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PubMed2000

Secondary structure alone is generally not statistically significant for the detection of noncoding RNAs.

MOTIVATION: Several results in the literature suggest that biologically interesting RNAs have secondary structures that are more stable than expected by chance. Based on these observations, we developed a scanning algorithm for detecting noncoding RNA genes in genome sequences, using a fully probabilistic version of the Zuker minimum-energy folding algorithm. RESULTS: Preliminary results were encouraging, but certain anomalies led us to do a carefully controlled investigation of this class of methods. Ultimately, our results argue that for the probabilistic model there is indeed a statistical effect, but it comes mostly from local base-composition bias and not from RNA secondary structure. For the thermodynamic implementation (which evaluates statistical significance by doing Monte Carlo shuffling in fixed-length sequence windows, thus eliminating the base-composition effect) the signals for noncoding RNAs are still usually indistinguishable from noise, especially when certain statistical artifacts resulting from local base-composition inhomogeneity are taken into account. We conclude that although a distinct, stable secondary structure is undoubtedly important in most noncoding RNAs, the stability of most noncoding RNA secondary structures is not sufficiently different from the predicted stability of a random sequence to be useful as a general genefinding approach.

Algorithms↗
PubMed2004

An efficient Monte Carlo approach to assessing statistical significance in genomic studies.

MOTIVATION: Multiple hypothesis testing is a common problem in genome research, particularly in microarray experiments and genomewide association studies. Failure to account for the effects of multiple comparisons would result in an abundance of false positive results. The Bonferroni correction and Holm's step-down procedure are overly conservative, whereas the permutation test is time-consuming and is restricted to simple problems. RESULTS: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then used the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as familywise error rates and positive false discovery rates. This approach is applicable to any data structures and test statistics. Applications to simulated and real data demonstrate that the proposed approach provides accurate error control, and can be substantially more powerful than the Bonferroni and Holm methods, especially when the test statistics are highly correlated.

Algorithms↗
PubMed2005

apTreeshape: statistical analysis of phylogenetic tree shape.

apTreeshape is a R package dedicated to simulation and analysis of phylogenetic tree topologies using statistical imbalance measures. It is a companion library of the R package 'ape', which provides additional functions for reading, plotting, manipulating phylogenetic trees and for connecting to public phylogenetic tree databases. One strength of the package is to include appropriate corrections of classical shape statistics as well as new tests based on the statistical theory of likelihood ratios.

Algorithms↗
PubMed1994

Why infant very low birthweight rates have failed to decline in the United States vital statistics.

BACKGROUND: Very low birthweight (VLBW) rates, i.e. births of < 1500 g, have been long established as the major determinant of outcome for infants. Nevertheless, despite much medical, political and social effort, these rates have not declined during the past 40 years in the US Vital Statistics. To explore the causes of this disturbing finding, trends in low birthweight distribution at a community hospital and the Vital Statistics data were investigated. METHODS: The weights of livebirths and foetal deaths (27,944 births) at a community hospital during three 5-year periods, 1973-1987, were examined for trends in low birthweight (LBW) distribution, and they were contrasted with the US Vital Statistics. RESULTS: For white infants weighing 500-999 g the livebirth rates declined from 3.7 per 1000 births in period one to 1.1 per 1000 in period three (70.2%); for white infants 1000-1499 g, livebirths declined from 4.2 to 3.2 per 1000. Hospital white livebirths < 2500 g declined 18.7% from the first to the third period. Among black livebirths the LBW rate also declined from 126.4 per 1000 in period one to 85.4 per 1000 in the third period. Foetal deaths declined from 8.2 per 1000 births to 6.2 per 1000 in period three, a 24.4% decline. However, foetal deaths < 500 g increased 411%, from 3.6% of the total foetal deaths in period one to 18.4% in the third period. CONCLUSION: Increasing registration of births in the US may have hidden a marked improvement in the VLBW problem. More complete registration of births and changes in the perception of viability appear responsible for the artefact.

Bias↗
PubMed2003

Aspects of design and statistical analysis in the Comet assay.

Some aspects of the statistical design and analysis of the Comet (single cell gel electrophoresis) assay have been evaluated by means of a simulation study. The tail length and tail moment were selected for the quantification of DNA migration. Results from the simulation study showed that the choice of measure to summarize the cells on each slide is extremely important in order to facilitate an efficient analysis. For tail moment, the mean of log transformed data is clearly superior to the other evaluated measures, whereas using the mean of raw data without transformation can lead to very inefficient analyses. The 90th percentile, capturing the upper tail of the distribution, performs well for the tail length, with a slight improvement obtained by applying a log transformation prior to calculations. Furthermore, the simulation study has been used to assess the appropriateness of some models for statistical analysis and to address the issue of design (i.e. number of cultures or animals in each group, number of slides per animal/culture and number of cells scored per slide). Combining the results from the simulations with practical experience from the pharmaceutical industry, we conclude the paper by providing concise recommendations regarding the design and statistical analysis in the Comet assay.

Animals↗
PubMed1995

Statistical methods and strategies for working with large data bases.

This article describes the statistical methods and strategies to be used in establishing the linkages between processes and structures of care with risk-adjusted outcomes in a large multicenter Veterans Affairs cooperative study in health services of patients undergoing cardiac surgery. The statistical analyses consist of test involving nine specific hypotheses related to the effect of processes and structures of care on risk-adjusted outcomes. From the statistical point of view, the major obstacles of this study are the need for data reduction and imputation of missing data. The former obstacle is addressed through the use of data-reduction techniques, such as principal components and cluster of variables. The latter is addressed through the use of classic and new techniques for imputation of missing data, such as MISSGEN, principal components for qualitative data, and the expectation and maximization algorithm. Data reduction and imputation of missing data are done with clinically derived variable groups called "dimensions" or "subdimensions." The effect of processes and structures of care is assessed by a two-step process. First, outcomes are modeled using only patient risk factors. The selection of risk factors in the modeling process is discussed in detail. Second, these risk-adjusted outcomes are modeled using one of the nine process or structure subhypotheses. The relationship of the processes and structures of care dimensions and/or subdimensions that are linked to risk-adjusted outcomes are identified.

Cardiac Surgical Procedures↗
PubMedDate not supplied

Helping students learn and apply statistical analysis. A metacognitive approach.

Metacognition, the ability to decipher known from unknown information continuously, can facilitate nursing students' study of statistical analysis. By giving learners the "big picture" about critical issues in quantitative data analysis, faculty members can help students use statistics knowledgeably. Faculty members can use devices such as the chart contained in this article to lower student anxiety and assist with the real-world application of statistical analysis.

Data Interpretation, Statistical↗
PubMed2005

An information fidelity criterion for image quality assessment using natural scene statistics.

Measurement of visual quality is of fundamental importance to numerous image and video processing applications. The goal of quality assessment (QA) research is to design algorithms that can automatically assess the quality of images or videos in a perceptually consistent manner. Traditionally, image QA algorithms interpret image quality as fidelity or similarity with a "reference" or "perfecft" image in some perceptual space. Such "full-referenc" QA methods attempt to achieve consistency in quality prediction by modeling salient physiological and psychovisual features of the human visual system (HVS), or by arbitrary signal fidelity criteria. In this paper, we approach the problem of image QA by proposing a novel information fidelity criterion that is based on natural scene statistics. QA systems are invariably involved with judging the visual quality of images and videos that are meant for "human consumption." Researchers have developed sophisticated models to capture the statistics of natural signals, that is, pictures and videos of the visual environment. Using these statistical models in an information-theoretic setting, we derive a novel QA algorithm that provides clear advantages over the traditional approaches. In particular, it is parameterless and outperforms current methods in our testing. We validate the performance of our algorithm with an extensive subjective study involving 779 images. We also show that, although our approach distinctly departs from traditional HVS-based methods, it is functionally similar to them under certain conditions, yet it outperforms them due to improved modeling. The code and the data from the subjective study are available at.

Algorithms↗
PubMed2006

Modeling the amplitude statistics of ultrasonic images.

In this paper, a new statistical model for representing the amplitude statistics of ultrasonic images is presented. The model is called the Rician inverse Gaussian (RiIG) distribution, due to the fact that it is constructed as a mixture of the Rice distribution and the Inverse Gaussian distribution. The probability density function (pdf) of the RiIG model is given in closed form as a function of three parameters. Some theoretical background on this new model is discussed, and an iterative algorithm for estimating its parameters from data is given. Then, the appropriateness of the RiIG distribution as a model for the amplitude statistics of medical ultrasound images is experimentally studied. It is shown that the new distribution can fit to the various shapes of local histograms of linearly scaled ultrasound data better than existing models. A log-likelihood cross-validation comparison of the predictive performance of the RiIG, the K, and the generalized Nakagami models turns out in favor of the new model. Furthermore, a maximum a posteriori (MAP) filter is developed based on the RiIG distribution. Experimental studies show that the RiIG MAP filter has excellent filtering performance in the sense that it smooths homogeneous regions, and at the same time preserves details.

Algorithms↗
PubMed2006

DTI segmentation by statistical surface evolution.

