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At least 1,639 records · Page 91Linked to original sources
PubMed2005

Statistical error in isothermal titration calorimetry: variance function estimation from generalized least squares.

The method of generalized least squares (GLS) is used to assess the variance function for isothermal titration calorimetry (ITC) data collected for the 1:1 complexation of Ba(2+) with 18-crown-6 ether. In the GLS method, the least squares (LS) residuals from the data fit are themselves fitted to a variance function, with iterative adjustment of the weighting function in the data analysis to produce consistency. The data are treated in a pooled fashion, providing 321 fitted residuals from 35 data sets in the final analysis. Heteroscedasticity (nonconstant variance) is clearly indicated. Data error terms proportional to q(i) and q(i)/v are well defined statistically, where q(i) is the heat from the ith injection of titrant and v is the injected volume. The statistical significance of the variance function parameters is confirmed through Monte Carlo calculations that mimic the actual data set. For the data in question, which fall mostly in the range of q(i)=100-2000 microcal, the contributions to the data variance from the terms in q(i)(2) typically exceed the background constant term for q(i)>300 microcal and v<10 microl. Conversely, this means that in reactions with q(i) much less than this, heteroscedasticity is not a significant problem. Accordingly, in such cases the standard unweighted fitting procedures provide reliable results for the key parameters, K and DeltaH(degrees) and their statistical errors. These results also support an important earlier finding: in most ITC work on 1:1 binding processes, the optimal number of injections is 7-10, which is a factor of 3 smaller than the current norm. For high-q reactions, where weighting is needed for optimal LS analysis, tips are given for using the weighting option in the commercial software commonly employed to process ITC data.

Analysis of Variance↗
PubMed2004

Understanding statistical power in the context of applied research.

Estimates of statistical power are widely used in applied research for purposes such as sample size calculations. This paper reviews the benefits of power and sample size estimation and considers several problems with the use of power calculations in applied research that result from misunderstandings or misapplications of statistical power. These problems include the use of retrospective power calculations and standardized measures of effect size. Methods of increasing the power of proposed research that do not involve merely increasing sample size (such as reduction in measurement error, increasing 'dose' of the independent variable and optimizing the design) are noted. It is concluded that applied researchers should consider a broader range of factors (other than sample size) that influence statistical power, and that the use of standardized measures of effect size should be avoided (except as intermediate stages in prospective power or sample size calculations).

Research↗
PubMedDate not supplied

Statistical prediction of terrestrial gamma radiation dose rate based on geological features and soil types in Kota Tinggi district, Malaysia.

A statistical prediction of terrestrial gamma radiation dose rate has been performed, covering the Kota Tinggi district of Peninsular Malaysia. The prediction has been based on geological features and soil types. The purpose of this study is to provide a methodology to statistically predict the gamma radiation dose rate with minimum surveying in an area. Results of statistical predictions using the hypothesis test were compared with the actual dose rate obtained by measurements.

Forecasting↗
PubMed2005

Nonylphenol induced changes in trophic web structure of plankton analysed by multivariate statistical approaches.

Microcosm studies unveil relevant information in ecotoxicology. However, statistical evaluation of data gained from the different parts of the ecosystem often remain on unsatisfying, discretely level, e.g. separately for different species communities. A combination of different multivariate statistical methods enables an ecological interpretation of the found changes in zoo- and phytoplankton abundances in connection with the physico-chemical variables. To investigate the effects of nonylphenol (NP) on plankton communities, NP was continuously applicated in microcosms by controlled release. Plankton taxa were identified and quantified, various physico-chemical variables were measured. Maximum NP concentration ranged between 29 and 129 microg/L. The most important abiotic variable explaining both zoo- and phytoplankton abundances was NP concentration. NP primarily inhibited Crustaceae, especially juveniles. Cladocera and Copepoda abundances decreased. Rotatoria tend to increase, probably due to a decline of competition and predator pressure. Shifts in phytoplankton structure occurred with a time lag. Aggregating phytoplankton on class level did not show NP effects, whereas aggregating them according to feeding protection strategies did. Thus NP influences phytoplankton at least prevailingly indirectly via zooplankton. Zooplankton shifts led to species composition with different feeding preferences and strategies. Thereby the feeding pressure on phytoplankton changed. NP reorganized the plankton interrelation. Whereas without NP phytoplankton composition was dominated by Rotatoria, in the NP treated microcosms the Crustaceae unveiled highest explanatory power. The found effects were reversible in all but the highest treated microcosm. There are indications that the found effects were caused by endocrine activity of the chemical. Iteratively combining multivariate statistical methods with ecological and toxicological knowledge proved to provide a deeper insight into the mode of ecosystem disturbance by toxic substances.

