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Results for “Models, Statistical”

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At least 271 records · Page 15Linked to original sources

PubMed1989

Development of a statistical model for predicting the ethanol content of blood from measurements on saliva or breath samples.

Blood, saliva and breath samples from a population of males and females subjected to the intake of preselected amounts of ethanol, whilst in different physical conditions (at rest, after physical exertion, on an empty stomach and after eating), were analysed by automatic methods employing immobilized (blood) or dissolved (saliva) enzymes and a breathanalyser. Treatment of the results obtained enabled the development of a statistical model for prediction of the ethanol concentration in blood at a given time from the ethanol concentration in saliva or breath obtained at a later time.

Adult↗

PubMedDate not supplied

[Statistical models and multivariable analysis].

Most clinical research can be simplified as an investigation of an input/output relationship. The inputs are called explanatory (independent) variables or predictors and are thought to be related to the outcome, or response (independent) variable. This relationship is usually complicated by other factors related to both the input and the output (presence of confounding) and can vary according to the levels of the other variables (presence of interaction). This input/output relationship is usually described by statistical models that include a fit part and a residual component or difference between the data and the fit. The most popular models are the general linear models, which can be considered the paradigm of all models used in multi-variable analyzes.

Biomedical Research↗

PubMed1988

Use of a Bayesian statistical model for risk assessment in coronary artery surgery.

A computerized statistical model based on the theorem of Bayes was developed to predict mortality after coronary artery bypass grafting. From January, 1984, to April, 1987, at our hospital, 700 patients underwent isolated coronary artery bypass grafting. The presence or absence of 20 risk factors was determined for each patient. The first 300 patients formed the initial database of the Bayesian predictive model, and the remaining 400 patients were prospectively evaluated in four groups of 100 each. Each group was prospectively evaluated and then incorporated into the database to update the model. There was good agreement between predicted and observed results. Bayesian theory is particularly suited to this task because it (1) accommodates multiple risk factors, (2) is tailored to one's specific practice, (3) determines individual, rather than group, prognosis, and (4) can be updated with time to compensate for a changing patient population. These flexible attributes are especially valuable in light of recent changes in the coronary artery bypass graft patient profile.

Aged↗

PubMed1999

[Statistical models and analysis of observational studies].

Although randomized clinical trials can be regarded as the gold standard in many fields of clinical research, non-randomized (so called observational) studies are indispensable. Methodologic quality of such studies is at least as important as that of randomized trials but has been much less standardized. The analysis and interpretation of the results of observational studies is a very demanding job which needs a high level of knowledge and experience--medical as well as statistical. Basis for these analyses are complex statistical models the grounds and application of which are discussed in the following article.

Humans↗

PubMed2000

[Statistical models and evaluation of observation studies].

Clinical Trials as Topic↗

PubMed2003

Prospective estimation of recombination signal efficiency and identification of functional cryptic signals in the genome by statistical modeling.

The recombination signals (RS) that guide V(D)J recombination are phylogenetically conserved but retain a surprising degree of sequence variability, especially in the nonamer and spacer. To characterize RS variability, we computed the position-wise information, a measure correlated with sequence conservation, for each nucleotide position in an RS alignment and demonstrate that most position-wise information is present in the RS heptamers and nonamers. We have previously demonstrated significant correlations between RS positions and here show that statistical models of the correlation structure that underlies RS variability efficiently identify physiologic and cryptic RS and accurately predict the recombination efficiencies of natural and synthetic RS. In scans of mouse and human genomes, these models identify a highly conserved family of repetitive DNA as an unexpected source of frequent, cryptic RS that rearrange both in extrachromosomal substrates and in their genomic context.

Animals↗

PubMed2005

Standards for statistical models used for public reporting of health outcomes: an American Heart Association Scientific Statement from the Quality of Care and Outcomes Research Interdisciplinary Writing Group: cosponsored by the Council on Epidemiology and Prevention and the Stroke Council. Endorsed by the American College of Cardiology Foundation.

With the proliferation of efforts to report publicly the outcomes of healthcare providers and institutions, there is a growing need to define standards for the methods that are being employed. An interdisciplinary writing group identified 7 preferred attributes of statistical models used for publicly reported outcomes. These attributes include (1) clear and explicit definition of an appropriate patient sample, (2) clinical coherence of model variables, (3) sufficiently high-quality and timely data, (4) designation of an appropriate reference time before which covariates are derived and after which outcomes are measured, (5) use of an appropriate outcome and a standardized period of outcome assessment, (6) application of an analytical approach that takes into account the multilevel organization of data, and (7) disclosure of the methods used to compare outcomes, including disclosure of performance of risk-adjustment methodology in derivation and validation samples.

American Heart Association↗

PubMed2001

Application of statistical models for secondary data usage of the US Navy's Occupational Exposure Database (NOED).

Many organizations around the world have collected data related to individual worker exposures that are used to determine compliance with workplace standards. These data are often warehoused and thereafter rarely used as an information resource. Using appropriate groupings and analysis of OSHA data, Gómez showed that such stored data can provide additional insight on factors affecting occupational exposures. Using data from the Occupational Exposure Database of the United States Navy, the usefulness of statistical models for defining probabilities of exposure above permissible limits for observed work conditions is examined. Analyses have highlighted worker Similar Exposure Groups (SEGs) with potential for overexposure to asbestos and lead. In terms of grouping data, Rappaport et al. defined the Within-Between Lognormal Model, a scale-independent measure for quantifying between-worker variability within a selected worker group: (B)R.95 = exp[3.92s(sB)], representing the ratio of arithmetic mean exposures received by workers in the 97.5th and 2.5th percentiles. To help search for groups, the Proportional Odds Model, a generalization of the logistic model to ordinal data, can predict probabilities for group exposure above the Occupational Exposure Limit (OEL), or the Action Level (AL), which is one-half of the OEL. Worker SEGs have been identified for asbestos workers removing friable asbestos ((B)R.95 = 11.0) and nonfriable asbestos ((B)R.95 = 6.5); metal cleaning workers sandingspecialized equipment ((B)R.95 = 11.3), and workers at target shooting ranges cleaning up lead debris ((B)R.95 = 10). Estimated probabilities for the categories OEL support current understanding of work processes examined. Differences in probability noted between tasks and levels of ventilation validate this method for evaluating other available workplace exposure determinants, and for predicting probability of membership in categories that may help further define worker exposure groups, and determinants of excessive exposures. Thus, analyses of retrospective exposure data can help identify work site and work practice factors for efficient targeting of remediation resources.

Databases, Factual↗

PubMed2006

Age-dependent traits: a new statistical model to separate within- and between-individual effects.

Evolutionary questions regarding aging address patterns of within-individual change in traits during a lifetime. However, most studies report associations between age and, for example, reproduction based on cross-sectional comparisons, which may be confounded with progressive changes in phenotypic population composition. Unbiased estimation of patterns of age-dependent reproduction (or other traits) requires disentanglement of within-individual change (improvement, senescence) and between-individual change (selective appearance and disappearance). We introduce a new statistical model that allows patterns of variance and covariance to differ between levels of aggregation. Our approach is simpler than alternative methods and can quantify the relative contributions of within- and between-individual changes in one framework. We illustrate our model using data on a long-lived bird species, the oystercatcher (Haematopus ostralegus). We show that for different reproductive traits (timing of breeding and egg size), either within-individual improvement or selective appearance can result in a positive association between age and reproductive traits at the population level. Potential applications of our methodology are manifold because within- and between-individual patterns are likely to differ in many biological situations.

Age Factors↗

PubMed1997

A statistical model for locating regulatory regions in genomic DNA.

In addition to genes, chromosomal DNA contains sequences that serve as signals for turning on and off gene expression. These signals are thought to be distributed as clusters in the regulatory regions of genes. We develop a Bayesian model that views locating regulatory regions in genomic DNA as a change-point problem, with the beginning of regulatory and non-regulatory regions corresponding to the change points. The model is based on a hidden Markov chain. The data consist of nucleotide positions of protein-binding elements in a genomic DNA sequence. These positions are identified using a reference catalogue containing elements that interact with transcription factors implicated in controlling the expression of protein-encoding genes. Among the protein-binding elements in a genomic DNA sequence, the statistical model automatically selects those that tend to predict regulatory regions. We test the model using viral sequences that include known regulatory regions and provide the results obtained for human genomic DNA corresponding to the beta globin locus on chromosome 11.

Adenoviridae↗

PubMed1994

Evaluation of alternative statistical models for crossover studies to demonstrate caffeine adjuvancy in the treatment of tension headache.