We address the problem of the segmentation of cerebral white matter structures from diffusion tensor images (DTI). A DTI produces, from a set of diffusion-weighted MR images, tensor-valued images where each voxel is assigned with a 3 x 3 symmetric, positive-definite matrix. This second order tensor is simply the covariance matrix of a local Gaussian process, with zero-mean, modeling the average motion of water molecules. As we will show in this paper, the definition of a dissimilarity measure and statistics between such quantities is a nontrivial task which must be tackled carefully. We claim and demonstrate that, by using the theoretically well-founded differential geometrical properties of the manifold of multivariate normal distributions, it is possible to improve the quality of the segmentation results obtained with other dissimilarity measures such as the Euclidean distance or the Kullback-Leibler divergence. The main goal of this paper is to prove that the choice of the probability metric, i.e., the dissimilarity measure, has a deep impact on the tensor statistics and, hence, on the achieved results. We introduce a variational formulation, in the level-set framework, to estimate the optimal segmentation of a DTI according to the following hypothesis: Diffusion tensors exhibit a Gaussian distribution in the different partitions. We must also respect the geometric constraints imposed by the interfaces existing among the cerebral structures and detected by the gradient of the DTI. We show how to express all the statistical quantities for the different probability metrics. We validate and compare the results obtained on various synthetic data-sets, a biological rat spinal cord phantom and human brain DTIs.

Algorithms↗
PubMed2005

On the use of error propagation for statistical validation of computer vision software.

Computer vision software is complex, involving many tens of thousands of lines of code. Coding mistakes are not uncommon. When the vision algorithms are run on controlled data which meet all the algorithm assumptions, the results are often statistically predictable. This renders it possible to statistically validate the computer vision software and its associated theoretical derivations. In this paper, we review the general theory for some relevant kinds of statistical testing and then illustrate this experimental methodology to validate our building parameter estimation software. This software estimates the 3D positions of buildings vertices based on the input data obtained from multi-image photogrammetric resection calculations and 3D geometric information relating some of the points, lines and planes of the buildings to each other.

Algorithms↗
PubMed2000

Approximating the distribution of maximally selected McNemar's statistics.

It is common in epidemiologic analyses to summarize continuous outcomes as falling above or below a threshold. With paired data and with a threshold chosen without reference to the outcomes, McNemar's test of marginal homogeneity may be applied to the resulting dichotomous pairs when testing for equality of the marginal distributions of the underlying continuous outcomes. If the threshold is chosen to maximize the test statistic, however, referring the resulting test statistic to the nominal chi 2 distribution is incorrect; instead, the p-value must be adjusted for the multiple comparisons. Here the distribution of a maximally selected McNemar's statistic is derived, and it is shown that an approximation due to Durbin (1985, Journal of Applied Probability 22, 99-122) may be used to estimate approximate p-values. The methodology is illustrated by an application to measurements of insulin-like growth factor-I (IGF-I) in matched prostate cancer cases and controls from the Physicians' Health Study. The results of simulation experiments that assess the accuracy of the approximation in moderate sample sizes are reported.

Case-Control Studies↗
PubMed2004

Methods for the statistical analysis of binary data in split-cluster designs.

Split-cluster designs are frequently used in the health sciences when naturally occurring clusters such as multiple sites or organs in the same subject are assigned to different treatments. However, statistical methods for the analysis of binary data arising from such designs are not well developed. The purpose of this article is to propose and evaluate a new procedure for testing the equality of event rates in a design dividing each of k clusters into two segments having multiple sites (e.g., teeth, lesions). The test statistic proposed is a generalization of a previously published procedure based on adjusting the standard Pearson chi-square statistic, but can also be derived as a score test using the approach of generalized estimating equations.

Anti-Infective Agents, Local↗
PubMed1996

Analysing the relationship between maternal weight gain and birthweight: exploration of four statistical issues.

Four statistical issues concerning the analysis of birthweight and maternal weight gain during pregnancy are discussed: (1) Part-whole correlation is described (e.g. the correlation between total maternal weight gain and her infant's birthweight). (2) The choice between a ratio or two separate explanatory variables is explored (e.g. body mass index or using maternal weight and height separately). (3) Two statistical properties (bias and power) when a binary variable replaces a continuous variable are discussed (e.g. consequences of using low birthweight instead of reported birthweight). (4) A model selection procedure is presented to provide a way to select a useful subset of variables from a large number of available explanatory variables to model an outcome variable (e.g. birthweight). These issues are illustrated with a set of 4017 births from Moffitt Hospital at the University of California, San Francisco. Furthermore, these four issues arise in a number of applications of statistical methods to data collected to study the epidemiology of newborn infants.

Bias↗
PubMed2006

Testing for additivity at select mixture groups of interest based on statistical equivalence testing methods.