Animals↗
PubMed2005

Harnessing the power of gene microarrays for the study of brain aging and Alzheimer's disease: statistical reliability and functional correlation.

During normal brain aging, numerous alterations develop in the physiology, biochemistry and structure of neurons and glia. Aging changes occur in most brain regions and, in the hippocampus, have been linked to declining cognitive performance in both humans and animals. Age-related changes in hippocampal regions also may be harbingers of more severe decrements to come from neurodegenerative disorders such as Alzheimer's disease (AD). However, unraveling the mechanisms underlying brain aging, AD and impaired function has been difficult because of the complexity of the networks that drive these aging-related changes. Gene microarray technology allows massively parallel analysis of most genes expressed in a tissue, and therefore is an important new research tool that potentially can provide the investigative power needed to address the complexity of brain aging/neurodegenerative processes. However, along with this new analytic power, microarrays bring several major bioinformatics and resource problems that frequently hinder the optimal application of this technology. In particular, microarray analyses generate extremely large and unwieldy data sets and are subject to high false positive and false negative rates. Concerns also have been raised regarding their accuracy and uniformity. Furthermore, microarray analyses can result in long lists of altered genes, most of which may be difficult to evaluate for functional relevance. These and other problems have led to some skepticism regarding the reliability and functional usefulness of microarray data and to a general view that microarray data should be validated by an independent method. Given recent progress, however, we suggest that the major problem for current microarray research is no longer validity of expression measurements, but rather, the reliability of inferences from the data, an issue more appropriately redressed by statistical approaches than by validation with a separate method. If tested using statistically defined criteria for reliability/significance, microarray data do not appear a priori to require more independent validation than data obtained by any other method. In fact, because of added confidence from co-regulation, they may require less. In this article we also discuss our strategy of statistically correlating individual gene expression with biologically important endpoints designed to address the problem of evaluating functional relevance. We also review how work by ourselves and others with this powerful technology is leading to new insights into the complex processes of brain aging and AD, and to novel, more comprehensive models of aging-related brain change.

Aging↗
PubMed2005

The acquisition of allophonic rules: statistical learning with linguistic constraints.

Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.

Humans↗
PubMed2004

Five-year change in statistical designs of phase II trials published in leading cancer journals.

This study compares the evolution in statistical design reporting for phase II cancer clinical trials published in the six following leading journals: American Journal of Clinical Oncology, Annals of Oncology, British Journal of Cancer, Cancer, European Journal of Cancer and Journal of Clinical Oncology. Only articles where tumour response was considered as the primary endpoint were selected. A total of 393 phase II trials published in 1995 (n=185) and 2000 (n=208) were reviewed. Neither sample size nor design parameters were specified in 157 (85%) and 113 (46%) papers in 1995 and 2000, respectively. 28 (15%) and 95 (46%) papers included at least some information on the statistical designs used: Gehan (4.3% and 3.3%), Fleming (2.2% and 4.3%), and Simon (2.7% and 11.0%). Ad hoc, non-referenced methods were used in 5.9% and 27.3% articles in 1995 and 2000, respectively. Although there is an increase in the mention of at least some statistical design parameters in phase II cancer clinical trials over a 5-year period in these selected cancer journals, the use of referenced methods is still short or often inadequate.

Clinical Trials, Phase II as Topic↗
PubMed2006

Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China.

Multivariate statistical techniques are efficient ways to display complex relationships among many objects. An attempt was made to study the data of trace elements in groundwater using multivariate statistical techniques such as principal component analysis (PCA), Q-mode factor analysis and cluster analysis. The original matrix consisted of 17 trace elements estimated from 55 groundwater samples colleted in 27 wells located in a coastal area in Shenzhen, China. PCA results show that trace elements of V, Cr, As, Mo, W, and U with greatest positive loadings typically occur as soluble oxyanions in oxidizing waters, while Mn and Co with greatest negative loadings are generally more soluble within oxygen depleted groundwater. Cluster analyses demonstrate that most groundwater samples collected from the same well in the study area during summer and winter still fall into the same group. This study also demonstrates the usefulness of multivariate statistical analysis in hydrochemical studies.

