test the goodness-of-fit of models developed using such datasets, no work has so far examined how the LMP affects the fixed dispersion parameter of Poisson-gamma models used for modeling motor vehicle crashes. The dispersion parameter plays an important role in many types of safety studies and should, therefore, be reliably estimated. The primary objective of this research project was to verify whether the LMP affects the estimation of the dispersion parameter and, if it is, to determine the magnitude of the problem. The secondary objective consisted of determining the effects of an unreliably estimated dispersion parameter on common analyses performed in highway safety studies. To accomplish the objectives of the study, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size. Three estimators commonly used by transportation safety modelers for estimating the dispersion parameter of Poisson-gamma models were evaluated: the method of moments, the weighted regression, and the maximum likelihood method. In an attempt to complement the outcome of the simulation study, Poisson-gamma models were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size. The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process. The probability the dispersion parameter becomes unreliably estimated increases significantly as the sample mean and sample size decrease. Consequently, the results show that an unreliably estimated dispersion parameter can significantly undermine empirical Bayes (EB) estimates as well as the estimation of confidence intervals for the gamma mean and predicted response. The paper ends with recommendations about minimizing the likelihood of producing Poisson-gamma models with an unreliable dispersion parameter for modeling motor vehicle crashes.

Accidents, Traffic↗

PubMedDate not supplied

Development of a vibratory white finger prevention program for shipyard workers: an exploratory study.

BACKGROUND: Vibratory White Finger (VWF) disease afflicts many shipyard workers who use vibrating tools over a period of years and can lead to a lifelong disability. Vascular symptoms include cold-induced spasms of the fingers and hands, blanching of the fingers, and atrophic skin changes in the fingertips. Sensorineural symptoms include numbness and tingling in the fingers and hands and reduced sensory perception and manipulative dexterity. The purpose of this study was to develop a screening program for shipyard workers, so that VWF disease could be identified and workers with the disease could be counseled on possible job changes involving reduced exposure to vibration. METHOD: This study was conducted during 1993. The subjects consisted of 39 users of vibrating tools and 10 nonusers. In the shipyard, each subject received a questionnaire dealing with VWF symptoms, followed by tests for multifrequency vibration thresholds, cutaneous sensitivity, and hand and finger strength. Each subject was subsequently examined outside the shipyard by a board-certified occupational health physician. RESULTS: Using discriminant analysis, statistical models for both the vascular and sensorineural stages of the disease were constructed. In general, there was a high level of agreement between the staging obtained from the statistical models and the outside physician's diagnoses, suggesting that the combination of questionnaire and test data was adequate for initial screening for this disease. CONCLUSION: It is not considered practical to perform a complete physician evaluation on every shipyard worker who is at risk for VWF. By using a structured questionnaire together with a limited number of VWF-related tests, and incorporating the data into a statistical model, it is possible to identify the priority candidates for physician follow-up and possibly alter the worksite to prevent serious disability.

Adult↗

PubMed2006

Predicting functional capacity outcome 12 months after hospitalized injury.

BACKGROUND: There is a recognized need to move from mortality to morbidity outcome predictions following traumatic injury. However, there are few morbidity outcome prediction scoring methods and these fail to incorporate important comorbidities or cofactors. This study aims to develop and evaluate a method that includes such variables. METHODS: This was a consecutive case series registered in the Queensland Trauma Registry that consented to a prospective 12-month telephone conducted follow-up study. A multivariable statistical model was developed relating Trauma Registry data to trichotomized 12-month post-injury outcome (categories: no limitations, minor limitations and major limitations). Cross-validation techniques using successive single hold-out samples were then conducted to evaluate the model's predictive capabilities. RESULTS: In total, 619 participated, with 337 (54%) experiencing no limitations, 101 (16%) experiencing minor limitations and 181 (29%) experiencing major limitations 12 months after injury. The final parsimonious multivariable statistical model included whether the injury was in the lower extremity body region, injury severity, age, length of hospital stay, pulse at admission and whether the participant was admitted to an intensive care unit. This model explained 21% of the variability in post-injury outcome. Predictively, 64% of those with no limitations, 18% of those with minor limitations and 37% of those with major limitations were correctly identified. CONCLUSION: Although carefully developed, this statistical model lacks the predictive power necessary for its use as a basis of a useful prognostic tool. Further research is required to identify variables other than those routinely used in the Trauma Registry to develop a model with the necessary predictive utility.

Adolescent↗

PubMed2004

Spot shape modelling and data transformations for microarrays.

MOTIVATION: To study lowly expressed genes in microarray experiments, it is useful to increase the photometric gain in the scanning. However, a large gain may cause some pixels for highly expressed genes to become saturated. Spatial statistical models that model spot shapes on the pixel level may be used to infer information about the saturated pixel intensities. Other possible applications for spot shape models include data quality control and accurate determination of spot centres and spot diameters. RESULTS: Spatial statistical models for spotted microarrays are studied including pixel level transformations and spot shape models. The models are applied to a dataset from 50mer oligonucleotide microarrays with 452 selected Arabidopsis genes. Logarithmic, Box-Cox and inverse hyperbolic sine transformations are compared in combination with four spot shape models: a cylindric plateau shape, an isotropic Gaussian distribution and a difference of two-scaled Gaussian distribution suggested in the literature, as well as a proposed new polynomial-hyperbolic spot shape model. A substantial improvement is obtained for the dataset studied by the polynomial-hyperbolic spot shape model in combination with the Box-Cox transformation. The spatial statistical models are used to correct spot measurements with saturation by extrapolating the censored data. AVAILABILITY: Source code for R is available at http://www.matfys.kvl.dk/~ekstrom/spotshapes/

Algorithms↗

PubMed1997

Analysis of dynamic radioligand displacement or "activation" studies.

We present a simple way of assessing dynamic or time-dependent changes in displacement during single-subject radioligand positron emission tomography (PET) activation studies. The approach is designed to facilitate dynamic activation studies using selective radioligands. These studies are, in principle, capable of characterising functional neurochemistry by analogy with the study of functional neuroanatomy using rCBF activation studies. The proposed approach combines time-dependent compartmental models of tracer kinetics and the general linear model used in statistical parametric mapping. This provides for a comprehensive, voxel-based and data-led assessment of regionally specific effects. The statistical model proposed in this paper is predicated on a single-compartment model extended to allow for time-dependent changes in kinetics. We have addressed the sensitivity and specificity of the analysis, as it would be used operationally, by applying the analysis to 11C-Flumazenil dynamic displacement studies. The activation used in this demonstration study was a pharmacological (i.v. midazolam) challenge, 30 min after administration of the tracer. We were able to demonstrate, and make statistical inferences about, regional increases in k2 (or decreases in the volume of distribution) in prefrontal and other cortical areas.

Activation Analysis↗

PubMed2005

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Adult↗

PubMed2000

Exponential family models and statistical genetics.

This article describes the evolution of applied exponential family models, starting at 1972, the year of publication of the seminal papers on generalized linear models and on Cox regression, and leading to multivariate (i) marginal models and inference based on estimating equations and (ii) random effects models and Bayesian simulation-based posterior inference. By referring to recent work in genetic epidemiology, on semiparametric methods for linkage analysis and on transmission/disequilibrium tests for haplotype transmission this paper illustrates the potential for the recent advances in applied probability and statistics to contribute to new and unified tools for statistical genetics. Finally, it is emphasized that there is a need for well-defined postgraduate education paths in medical statistics in the year 2000 and thereafter.

Biometry↗

PubMed2006

GALGO: an R package for multivariate variable selection using genetic algorithms.

SUMMARY: The development of statistical models linking the molecular state of a cell to its physiology is one of the most important tasks in the analysis of Functional Genomics data. Because of the large number of variables measured a comprehensive evaluation of variable subsets cannot be performed with available computational resources. It follows that an efficient variable selection strategy is required. However, although software packages for performing univariate variable selection are available, a comprehensive software environment to develop and evaluate multivariate statistical models using a multivariate variable selection strategy is still needed. In order to address this issue, we developed GALGO, an R package based on a genetic algorithm variable selection strategy, primarily designed to develop statistical models from large-scale datasets.

Algorithms↗

PubMed2004

Comparison of Bayesian, classical, and heuristic approaches in identifying acute disease events in lung transplant recipients.

This study compares a typical heuristic algorithm with classical and Bayesian regression models in ascertaining the presence of acute bronchopulmonary disease events in lung transplant recipients. These models attempt to predict whether an epoch will end in an event, based on the preceding two weeks of data. The data consist of 150 two-week epochs of daily to biweekly spirometry and symptom covariates for 30 subjects over 60 subject-years. Seventy-five 'event' epochs end on a day when an acute bronchopulmonary disease event is documented in the medical record; 75 randomly selected 'non-event' epochs end on a day when no event is documented. The data are partitioned by randomly assigning 15 subjects for training and the remaining 15 subjects for testing. For cross-validation, a second random partition is generated from the same data set. The statistical models are trained and tested on both partitions. For the heuristic algorithm, its historical event classifications on the same test cases are used. Classification performance on both partitions of all models is compared using receiver operating characteristic curves, sensitivity and specificity, and a Shannon information score. Data partition did not appreciably affect statistical model performance. All statistical models, unlike the heuristic algorithm, performed significantly different than chance (family significance < 0.05, Pearson independence chi-square, Bonferroni multiple correction), and better than the heuristic algorithm. The best models were Bayesian changepoint models. Through a clinically oriented discussion, a case classified by all of these algorithms is presented, suggesting the clinical usefulness of the Bayesian approach compared with the classical and heuristic approaches.

Acute Disease↗