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At least 1,351 records · Page 75Linked to original sources
PubMed2000

Searching for reliable relationships with statistics packages: an empirical example of the potential problems.

Many social scientists appear to possess an overconfidence in the reliability of research results from a single, small-sample, inferential study. In this article, the authors speculate that "user-friendly" statistics packages have the potential to exacerbate statistical misinterpretation by providing researchers with a tool to explore data easily and identify what is interpreted as "reliable" relationships. This article contains an empirical demonstration of the potential problems that arise when a large number of statistical tests are interpreted. Results show that statistically significant results may be unreliable. Also, a zero relationship can erroneously appear as a medium to large effect size relationship when a small sample is used (e.g., n = 30). The authors suggest the need for multiple replications as the criterion of a reliable finding.

Adult↗
PubMed1989

Linguistics of nucleotide sequences. I: The significance of deviations from mean statistical characteristics and prediction of the frequencies of occurrence of words.

Mathematical models of the generation of genetic texts appeared simultaneously with the first sequencing DNA. They are used to establish functional and evolutionary relations between genetic texts, to predict the number and distribution of specific sites in a sequence and to identify "meaningful" words. The present paper deals with two problems: 1) The significance of deviations from the mean statistical characteristics in a genetic text. Anyone who has addressed himself to the statistical analysis of sequenced DNA is familiar with the question: what deviations from the expected frequencies of occurrence of particular words testify to the "biological" significance of those words? We propose a formula for the variance of the number of word's occurrences in the text, with allowance for word overlaps, making it possible to assess the significance of the deviations from the expected statistical characteristics. 2) A new method for predicting the frequencies of occurrence of particular words in a genetic text using the statistical characteristics of "spaced" L-grams. The method can be used for predicting the number of restriction sites in human DNA and in planning experiments on the physical mapping and sequencing of the human genome.

Bacteriophage lambda↗
PubMed2006

Statistical power of studies examining the cognitive effects of subthalamic nucleus deep brain stimulation in Parkinson's disease.

It has been argued that neuropsychological studies generally possess adequate statistical power to detect large effect sizes. However, low statistical power is problematic in neuropsychological research involving clinical populations and novel interventions for which available sample sizes are often limited. One notable example of this problem is evident in the literature regarding the cognitive sequelae of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in persons with Parkinson's disease (PD). In the current review, a post hoc estimate of the statistical power of 30 studies examining cognitive effects of STN DBS in PD revealed adequate power to detect substantial cognitive declines (i.e., very large effect sizes), but surprisingly low estimated power to detect cognitive changes associated with conventionally small, medium, and large effect sizes. Such wide spread Type II error risk in the STN DBS cognitive outcomes literature may affect the clinical decision-making process as concerns the possible risk of postsurgical cognitive morbidity, as well as conceptual inferences to be drawn regarding the role of the STN in higher-level cognitive functions. Statistical and methodological recommendations (e.g., meta-analysis) are offered to enhance the power of current and future studies examining the neuropsychological sequelae of STN DBS in PD.

Cognition Disorders↗
PubMed2005

The Web as an educational tool for/in learning/teaching bioinformatics statistics.

Statistics provides essential tool in Bioinformatics to interpret the results of a database search or for the management of enormous amounts of information provided from genomics, proteomics and metabolomics. The goal of this project was the development of a software tool that would be as simple as possible to demonstrate the use of the Bioinformatics statistics. Computer Simulation Methods (CSMs) developed using Microsoft Excel were chosen for their broad range of applications, immediate and easy formula calculation, immediate testing and easy graphics representation, and of general use and acceptance by the scientific community. The result of these endeavours is a set of utilities which can be accessed from the following URL: http://gmein.uib.es/bioinformatica/statistics. When tested on students with previous coursework with traditional statistical teaching methods, the general opinion/overall consensus was that Web-based instruction had numerous advantages, but traditional methods with manual calculations were also needed for their theory and practice. Once having mastered the basic statistical formulas, Excel spreadsheets and graphics were shown to be very useful for trying many parameters in a rapid fashion without having to perform tedious calculations. CSMs will be of great importance for the formation of the students and professionals in the field of bioinformatics, and for upcoming applications of self-learning and continuous formation.