Anions↗
PubMed2006

On statistical analysis of forensic DNA: theory, methods and computer programs.

Statistics plays an important role in evaluating the evidential weight of forensic DNA. In this paper, general statistical principles for forensic DNA analysis are presented. We introduce the theory and methods for the statistical assessment in kinship determination and DNA mixture evaluation. In particular, analytical formulas for testing for biological relationship among three individuals and for assessing the DNA mixture evidence in the case of multiple subdivided ethnic groups are developed. Two user-friendly computer programs are demonstrated to exhibit their wide applicability in tackling with complex kinship/paternity and mixture problems. The EasyDNA program can solve a complicated paternity case in 1 min.

DNA↗
PubMed2005

Statistical issues in clinical trials that involve the double-blind, placebo-controlled food challenge.

The double-blind, placebo-controlled food challenge is a rigorous tool that has become popular for evaluating adverse reactions to foods. The standard use of the double-blind, placebo-controlled food challenge has been to document food allergies for individual patients, but it recently has been gaining acceptance as a procedure for investigating the effectiveness of therapies to prevent/minimize food-induced anaphylaxis. The purpose of this study was to describe the statistical design and analysis issues for clinical trials that use the double-blind, placebo-controlled food challenge in measuring sensitivity to food allergens. Nonparametric tests for within-group and between-group comparisons are described, as well as a discrete-time survival analysis. The statistical methods are applied to simulated data from a clinical trial that compares control therapy and experimental therapy groups. The results indicate that the experimental therapy is significantly better than control in improving the tolerance to peanut flour in patients with peanut allergy. Although simple nonparametric tests for within-group and between-group comparisons are easy to apply, a discrete-time survival analysis provides the best approach because of its flexibility in accounting for important independent variables (regressors) and longitudinal data. Statistical software packages can be adapted to perform such analyses.

Anaphylaxis↗
PubMed2005

137Cs in freshwater fish in Finland since 1986--a statistical analysis with multivariate linear regression models.

The accident at the Chernobyl nuclear power plant in 1986 significantly elevated the 137Cs levels of fish in Finnish lakes. About 6200 fish samples from 390 lakes comprising 20 species have been analysed for 137Cs since 1986. The sizes of the lakes varied from a few hectares to about 1000 km2. Activity concentrations of 137Cs in fish still varied widely in 2003, from 16 to 6400 Bq/kg fresh weight. This paper presents the results of statistical analyses with multivariate linear regression models carried out on the empirical data collected since 1986. The statistical analysis resulted in separate models for two time periods describing temporal changes of 137Cs in fish. The explanatory variables were fish species with various feeding habits, the size class of the lake, municipal division, drainage area, time since the deposition and deposition level of the municipality. The calculated values for 137Cs in fish did not differ statistically significantly from the observed values in the validation data. The explanatory variables explained 58% (the first time period) and 72% (the second time period) of the total variability of 137Cs in fish.

Animal Feed↗
PubMed2005

On statistical tests of phylogenetic tree imbalance: the Sackin and other indices revisited.

We investigate the distribution of statistical measures of tree imbalance in large phylogenies. More specifically, we study normalized versions of the Sackin's index and the number of subtrees of given sizes. Using the connection with structures from theoretical computer science, we provide precise description for the limiting distribution under the null hypothesis of Yule trees. Corrected p-values are then computed, and the statistical power of these statistics for testing the Yule model against a model of biased speciation is evaluated from simulations. As an illustration, the tests are applied to the HIV-1 reconstructed phylogeny.

Acquired Immunodeficiency Syndrome↗
PubMed2006

Sampling properties of homozygosity-based statistics for linkage disequilibrium.