This paper discusses alternative statistical models for the analysis of six crossover studies to determine whether better relief of tension headache occurs from treatment with an analgesic plus caffeine (C) than with the analgesic alone (A) or with placebo (P). Each patient in these crossover studies randomly received a pair of distinct medications in such a way as to treat the first two of four headaches with the initial medication in the pair and to treat the third and fourth headaches with the last medication in the pair. In order to have greater power for the C versus A comparison, three times as many patients were randomly assigned to the A:C and C:A sequence groups as to the A:P, C:P, P:A, and P:C sequence groups. An issue of statistical interest for these crossover studies is the extent to which the possibility of unequal carryover effects of the three medications influences the roles of alternative models for data analysis and the interpretation of results. When carryover effects for all three medications are equal, univariate analysis of variance for the difference scores between the average response for the first two headaches and the average response for the third and fourth headaches for each patient provides nearly the same power for pairwise treatment comparisons as more comprehensive multivariate methods for all four headaches. However, for comparisons concerning carryover effects and for treatment comparisons with adjustment for carryover effects, multivariate methods encompassing all four headaches jointly can provide greater power than univariate analysis for difference scores, particularly when there is low intraclass correlation for responses within the same patient. Another noteworthy role for multivariate methods in situations with potentially unequal carryover effects is their capacity to clarify whether multiple types of carryover effects occur across the second, third, and fourth headaches in the respective sequence groups. Multivariate models with alternative specifications of carryover effects are fit to the data from the six crossover studies to compare C, A, and P by weighted least squares. The role of potential variation among centers is addressed in these analyses by the use of stratified proportional means over centers, means of center means, and means ignoring centers. The primary focus of attention in the respective analyses is the evaluation of treatment comparisons with and without adjustment for potential differences among carryover effects of the treatments.(ABSTRACT TRUNCATED AT 400 WORDS)

Caffeine↗

PubMed1990

Statistical models for evaluating the penetrating ability of endodontic instruments.

An in vitro study was conducted to evaluate the penetrating ability of hand instruments (No. 10 K-file and "Pathfinder") and an automated device with a No. 08 (Canal Finder System) file in 270 extracted curved and narrow molar roots. The roots were randomly allocated to six groups, each one corresponding to a different permutation of the three instruments. The experiment was performed in three sequential stages, and Stages II and III were undertaken only in roots where no apical penetration was achieved in the previous stage(s). Statistical models were designed to fit the resulting clinical data. The No. 08 Canal Finder System and No. 10 K-file proved to be similar in their penetrating ability in Stage I (with estimated probabilities of 71.1% and 68.8%, respectively). The No. 08 Canal Finder was found to be the most efficient in Stage II and especially in Stage III, with a probability of penetration of 20.7%, compared with 9.8% for the "Pathfinder" and 7.4% for the No. 10 K-file.

Dental Pulp Cavity↗

PubMedDate not supplied

Application of statistical models to estimate the correlation between urinary benzene as biological indicator of exposure and air concentrations determined by personal monitoring.

This study evaluated the correlation between benzene in urine and in workplace air at low airborne benzene levels (below 1 ppm). Eleven workers were monitored over a period of 1-4 days at a petrochemical plant in Italy; samples of end-of-shift urine and workplace air were analyzed for benzene. A significant correlation, with a coefficient of determination R(2)=0.63, was found between urine and airborne benzene, confirming the results of previous studies. Two different statistical models were utilized to estimate urine benzene values of 9-16 microg/L corresponding to the American Conference of Governmental Industrial Hygienists' threshold limit value (TLV) of 0.5 ppm in workplace air. Not withstanding the variability inherent to biological monitoring, the results suggest application of biomonitoring as a trigger for identification of lower exposure level below, but approaching the TLV. Additionally, the proposed benzene biomonitoring may be useful in evaluating PPE effectiveness and use characteristics as well as dermal contribution to total exposure.

Adult↗

PubMed1994

Statistical models of synaptic transmission evaluated using the expectation-maximization algorithm.

Amplitude fluctuations of evoked synaptic responses can be used to extract information on the probabilities of release at the active sites, and on the amplitudes of the synaptic responses generated by transmission at each active site. The parameters that describe this process must be obtained from an incomplete data set represented by the probability density of the evoked synaptic response. In this paper, the equations required to calculate these parameters using the Expectation-Maximization algorithm and the maximum likelihood criterion have been derived for a variety of statistical models of synaptic transmission. These models are ones where the probabilities associated with the different discrete amplitudes in the evoked responses are a) unconstrained, b) binomial, and c) compound binomial. The discrete amplitudes may be separated by equal (quantal) or unequal amounts, with or without quantal variance. Alternative models have been considered where the variance associated with the discrete amplitudes is sufficiently large such that no quantal amplitudes can be detected. These models involve the sum of a normal distribution (to represent failures) and a unimodal distribution (to represent the evoked responses). The implementation of the algorithm is described in each case, and its accuracy and convergence have been demonstrated.

Algorithms↗

PubMed2005

Statistical modelling of riverine nutrient sources and retention in the Lake Peipsi drainage basin.

Implementation of the Water Framework Directive calls for methodologies and tools to quantify nutrient losses from diffuse sources at a river basin district scale. Here, we examine the possibility of using a statistical model for source apportionment and retention of nutrients in a large transboundary drainage basin (44,000 km2). The model approach uses non-linear regression for simultaneous estimation of e.g. source strength, i.e. export coefficients to surface waters, for the different specified land-use or soil categories and retention coefficients for pollutants in a drainage basin. The model was tested on data from 26 water quality stations with corresponding sub-basin data, i.e., land cover, point sources and atmospheric deposition, from the Estonian part of the Lake Peipsi drainage basin. The model showed that it was statistically possible to derive reliable export coefficients (i.e. unit-area loads) for nitrogen on agricultural land and forests. Moreover, it was shown with simple empirical functions that lake retention was approximately 30-35% for both nitrogen and phosphorus and that the riverine retention was low for both nitrogen and phosphorus (approx. 10%). Results show that the MESAW model is a simple and powerful tool for simultaneous estimation of sources and retention of nutrient loads in a river basin.

Agriculture↗

PubMed1997

Statistical modeling and analysis of the LAGLIDADG family of site-specific endonucleases and identification of an intein that encodes a site-specific endonuclease of the HNH family.

The LAGLIDADG and HNH families of site-specific DNA endonucleases encoded by viruses, bacteriophages as well as archaeal, eucaryotic nuclear and organellar genomes are characterized by the sequence motifs 'LAGLIDADG' and 'HNH', respectively. These endonucleases have been shown to occur in different environments: LAGLIDADG endonucleases are found in inteins, archaeal and group I introns and as free standing open reading frames (ORFs); HNH endonucleases occur in group I and group II introns and as ORFs. Here, statistical models (hidden Markov models, HMMs) that encompass both the conserved motifs and more variable regions of these families have been created and employed to characterize known and potential new family members. A number of new, putative LAGLIDADG and HNH endonucleases have been identified including an intein-encoded HNH sequence. Analysis of an HMM-generated multiple alignment of 130 LAGLIDADG family members and the three-dimensional structure of the I- Cre I endonuclease has enabled definition of the core elements of the repeated domain (approximately 90 residues) that is present in this family of proteins. A conserved negatively charged residue is proposed to be involved in catalysis. Phylogenetic analysis of the two families indicates a lack of exchange of endonucleases between different mobile elements (environments) and between hosts from different phylogenetic kingdoms. However, there does appear to have been considerable exchange of endonuclease domains amongst elements of the same type. Such events are suggested to be important for the formation of elements of new specficity.

Amino Acid Sequence↗

PubMed2004

Sensory evaluation of the odors produced during bromophenol formation using a multi-level statistical model.

In response to reports of medicinal taste and odor problems in suburban Paris, a lab scale study was conducted to investigate the contribution of different water quality parameters--pH, phenol, bromide, chlorine, temperature and dissolved oxygen levels--on bromophenol medicinal odor formation using the Flavor Profile Analysis (FPA) method. A study of six parameters at 2 levels (64 experiments) analyzed by the FPA method suggests that chlorine at high concentration is more important as a controlling agent than phenol under similar conditions and the ratio of HOBr:Phenol and the time for reaction will control subsequent brominated products of reaction. Results from a three-level statistical model indicate that high pH was associated with lower odor intensities, whereas high levels of chlorine, phenol and temperature were associated with high odor intensities. Potential worst case scenarios of water quality conditions were determined for evaluation by chemical identification and kinetics.

Bromine↗

PubMed2000

A statistical model for assessing sample size for bacterial colony selection: a case study of Escherichia coli and avian cellulitis.

A general problem for microbiologists is determining the number of phenotypically similar colonies growing on an agar plate that must be analyzed in order to be confident of identifying all of the different strains present in the sample. If a specified number of colonies is picked from a plate on which the number of unique strains of bacteria is unknown, assigning a probability of correctly identifying all of the strains present on the plate is not a simple task. With Escherichia coli of avian cellulitis origin as a case study, a statistical model was designed that would delineate sample sizes for efficient and consistent identification of all the strains of phenotypically similar bacteria in a clinical sample. This model enables the microbiologist to calculate the probability that all of the strains contained within the sample are correctly identified and to generate probability-based sample sizes for colony identification. The probability of cellulitis lesions containing a single strain of E. coli was 95.4%. If one E. coli strain is observed out of three colonies randomly selected from a future agar plate, the probability is 98.8% that only one strain is on the plate. These results are specific for this cellulitis E. coli scenario. For systems in which the number of bacterial strains per sample is variable, this model provides a quantitative means by which sample sizes can be determined.

Animals↗