Computational Biology↗
PubMed2001

Nonlinear estimation and statistical testing of periods in nonsinusoidal longitudinal time series with unequidistant observations.

The analysis of multiple components is often used to model biological variables that show nonsinusoidal predictable changes of known periods. In general, to anticipate the periods is not easy, and even in cases when we have some a priori information, it is advisable to have a statistical tool to test the chosen periods. In this work, we introduce a statistical procedure to estimate periods of longitudinal series by applying nonlinear regression techniques to the multiple sinusoidal model, as well as to the general linear model. Approximate inferences about the parameters of the model are carried out under the usual hypothesis of normality, independence, and constant variance of the errors. Confidence intervals (CIs) for each individual parameter, as well as for the amplitude-acrophase pair or for any other subgroup of parameters of interest, can be computed. As in the linear analysis of multiple components, it is possible to check the existence of rhythm by means of a zero-amplitude test. The method also allows statistical testing of several hypotheses related to the periods. For example, it is possible to test if the periods are equal to certain values of chronobiologic interest and to check if some components included in the model are harmonically related. On the other hand, when the fitted components have proximal periods, the method allows one to verify if they are modeling the same or different spectral peaks. The method, which was validated by a simulation study for a model of two components and is illustrated by an example of modeling the diastolic blood pressure of two subjects, represents a new step in the development of statistical procedures in chronobiology.

Adult↗
PubMed2000

Statistics of Dark Matter Halos from Gravitational Lensing.

We present a new approach to measure the mass function of dark matter halos and to discriminate models with differing values of Omega through weak gravitational lensing. We measure the distribution of peaks from simulated lensing surveys and show that the lensing signal due to dark matter halos can be detected for a wide range of peak heights. Even when the signal-to-noise ratio is well below the limit for detection of individual halos, projected halo statistics can be constrained for halo masses spanning galactic to cluster halos. The use of peak statistics relies on an analytical model of the noise due to the intrinsic ellipticities of source galaxies. The noise model has been shown to accurately describe simulated data for a variety of input ellipticity distributions. We show that the measured peak distribution has distinct signatures of gravitational lensing, and its non-Gaussian shape can be used to distinguish models with different values of Omega. The use of peak statistics is complementary to the measurement of field statistics, such as the ellipticity correlation function, and is possibly not susceptible to the same systematic errors.

Journal Article↗
PubMed2006

Mapping tumor-suppressor genes with multipoint statistics from copy-number-variation data.

Array-based comparative genomic hybridization (arrayCGH) is a microarray-based comparative genomic hybridization technique that has been used to compare tumor genomes with normal genomes, thus providing rapid genomic assays of tumor genomes in terms of copy-number variations of those chromosomal segments that have been gained or lost. When properly interpreted, these assays are likely to shed important light on genes and mechanisms involved in the initiation and progression of cancer. Specifically, chromosomal segments, deleted in one or both copies of the diploid genomes of a group of patients with cancer, point to locations of tumor-suppressor genes (TSGs) implicated in the cancer. In this study, we focused on automatic methods for reliable detection of such genes and their locations, and we devised an efficient statistical algorithm to map TSGs, using a novel multipoint statistical score function. The proposed algorithm estimates the location of TSGs by analyzing segmental deletions (hemi- or homozygous) in the genomes of patients with cancer and the spatial relation of the deleted segments to any specific genomic interval. The algorithm assigns, to an interval of consecutive probes, a multipoint score that parsimoniously captures the underlying biology. It also computes a P value for every putative TSG by using concepts from the theory of scan statistics. Furthermore, it can identify smaller sets of predictive probes that can be used as biomarkers for diagnosis and therapeutics. We validated our method using different simulated artificial data sets and one real data set, and we report encouraging results. We discuss how, with suitable modifications to the underlying statistical model, this algorithm can be applied generally to a wider class of problems (e.g., detection of oncogenes).

Gene Deletion↗
PubMed1998

Statistical quality control methods in infection control and hospital epidemiology, part I: Introduction and basic theory.

This article is the first in a two-part series discussing and illustrating the application of statistical process control (SPC) to processes often examined by hospital epidemiologists. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand mutual understanding and cross-fertilization between these two disciplines. Part I provides an overview of quality engineering and SPC, illustrates common types of control charts, and provides references for further information or statistical formulae. Part II discusses statistical properties of control charts, issues of chart design and optimal control limit widths, alternate possible SPC approaches to infection control, some common misunderstandings, and more advanced issues. The focus of both articles is mostly non-mathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

Humans↗
PubMed1993

A hybrid method to cluster protein sequences based on statistics and artificial neural networks.

We have recently proposed a method, based on artificial neural networks (ANNs) to cluster protein sequences into families according to their degree of sequence similarity. The network was trained with an unsupervised learning algorithm, using, as inputs, matrix patterns derived from the bipeptide composition of the protein sequences. We describe here some further improvements to that approach. First, we propose a statistical method to cluster a set of bipeptidic matrices into families. It consists of three stages: (i) principal component analysis, (ii) determination of the optimal number M of clusters and (iii) final classification of the bipeptidic matrices into M clusters. Using a set of 444 protein sequences, we show that the classification given by the statistical method is in agreement with biological knowledge. We also show that the resulting classification is very similar to the one previously obtained with the ANN approach. Finally, we propose a new hybrid method of the statistical and ANN approaches, in which the results of the statistical method are used to choose the number of neurons and inputs of the network. We show that a network built in this way, and fed with a few principal components of the set of bipeptidic matrices as input signals, can be trained in an extremely short computing time. The resulting topological maps do not essentially differ from the ones obtained with the initial ANN approach.

Algorithms↗
PubMed2025

LimROTS: a hybrid method integrating empirical Bayes and reproducibility-optimized statistics for robust differential expression analysis.

MOTIVATION: Differential expression analysis plays a vital role in omics research enabling precise identification of features that associate with different phenotypes. This process is critical for uncovering biological differences between conditions, such as disease versus healthy states. In proteomics, several statistical methods have been used, ranging from simple t-tests to more advanced methods like DEqMS, limma and ROTS. However, a flexible method for reproducibility-optimized statistics tailored for clinical omics data has been lacking. RESULTS: In this study, we developed LimROTS, a hybrid method that integrates a linear regression model and the empirical Bayes approach with reproducibility optimized statistics, to create a novel moderated ranking statistic, for robust and flexible analysis of proteomics data. We validated its performance using twenty-one proteomics gold standard spike-in datasets with different protein mixtures, MS instruments, and techniques for benchmarking. This hybrid approach improves accuracy and reproducibility of complex proteomics data, making LimROTS a powerful tool for high-dimensional omics data analysis. AVAILABILITY AND IMPLEMENTATION: LimROTS has been implemented as an R/Bioconductor package, available at https://doi.org/doi:10.18129/B9.bioc.LimROTS. Additionally, the code used in this study is available in GitHub repository https://github.com/AliYoussef96/LimROTSmanuscript.

Bayes Theorem↗
PubMed1992

Statistical awareness of research workers in British anaesthesia.

A survey was made of simple statistical errors in the precirculated booklets of abstracts to the Anaesthetic Research Society. In the five booklets June 1988-November 1989, only four of 19 figures included clearly labelled error bars when necessary; in four figures there was no indication of variability. In a more detailed survey of the 115 abstracts presented in 1990, errors were placed in categories: the presentation of method or choice of statistical test; variability; probability. There was a total of 115 errors in 61 abstracts, which was 65% of the 94 abstracts presenting numerical information. The most common errors were: failure to identify tests of inferential statistics (n = 29 abstracts); failure to present data to allow interpretation of P values (n = 21); misuse of SEM (n = 13). Confidence limits were given in seven abstracts. Type II (beta) error was not searched for formally, but no abstract that reported a negative result included the power of the study. With this level of elementary errors, there is considerable room for improvement in the use of straightforward guidelines to the presentation of statistical information. The problem is not confined to British anaesthesia: in more than 30% (25 of 71) of the figures contained in abstracts of the International Anesthesia Research Society in 1990, plots of variables against time showed only mean values.

Anesthesia↗
PubMed1995

A statistical approach to measuring the competence of anaesthetic trainees at practical procedures.

Cusum analysis is a statistical technique to distinguish deviations from an acceptable failure rate. The progress of anaesthetic trainees learning four practical procedures (obstetric extradural anaesthesia, spinal anaesthesia, central venous cannulation and arterial cannulation) was monitored from their first attempt using cusum analysis. Suitable acceptable and unacceptable failure rates for each procedure were chosen by consultant anaesthetists. For obstetric extradural anaesthesia, four trainees eventually achieved acceptable failure rates (5%) and the number of attempts required to demonstrate this statistically ranged from 29 to 185; three trainees had an unacceptable failure rate (10%) and five trainees had inconclusive records. For spinal anaesthesia, two trainees achieved an acceptable failure rate (10%) and the number of attempts required to demonstrate this statistically ranged from 39 to 67; two trainees had an unacceptable failure rate (20%) and four trainees had inconclusive records. One trainee demonstrated statistically an acceptable failure rate in arterial cannulation (20%) after 14 attempts and four trainees had inconclusive records. Two records of central venous cannulation were inconclusive. Some records showed variable failure rates which were sometimes associated with lack of practice or a change in technique. Cusum analysis can be used to monitor training in practical procedures and as a continuous audit of quality of clinical practice.

Anesthesia↗
PubMed1998

Brain-behaviour relationships. Some models and related statistical procedures for the study of brain-damaged patients.

The study of brain-damaged patients provides the opportunity to examine the anatomy of brain functions, and has been renewed by the development of structural neuroimaging. Despite the development of neuroimaging and neuropsychological assessment, major uncertainties persist on the exact delimitation of the brain areas involved in specific processes, and these contribute to the enduring controversies over the effective lesions associated with neuropsychological disorders. These uncertainties are mainly due to the methods used in the study of brain-behaviour relationships, which frequently rely on a group comparison design. The aim of this study was to provide models for the study of brain-behaviour relationships and to assess the reliability of related statistical procedures. In the present work, four theoretical modes of brain-behaviour relationship consistent with neuropsychological data are put forward: unicity, equivalence, association and summation. The first experimental study was based on a simulated population of patients. Lesions associated with the occurrence of a deficit were predetermined according to modes of brain-behaviour relationship and were compared with lesions selected by statistical analysis. The study showed that (i) the group comparison design did not allow determination of the effective lesion, (ii) stepwise regression analysis was sensitive to the relative frequency of lesions, especially when the occurrence of a deficit depended on two lesions, but did not allow determination of the mode of brain-behaviour relationship, and (iii) the classification tree test described the data very satisfactorily and permitted the determination of the mode of brain-behaviour relationships. In order to assess the validity of statistical analyses, a second study was performed in which lesion locations associated with motor weakness in stroke patients were examined. Selected lesions were compared with the anatomy of the human motor system. The study mainly showed that (i) the stepwise regression analysis of selected lesions was not related to the presence of a motor deficit, and (ii) the classification tree test provided perfect agreement with motor weakness predicted by lesion locations and suggested an equivalence mode of brain-behaviour relationship consistent with current neurological knowledge. These studies provide models of brain-behaviour relationships and related statistical procedures that may allow more precise documentation of the anatomy of brain functions and its pathology, and further investigation of the modalities of brain-behaviour relationships.

Behavior↗
PubMed1990

Statistical aspects of controlled single subject trials.

Randomized controlled trials in groups and single subjects differ in several statistical aspects. In group trials the experimental unit is a randomly selected subject from a predefined population and this subject is randomly assigned to a treatment. Outcome is confined to average effects which can be generalized to the specific population, but which do not necessarily apply to individual persons. In single subject trials the experimental unit is a treatment period and each treatment period is randomly allocated in a multiple cross-over sequence of periods. The single subject is only representative of itself, but similar responses in corresponding single subject trials may justify careful extrapolation of the results. Single subject trials have a high risk of Type II errors. However, the randomization procedure chosen and the type of statistical test applied may enhance the statistical power of such trials. Internal validity depends on modeling the trial design to the clinical features, drug properties and statistical requirements, while reliability is determined by the reproducibility of the trial response.

Humans↗
PubMed1986

The allelic correlation structure of Gainj- and Kalam-speaking people. I. The estimation and interpretation of Wright's F-statistics.

The internal patterning of allelic correlations in the Gainj and Kalam swidden horticulturalists of highland Papua New Guinea is examined within the context of Sewall Wright's F-statistic model. A multiallelic extension of the model is given first, and multivariate variance-component estimators for the parameters are suggested. Then, it is shown that the expectation of the F-statistic set depends on the age structure of the population and that knowledge of the population and sample age structure is critical for meaningful analysis. The array of F-statistics estimated jointly over five polymorphic enzyme loci reveals the following features of Gainj and Kalam population structure: (1) significant departures from panmictic expectations and (2) characteristics of a continuously distributed breeding population, rather than those expected for populations subdivided into demes with discrete boundaries. Finally, the F-statistics estimated for the Gainj and Kalam are briefly compared to estimates obtained from other tribal populations. It is seen that the level of differentiation observed in the Gainj and Kalam is only about one-third that observed in South American swidden horticulturalists. Consequently, some conventional wisdom regarding the interrelationship of socioecological settings and genetic structures may require reevaluation.

Alleles↗
PubMed1990

Statistical genetics of an annual plant, Impatiens capensis. I. Genetic basis of quantitative variation.

Analysis of quantitative genetics in natural populations has been hindered by computational and methodological problems in statistical analysis. We developed and validated a jackknife procedure to test for existence of broad sense heritabilities and dominance or maternal effects influencing quantitative characters in Impatiens capensis. Early life cycle characters showed evidence of dominance and/or maternal effects, while later characters exhibited predominantly environmental variation. Monte Carlo simulations demonstrate that these jackknife tests of variance components are extremely robust to heterogeneous error variances. Statistical methods from human genetics provide evidence for either a major locus influencing germination date, or genes that affect phenotypic variability per se. We urge explicit consideration of statistical behavior of estimation and testing procedures for proper biological interpretation of statistical results.

Genetic Variation↗
PubMed1996

A statistical test of a neutral model using the dynamics of cytonuclear disequilibria.

In this paper we use cytonuclear disequilibria to test the neutrality of mtDNA markers. The data considered here involve sample frequencies of cytonuclear genotypes subject to both statistical sampling variation as well as genetic sampling variation. First, we obtain the dynamics of the sample cytonuclear disequilibria assuming random drift alone as the source of genetic sampling variation. Next, we develop a test statistic using cytonuclear disequilibria via the theory of generalized least squares to test the random drift model. The null distribution of the test statistic is shown to be approximately chi-squared using an asymptotic argument as well as computer simulation. Power of the test statistic is investigated under an alternative model with drift and selection. The method is illustrated using data from cage experiments utilizing different cytonuclear genotypes of Drosophila melanogaster. A program for implementing the neutrality test is available upon request.

Animals↗
PubMed1997

Effective size and F-statistics of subdivided populations. II. Dioecious species.

For a dioecious diploid population subdivided into an arbitrary number of subpopulations, we have derived recurrence equations for the inbreeding coefficient and coancestries between individuals within and among subpopulations and formulas for effective size and F-statistics. Stable population size and structure, discrete generations, autosomal inheritance, and the island migration model are assumed, and arbitrary distributions of the numbers of male and female progeny per family, different numbers and variable migration rates of males and females are incorporated in our derivation. Some published equations for effective size and F-statistics for a subdivided population are shown to be incorrect because several incorrect probabilities are used in the derivation. A more general equation for effective size is obtained by finding eigenvalue solutions to the recurrence equations for inbreeding coefficient and coancestry in this article, which reduces to the simple and familiar expressions derived by previous authors for the special case of a single unsubdivided population. Our general expressions for F-statistics also reduce to the classical results of WRIGHT's infinite island model and its extensions. It is shown that population structure is important in determining effective size and F-statistics and should be recognized and incorporated into programs for genetic conservation and evolution.

Biological Evolution↗