Several assumptions, defined and undefined, are used in the toxicity assessment of chemical mixtures. In scientific practice mixture components in the low-dose region, particularly subthreshold doses, are often assumed to behave additively (i.e., zero interaction) based on heuristic arguments. This assumption has important implications in the practice of risk assessment, but has not been experimentally tested. We have developed methodology to test for additivity in the sense of Berenbaum (Advances in Cancer Research, 1981), based on the statistical equivalence testing literature where the null hypothesis of interaction is rejected for the alternative hypothesis of additivity when data support the claim. The implication of this approach is that conclusions of additivity are made with a false positive rate controlled by the experimenter. The claim of additivity is based on prespecified additivity margins, which are chosen using expert biological judgment such that small deviations from additivity, which are not considered to be biologically important, are not statistically significant. This approach is in contrast to the usual hypothesis-testing framework that assumes additivity in the null hypothesis and rejects when there is significant evidence of interaction. In this scenario, failure to reject may be due to lack of statistical power making the claim of additivity problematic. The proposed method is illustrated in a mixture of five organophosphorus pesticides that were experimentally evaluated alone and at relevant mixing ratios. Motor activity was assessed in adult male rats following acute exposure. Four low-dose mixture groups were evaluated. Evidence of additivity is found in three of the four low-dose mixture groups. The proposed method tests for additivity of the whole mixture and does not take into account subset interactions (e.g., synergistic, antagonistic) that may have occurred and cancelled each other out.

Animals↗
PubMed2006

Optimization of two-stage genetic designs where data are combined using an accurate and efficient approximation for Pearson's statistic.

There is an increasing interest in the use of two-stage case-control studies to reduce genotyping costs in the search for genes underlying common disorders. Instead of analyzing the data from the second stage separately, a more powerful test can be performed by combining the data from both stages. However, standard tests cannot be used because only the markers that are significant in the first stage are selected for the second stage and the test statistics at both stages are dependent because they partly involve the same data. Theoretical approximations are not available for commonly used test statistics and in this specific context simulations can be problematic because of the computational burden. We therefore derived a cost-effective, that is, accurate but fast in terms of central processing unit (CPU) time, approximation for the distribution of Pearson's statistic on 2 xm contingency tables in two-stage design with combined data. We included this approximation in an iterative method for designing optimal two-stage studies. Simulations supported the accuracy of our approximation. Numerical results confirmed that the use of two-stage designs reduces the genotyping burden substantially. Compared to not combining data, combining the data decreases the required sample sizes on average by 15% and the genotyping burden by 5%.

Biometry↗
PubMed1999

Statistical methodology: VIII. Using confirmatory factor analysis (CFA) in emergency medicine research.

How many underlying characteristics (or factors) does a set of survey questions measure? When subjects answer a set of self-report questions, is it more appropriate to analyze the questions individually, to pool responses to all of the questions to form one global score, or to combine subsets of related questions to define multiple underlying factors? Factor analysis is the statistical method of choice for answering such questions. When researchers have no idea beforehand about what factors may underlie a set of questions, they use exploratory factor analysis to infer the best explanatory model from observed data "after the fact." If, on the other hand, researchers have a hypothesis beforehand about the underlying factors, then they can use confirmatory factor analysis (CFA) to evaluate how well this model explains the observed data and to compare the model's goodness-of-fit with that of other competing models. This article describes the basic rules and building blocks of CFA: what it is, how it works, and how researchers can use it. The authors begin by placing CFA in the context of a common research application-namely, assessing quality of medical outcome using a patient satisfaction survey. They then explain, within this research context, how CFA is used to evaluate the explanatory power of a factor model and to decide which model or models best represent the data. The information that must be specified in the analysis to estimate a CFA model is highlighted, and the statistical assumptions and limitations of this analysis are noted. Analyzing the responses of 1,614 emergency medical patients to a commonly-used "patient satisfaction" questionnaire, the authors demonstrate how to: 1) compare competing factor-models to find the best-fitting model; 2) modify models to improve their goodness-of-fit; 3) test hypotheses about relationships among the underlying factors; 4) examine mean differences in "factor scores"; and 5) refine an existing instrument into a more streamlined form that has fewer questions and better conceptual and statistical precision than the original instrument. Finally, the role of CFA in developing new instruments is discussed.

Data Interpretation, Statistical↗
PubMed1990

Statistical models for data from periodontal research.

Many factors have been hypothesised either to characterise groups and individuals at risk for periodontal disease or to be markers of periodontal breakdown. In order to identify these as associated either with disease status or progression, a statistical association between the factor and a measure of disease will have to be demonstrated. The statistical modelling of data arising from periodontal research presents special problems. These include the large number of measurements made in each subject, the large magnitude of measurement error compared to the changes in attachment level, the analysis of longitudinal studies, the lack of a measure of instantaneous rate of attachment loss and controversies over the nature of the progression of the disease. We consider statistical methods currently available in the light of these difficulties and identify areas in which further research is necessary.

Analysis of Variance↗