Homozygosity-based statistics such as Ohta's identity-in-state (IIS) excess offer the potential to measure linkage disequilibrium for multiallelic loci in small samples. However, previous observations have suggested that for independent loci, in small samples these statistics might produce values that more frequently lie on one side rather than on the other side of zero. Here we investigate the sampling properties of the IIS excess. We find that for any pair of independent polymorphic loci, as sample size n approaches infinity, the sampling distribution of the IIS excess approaches a normal distribution. For large samples, the IIS excess tends towards symmetry around zero, and the probabilities of positive and of negative IIS excess both approach 1/2. Surprisingly, however, we also find that for sufficiently large n, independent loci can be chosen so that the probability of a sample having positive IIS excess is arbitrarily close to either 0 or 1. The results are applied to interpretation of data from human populations, and we conclude that before employing homozygosity-based statistics to measure LD in a particular sample, especially for loci with either very small or very large homozygosities, it is useful to verify that loci with the observed homozygosity values are not likely to produce a large bias in IIS excess in samples of the given size.

Algorithms↗
PubMed2004

Optimized prostate biopsy via a statistical atlas of cancer spatial distribution.

A methodology is presented for constructing a statistical atlas of spatial distribution of prostate cancer from a large patient cohort, and it is used for optimizing needle biopsy. An adaptive-focus deformable model is used for the spatial normalization and registration of 100 prostate histological samples, which were provided by the Center for Prostate Disease Research of the US Department of Defense, resulting in a statistical atlas of spatial distribution of prostate cancer. Based on this atlas, a statistical predictive model was developed to optimize the needle biopsy sites, by maximizing the probability of detecting cancer. Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples.

Algorithms↗
PubMed2006

Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.

Quantitative diffusion tensor imaging (DTI) has become the major imaging modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics of tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.

Algorithms↗
PubMed2004

Statistical parametric mapping for event-related potentials: I. Generic considerations.

In this paper, we frame the strategy and motivations behind developments in statistical parametric mapping (SPM) for the analysis of electroencephalogram (EEG) data. This work deals specifically with SPM procedures for the analysis of event-related potentials (ERP). We place these developments in the larger context of integrating electrophysiological and hemodynamic measurements of evoked brain responses through the fusion of EEG and fMRI data. In this paper, we consider some fundamental issues when selecting an appropriate statistical model that enables diverse questions to be asked of the data and at the same time retains maximum sensitivity. The three key issues addressed in this paper are as follows: (i) should multivariate or mass univariate analyses be adopted, (ii) should time be treated as an experimental factor or as a dimension of the measured response variable, and (iii) how to form appropriate explanatory variables in a hierarchical observation model. We review the relative merits of the different options and explain the rationale for our choices. In brief, we motivate a mass univariate approach in terms of sensitivity to region-specific responses. This involves modeling responses at each voxel or space bin separately. In contradistinction, we treat time as an experimental factor to enable inferences about temporally distributed responses that encompass multiple time bins. In a companion paper, we develop statistical models of ERPs in the time domain that follow from the heuristics established here and illustrate the approach using simulated and real data.

Analysis of Variance↗
PubMed2004

Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model.

In this paper, we describe a temporal model for event-related potentials (ERP) in the context of statistical parametric mapping (SPM). In brief, we project channel data onto a two-dimensional scalp surface or into three-dimensional brain space using some appropriate inverse solution. We then treat the spatiotemporal data in a mass-univariate fashion. This implicitly factorises the model into spatial and temporal components. The key contribution of this paper is the use of observation models that afford an explicit distinction between observation error and variation in the expression of ERPs. This distinction is created by employing a two-level hierarchical model, in which the first level models the ERP effects within-subject and trial type, while the second models differences in ERP expression among trial types and subjects. By bringing the analysis of ERP data into a classical hierarchical (i.e., mixed effects) framework, many apparently disparate approaches (e.g., conventional P300 analyses and time-frequency analyses of stimulus-locked oscillations) can be reconciled within the same estimation and inference procedure. Inference proceeds in the normal way using t or F statistics to test for effects that are localised in peristimulus time or in some time-frequency window. The use of F statistics is an important generalisation of classical approaches, because it allows one to test for effects that lie in a multidimensional subspace (i.e., of unknown but constrained form). We describe the analysis procedures, the underlying theory and compare its performance to established techniques.

Analysis of Variance↗
PubMed2005

Quantile estimation to derive optimized test thresholds for random field statistics.

We present a numerical method to estimate the true threshold values in random fields needed to determine the significance of apparent signals observed in noisy images. To accomplish this, a quantile estimation algorithm is applied to derive the threshold with a predefined confidence interval from a large number of simulated random fields. Also, a computationally efficient method for generating a random field simulation is presented using resampling techniques. