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Results for “Models, Statistical”

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At least 1,171 records · Page 65Linked to original sources

PubMed2001

An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures.

This paper presents a deformable model for automatically segmenting brain structures from volumetric magnetic resonance (MR) images and obtaining point correspondences, using geometric and statistical information in a hierarchical scheme. Geometric information is embedded into the model via a set of affine-invariant attribute vectors, each of which characterizes the geometric structure around a point of the model from a local to a global scale. The attribute vectors, in conjunction with the deformation mechanism of the model, warranty that the model not only deforms to nearby edges, as is customary in most deformable surface models, but also that it determines point correspondences based on geometric similarity at different scales. The proposed model is adaptive in that it initially focuses on the most reliable structures of interest, and gradually shifts focus to other structures as those become closer to their respective targets and, therefore, more reliable. The proposed techniques have been used to segment boundaries of the ventricles, the caudate nucleus, and the lenticular nucleus from volumetric MR images.

Brain↗

PubMed1990

Risk stratification after percutaneous transluminal coronary angioplasty.

Approximately 20-30% of patients who undergo elective percutaneous transluminal coronary angioplasty (PTCA) require a second angioplasty within 12 months. A significant proportion of patients develop clinical cardiac events during the first year following the initial procedure. The present investigation was undertaken to establish a statistical model for predicting such events. The study group consisted of 100 patients who underwent elective PTCA at the University of Alberta Hospital. All patients were prescribed nifedipine (10 mg tid) and aspirin (325 mg daily) in addition to other medications determined by the attending cardiologist. The patients were reviewed 10 weeks after the procedure and again at the end of 1 year. The follow-up was completed on 96 patients. Within the first year, forty-five experienced cardiac events (1 death, 5 myocardial infarctions, 4 bypass surgeries, 22 repeat PTCAs). These events occurred in 29 patients. An additional 16 patients experienced significant anginal symptoms. A statistical model based upon the patients' perception of symptoms immediately after the procedure, history of hypertension, vessel subjected to PTCA, ejection fraction pre-PTCA, and occurrence of intimal dissection during PTCA was used to identify patients likely to develop cardiac events. Overall, the model classified 72% of the patients (with and without events). Such a statistical model could be used to identify patients who should be subjected to an enhanced degree of cardiologic surveillance in a rehabilitation program.

Aged↗

PubMed1992

The relationship between inattentiveness in the classroom and reading achievement (Part A): methodological issues.

Conclusions drawn from recent reviews of research concerned with relationships between child behavior disorders and learning difficulties highlight major methodological and analytical problems that have plagued empirical research in the field. This paper (Part A) identifies and discusses some of these problems, including those associated with the design and use of psychometric instruments, cutoff scores, and statistical modeling techniques. The paper provides a contextual rationale for the choice of methods and statistical models used in a large explanatory study of the relationship between inattentiveness in the classroom and reading achievement, reported in Part B.

Achievement↗

PubMed2006

Estimating spiking irregularities under changing environments.

We considered a gamma distribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape parameter that characterizes spiking irregularities of individual neurons. Because the environment changes over time, observed data are generated from a model with a time-dependent firing rate, which is an unknown function. A statistical model with an unknown function is called a semiparametric model and is generally very difficult to solve. We used a novel method of estimating functions in information geometry to estimate the shape parameter without estimating the unknown function. We obtained an optimal estimating function analytically for the shape parameter independent of the functional form of the firing rate. This estimation is efficient without Fisher information loss and better than maximum likelihood estimation. We suggest a measure of spiking irregularity based on the estimating function, which may be useful for characterizing individual neurons in changing environments.

Action Potentials↗

PubMed1996

The megaprior heuristic for discovering protein sequence patterns.

Several computer algorithms for discovering patterns in groups of protein sequences are in use that are based on fitting the parameters of a statistical model to a group of related sequences. These include hidden Markov model (HMM) algorithms for multiple sequence alignment, and the MEME and Gibbs sampler algorithms for discovering motifs. These algorithms are sometimes prone to producing models that are incorrect because two or more patients have been combined. The statistical model produced in this situation is a convex combination (weighted average) of two or more different models. This paper presents a solution to the problem of convex combinations in the form of a heuristic based on using extremely low variance Dirichlet mixture priors as part of the statistical model. This heuristic, which we call the megaprior heuristic, increase the strength (i.e., decreases the variance) of the prior in proportion to the size of the sequence dataset. This causes each column in the final model to strongly resemble the mean of a single component of the prior, regardless of the size of the dataset. We describe the cause of the convex combination problem, analyze it mathematically, motivate and describe the implementation of the megaprior heuristic, and show how it can effectively eliminate the problem of convex combinations in protein sequence pattern discovery.

Algorithms↗

PubMed2004

A model for random sampling and estimation of relative protein abundance in shotgun proteomics.

Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.

Data Collection↗

PubMed2004

Presentation of multivariate data for clinical use: The Framingham Study risk score functions.

The Framingham Heart Study has been a leader in the development and dissemination of multivariable statistical models to estimate the risk of coronary heart disease. These models quantify the impact of measurable and modifiable risk factors on the development of coronary heart disease and can be used to generate estimates of risk of coronary heart disease over a predetermined period, for example the next 10 years. We developed a system, which we call a points system, for making these complex statistical models useful to practitioners. The system is easy to use, it does not require a calculator or computer and it simplifies the estimation of risk based on complex statistical models. This system represents an effort to make available a tool for clinicians to aid in their decision-making process regarding treatment and to assist them in motivating patients toward healthy behaviours. The system is also readily available to patients who can easily estimate their own coronary heart disease risk and monitor this risk over time.

Adult↗

PubMed2001

Development and validation of the Ontario acute myocardial infarction mortality prediction rules.

OBJECTIVES: To develop and validate simple statistical models that can be used with hospital discharge administrative databases to predict 30-day and one-year mortality after an acute myocardial infarction (AMI). BACKGROUND: There is increasing interest in developing AMI "report cards" using population-based hospital discharge databases. However, there is a lack of simple statistical models that can be used to adjust for regional and interinstitutional differences in patient case-mix. METHODS: We used linked administrative databases on 52,616 patients having an AMI in Ontario, Canada, between 1994 and 1997 to develop logistic regression statistical models to predict 30-day and one-year mortality after an AMI. These models were subsequently validated in two external cohorts of AMI patients derived from administrative datasets from Manitoba, Canada, and California, U.S. RESULTS: The 11-variable Ontario AMI mortality prediction rules accurately predicted mortality with an area under the receiver operating characteristic (ROC) curve of 0.78 for 30-day mortality and 0.79 for one-year mortality in the Ontario dataset from which they were derived. In an independent validation dataset of 4,836 AMI patients from Manitoba, the ROC areas were 0.77 and 0.78, respectively. In a second validation dataset of 112,234 AMI patients from California, the ROC areas were 0.77 and 0.78 respectively. CONCLUSIONS: The Ontario AMI mortality prediction rules predict quite accurately 30-day and one-year mortality after an AMI in linked hospital discharge databases of AMI patients from Ontario, Manitoba and California. These models may also be useful to outcomes and quality measurement researchers in other jurisdictions.

Aged↗

PubMed2004

Quality of life: a longitudinal analysis of correlates of morale in old age.

This paper examines recurrent continuous morale in old age within a statistical modelling paradigm. The Anglicised Philadelphia Geriatric Centre Morale Scale was used as a small component of a major longitudinal study of old age in rural North Wales, U.K. The literature review and cross-sectional analysis of morale in old age is published elsewhere. This paper deals with the aspect of the longitudinal analysis of morale in old age. The proposed statistical modelling relates recurrent morale to a set of explanatory variables that includes subjective as well as objective measures. In order to assess the degree to which explanatory variables influence morale, an adequate statistical model must handle the possibility that substantial variation between respondents will be due to unmeasured and potentially unmeasurable variables (residual heterogeneity), multicollinearity, and past behaviour effect. These applications are illustrated using morale in old age from the North Wales Longitudinal Study Old Age. The results suggested a strong presence of heterogeneity effect, i.e., current levels of morale appear to be individual specific and independent of its previous levels.

Aged↗

PubMedDate not supplied

Analysis of patient variables affecting neurologic outcome after traumatic cervical facet dislocation.

BACKGROUND CONTEXT: Traumatic cervical facet dislocation accounts for a disproportionate rate of neurologic disability. The relative importance of patient and management variables, including the timing of spinal reduction, in ultimate neurologic outcome has not been well defined. PURPOSE: To analyze data from a cohort of patients sustaining traumatic cervical facet dislocation to determine the relative importance of several patient and management variables in neurologic recovery after injury. STUDY DESIGN/SETTING: A retrospective study was conducted at a major referral center for spinal-cord-injured patients. PATIENT SAMPLE: Forty-five patients sustaining traumatic cervical facet dislocation. OUTCOME MEASURES: Using improvement in American Spinal Injury Association (ASIA) motor score as the primary outcome measure, patient data were used to construct a statistical model allowing the analysis of several clinically relevant variables. METHODS: The records of patients sustaining a traumatic cervical facet dislocation over a 5-year period were reviewed. Clinical data were collected for all patients with adequate follow-up. The data were used to construct a statistical model designed to analyze the contribution of the variables age, gender, time to reduction of the spine and initial motor score to neurologic improvement (the outcome measure). In addition, the effect of variable interaction was studied. RESULTS: Most patients demonstrated neurologic improvement over the course of follow-up after cervical facet dislocation. For this data set, the variables age and initial motor score were significantly associated with neurologic improvement. However, time to reduction of the spine did not demonstrate a significant independent relationship to neurologic outcome. No significant interaction was found between patient age or gender and the time to reduction with regard to predicting neurologic recovery. CONCLUSION: The present study uses a statistical model to determine the relative importance of clinically relevant variables for a population of patients after traumatic cervical facet dislocation. This model confirms the clinical impression that younger patients with lesser degrees of neurologic injury tend to achieve the best neurologic recovery after a traumatic facet dislocation. Although a strong benefit from earlier spinal column reduction did not emerge from the present data set, additional study is needed to define those patients who would benefit from immediate reduction of the spinal column.

Adult↗

PubMed2005

In search of fewer independent risk factors.

More than 1100 articles now appear annually investigating "independent risk factors" or "independent predictors" for various clinical outcomes. In medical research, independence is generally defined in a statistical sense: a variable is called an independent risk factor if it has a significant contribution to an outcome in a statistical model that includes established risk factors. As such, independence is based on a specific statistical model and depends on the set of established risk factors included in that model. Even when strong statistical evidence indicates that a variable is an independent risk factor for an outcome, this does not necessarily indicate that the risk factor causally contributes to the outcome. The opposite is also true: risk factors that have causal relationships with the outcome will not necessarily prove to be independent risk factors. These are basic statistical principles that are too often given short shrift in medical research. Herein, we discuss the clinical implications conferred by the above definition of independence, primarily using examples from recent cardiovascular literature. A glossary and schema are provided to help clinicians and researchers understand and discuss these matters effectively.

Age Factors↗

PubMed2006

Probabilistic dietary exposure assessment taking into account variability in both amount and frequency of consumption.

Probabilistic dietary exposure assessments that are fully based on Monte Carlo sampling from the raw intake data may not be appropriate. This paper shows that the data should first be analysed by using a statistical model that is able to take the various dimensions of food consumption patterns into account. A (parametric) model is discussed that takes into account the interindividual variation in (daily) consumption frequencies, as well as in amounts consumed. Further, the model can be used to include covariates, such as age, sex, or other individual attributes. Some illustrative examples show how this model may be used to estimate the probability of exceeding an (acute or chronic) exposure limit. These results are compared with the results based on directly counting the fraction of observed intakes exceeding the limit value. This comparison shows that the latter method is not adequate, in particular for the acute exposure situation. A two-step approach for probabilistic (acute) exposure assessment is proposed: first analyse the consumption data by a (parametric) statistical model as discussed in this paper, and then use Monte Carlo techniques for combining the variation in concentrations with the variation in consumption (by sampling from the statistical model). This approach results in an estimate of the fraction of the population as a function of the fraction of days at which the exposure limit is exceeded by the individual.

Adolescent↗

PubMed2002

Stochastic processes defining sensitivity and variability of internally calibrated quantitative NASBA-based viral load assays.

For quantitative assessment of virus particles in patient plasma samples various assays are commercially available. Typical performance characteristics for such assays are sensitivity, precision and the range of linearity. In order to assess these properties it is common practice to divide the range of inputs into subranges in order to apply different statistical models to evaluate these properties separately. We developed a general statistical model for internally calibrated amplification based viral load assays that combines these statistical properties in one powerful analysis. Based on the model an unambiguous definition of the lower limit of the linear range can be given. The proposed method of analysis was illustrated by a successful application to data generated by the NucliSens EasyQ HIV-1 assay.

HIV-1↗

PubMed2002

A systematic statistical linear modeling approach to oligonucleotide array experiments.

We outline and describe steps for a statistically rigorous approach to analyzing probe-level Affymetrix GeneChip data. The approach employs classical linear mixed models and operates on a gene-by-gene basis. Forgoing any attempts at gene presence or absence calls, the method simultaneously considers the data across all chips in an experiment. Primary output includes precise estimates of fold change (some as low as 1.1), their statistical significance, and measures of array and probe variability. The method can accommodate complex experiments involving many kinds of treatments and can test for their effects at the probe level. Furthermore, mismatch probe data can be incorporated in different ways or ignored altogether. Data from an ionizing radiation experiment on human cell lines illustrate the key concepts.

Cells, Cultured↗

PubMed2004

Statistical evaluation of an acute dermal toxicity test using the dermal fixed dose procedure.

The conventional method for the assessment of acute dermal toxicity (OECD Test Guideline 402, 1987) uses death of animals as an endpoint to identify the median lethal dose (LD50). A new OECD Testing Guideline called the dermal fixed dose procedure (dermal FDP) is being prepared to provide an alternative to Test Guideline 402. In contrast to Test Guideline 402, the dermal FDP does not provide a point estimate of the LD50, but aims to identify that dose of the substance under investigation that causes clear signs of nonlethal toxicity. This is then used to assign classification according to the new Globally Harmonised System of Classification and Labelling scheme (GHS). The dermal FDP has been validated using statistical modelling rather than by in vivo testing. The statistical modelling approach enables calculation of the probability of each GHS classification and the expected numbers of deaths and animals used in the test for imaginary substances with a range of LD50 values and dose-response curve slopes. This paper describes the dermal FDP and reports the results from the statistical evaluation. It is shown that the procedure will be completed with considerably less death and suffering than guideline 402, and will classify substances either in the same or a more stringent GHS class than that assigned on the basis of the LD50 value.

Administration, Cutaneous↗

PubMed2006

Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins.

MOTIVATION: Integrated analysis of global scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. However, because the relationship between protein abundance and mRNA expression level is complicated by many cellular and physical processes, sophisticated statistical models need to be developed to capture their relationship. RESULTS: In this study, we describe a novel data-driven statistical model to integrate whole-genome microarray and proteomic data collected from Desulfovibrio vulgaris grown under three different conditions. Based on the Poisson distribution pattern of proteomic data and the fact that a large number of proteins were undetected (excess zeros), zero-inflated Poisson (ZIP)-based models were proposed to define the correlation pattern between mRNA and protein abundance. In addition, by assuming that there is a probability mass at zero representing unexpressed genes and expressed proteins that were undetected owing to technical limitations, a Potential ZIP model was established. Two significant improvements introduced by this approach are (1) the predicted protein abundance level values for experimentally detected proteins are corrected by considering their mRNA levels and (2) protein abundance values can be predicted for undetected proteins (in the case of this study, approximately 83% of the proteins in the D.vulgaris genome) for better biological interpretation. We demonstrated the use of these statistical models by comparatively analyzing proteomic and microarray results from D.vulgaris grown on lactate-based versus formate-based media. These models correctly predicted increased expression of Ech hydrogenase and decreased expression of Coo hydrogenase for D.vulgaris grown on formate.

Adenosine Triphosphate↗

PubMedDate not supplied

Multilevel modeling of fetal and placental growth using echo-planar magnetic resonance imaging.

OBJECTIVE: To quantify longitudinal increases in fetal, fetal liver, and fetal brain volume using echo-planar magnetic resonance imaging and to quantify the results using appropriate statistical modeling. METHODS: Fifty-six singleton fetuses were studied using echo-planar (snap-shot) magnetic resonance imaging, between 19 weeks and term. They were assessed at a variety of different gestations and on a different number of occasions, thereby requiring multilevel statistical modeling to analyze the pattern of fetal growth. RESULTS: Fetal volume varied according to the following equation: square root (radical) [fetal volume]=-37.71+2.17 x gestational age (GA)-0.004 x GA(2). The equation for fetal liver volume was radical[fetal liver volume]=9.47+0.56 x GA-0.02 x GA(2), for fetal brain volume was radical[fetal brain volume]=15.50+0.69 x GA-0.014 x GA(2), and for placental volume radical[placental volume]=28.54+0.95 x GA-0.039 x GA(2), where GA is the gestational age in weeks -30. CONCLUSION: The assessment of fetal, fetal organ, and placental volume was feasible using echo-planar magnetic resonance imaging from 20 weeks to term. Multilevel statistical modeling can be applied to analyze sets of data with different measurements on different occasions. This information is useful clinically to assess abnormal fetal growth.

Adult↗

PubMed2006

EVEREST: automatic identification and classification of protein domains in all protein sequences.

BACKGROUND: Proteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational methodologies for large-scale determination of protein domains and their boundaries. We provide and rigorously evaluate a novel set of domain families that is automatically generated from sequence data. Our domain family identification process, called EVEREST (EVolutionary Ensembles of REcurrent SegmenTs), begins by constructing a library of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families. The selection of the best putative families is done using machine learning techniques. A statistical model is then created for each of the chosen families. This procedure is then iterated: the aforementioned statistical models are used to scan all protein sequences, to recreate a library of segments and to cluster them again. RESULTS: Processing the Swiss-Prot section of the UniProt Knoledgebase, release 7.2, EVEREST defines 20,230 domains, covering 85% of the amino acids of the Swiss-Prot database. EVEREST annotates 11,852 proteins (6% of the database) that are not annotated by Pfam A. In addition, in 43,086 proteins (20% of the database), EVEREST annotates a part of the protein that is not annotated by Pfam A. Performance tests show that EVEREST recovers 56% of Pfam A families and 63% of SCOP families with high accuracy, and suggests previously unknown domain families with at least 51% fidelity. EVEREST domains are often a combination of domains as defined by Pfam or SCOP and are frequently sub-domains of such domains. CONCLUSION: The EVEREST process and its output domain families provide an exhaustive and validated view of the protein domain world that is automatically generated from sequence data. The EVEREST library of domain families, accessible for browsing and download at 1, provides a complementary view to that provided by other existing libraries. Furthermore, since it is automatic, the EVEREST process is scalable and we will run it in the future on larger databases as well. The EVEREST source files are available for download from the EVEREST web site.

Cluster Analysis↗