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Results for “Models, Statistical”

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At least 163 records · Page 9Linked to original sources
PubMed2006

Interobserver variation in breast cancer grading: a statistical modeling approach.

OBJECTIVE: To study random and the systematic error in breast cancer grading, to find the source of disagreements and measure the reliability of graders so that appropriate corrective action can be taken. STUDY DESIGN: Five independent observers graded 50 breast carcinoma slides from 50 consecutive breast cancer specimens according to the Nottingham criteria. The polychoric correlation was used to measure association. Stuart-Maxwell and McNemar tests were used to measure equality of thresholds. RESULTS: The polychoric correlation among observers was high (mean = 0.803, 0.712, 0.797 and 0.602 for the final grade, tubule formation, nuclear pleomorphism and mitotic figures, respectively). However, there were significant differences in thresholds (6, 7, 7 and 9 pairs of 10 showing significant differences in classification of grades/scores for final grade, tubule formation, nuclear pleomorphism and mitotic counts, respectively). CONCLUSION: The high polychoric correlations suggest that random error in grading breast cancers in this study was low, confirming the underlying reliability of grading and graders. However, significant differences in the thresholds lowers raw agreement. Such a scenario may be rectified by increased intradepartmental discussion.

Breast Neoplasms↗
PubMedDate not supplied

[Statistical model of the inherent noise in the visual system].

A concept of intrinsic noise of the visual system has been formulated. It has been shown that the intrinsic noise is realized in the form of the stochastic (from simple chance to complicated pithy) visual images. On the basis of the suggested concept in the limits of a normal approach, taking into account several obvious suppositions, two identical forms have been obtained of representations of probability density of own noise--the mathematical model of own noise. This model includes, as a particular case, the model of Swets, Tanner and Birdsall and has no inherent limitations.

Humans↗
PubMed1991

[A statistical model of evolution with stabilizing selection].

We consider a population of fixed size and reproducing asexually, evolving in a rugged fitness landscape. Selection takes place only via the elimination of individuals with unfit genomes. Unfit genotypes are distributed at random in genotypic space. The genetic structure of the population and the speed of genetic drift are explicitly computed in the infinite genome limit.

Biological Evolution↗
PubMed1994

Collective adaptation in a statistical model of an evolving population.

We simulate asexually and sexually reproducing model populations evolving in a rugged fitness landscape where fit and unfit genotypes are distributed at random, and where all fit genotypes have the same a priori probability of reproduction. Varying the fraction chi of unfit genotypes at a fixed mutation rate we observe a strikingly different behavior for the two reproduction mechanisms. For the asexually reproducing population, the effective mutation rate lambda decreases roughly proportionally to (1-chi), and the fraction delta of sterile individuals--processing an unfit genotype--accordingly increases roughly proportionally to chi. On the other hand, lambda remains approximately constant (and delta increases proportionally to chi) for small values of chi:but, at a critical value chi*, both lambda and delta suddenly drop. This corresponds to the transition to an adaptive regime where the average fitness of the population is enhanced. We show how this transition can be interpreted in terms of an improvement of the collective fitness of the population.

Biological Evolution↗
PubMed1993

Nonlinear statistical models for the joint action of toxins.

A general approach using nonlinear regression models is presented for evaluating additivity, synergism, and antagonism of mixtures of toxins for proportions and ratio-scale response measures. This approach provides several advantages over the analysis methods typically used, which involve linear regression with logits or probits. A single model fit is performed, rather than a multistep procedure. Nonadditive alternative models can be easily constructed and tested against the appropriate additive models. The approach avoids the use of data "adjustments" for nonzero background response rates. The analyses are performed in the natural response metric, making interpretation straightforward. Also, the nonlinear regression model can be reparameterized to provide more meaningful primary parameters.

Animals↗
PubMedDate not supplied

[Statistical modeling of random forearm microoscillations].

The process of motor unit switching taking into consideration morphological data about their distribution on the developed force is under analysis in this paper. The correlation between the tremor and the loading coincides with the results of psychophysical experiments (known as modified Veber-Fehner law), combining the organism reaction with the stimulation intensity.

Forearm↗
PubMed1998

Deriving ribosomal binding site (RBS) statistical models from unannotated DNA sequences and the use of the RBS model for N-terminal prediction.

Accurate prediction of the position of translation initiation (N-terminal prediction) is a difficult problem. N-terminal prediction from DNA sequence alone is ambiguous is several candidate start sites are close to each other. Protein similarity search is usually unable to indicate the true start of a gene as it would require a strong protein sequence similarity at the N-terminal portion of a protein where conservative regions are rarely situated. With the aid of the GeneMark program for gene identification, we extract DNA sequence fragments presumably containing ribosome binding sites (RBS) from unannotated complete genomic sequences. These DNA segments are aligned to generate the RBS model using the Gibbs' sampling method. N-terminal prediction is then performed by using the RBS model in conjunction with the GeneMark start codon prediction to aid in determining the true N-terminal site.

Base Sequence↗
PubMed1993

Statistical modelling of measles and influenza outbreaks.

This paper reviews the application of statistical models to outbreaks of two common respiratory viral diseases, measles and influenza. For each disease, we look first at its epidemiological characteristics and assess the extent to which these either aid or hinder modelling. We then turn to the models that have been developed to simulate geographical spread. For measles, a distinction is drawn between process-based and time series models; for influenza, it is the scale of the communities (from small groups to global populations) which primarily determines modelling style. Applications are provided from work by the authors, largely using Icelandic data. Finally we consider the forecasting potential of the models described.

Adolescent↗
PubMed1988

A statistical model of the timing mechanism of the erythrocyte life-span.

Three statistical models are introduced as explanations of erythrocyte senescence. For the sake of simplicity, we prefer the multievent model, in which one of the parameters represents the number of events (between 7 and 12) needed to signal likely senescence. Proteolysis of a dimeric protein, such as protein 3, accounts for four events in the model, if little of the protein is degraded by senescence. Although each model we propose accommodates the possibility of random destruction, this alternative does not fit well with available experimental data.

Blood Proteins↗
PubMed2006

Building statistical models to analyze species distributions.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Bayes Theorem↗
PubMed2005

Statistical modeling in case-control real-time RT-PCR assays, for identification of differentially expressed genes in schizophrenia.

Aspects of experimental design, statistical modeling, and statistical inference in case-control real-time reverse transcription-polymerase chain reaction (RT-PCR) assays are discussed. The background is mRNA expression data from an investigation of genes previously suggested to be schizophrenia related. Real-time RT-PCR allows large samples of individuals. However, with more individuals than positions per plate, incomplete designs are required. A basic multivariate (for several genes jointly) random-effects analysis of covariance model, incorporating heterogeneity both between and within individuals, is formulated. The use of reference genes to form additional regressors is found to be highly efficient. Because regressions between and within individuals are usually different, it is important first to average over the intraindividual replicates. This has consequences for the influence of plate effects. Topics also discussed are testing for a significant mean disease effect, differential coregulation, and the difficulty of identifying genes affected in only a subgroup of cases.

Analysis of Variance↗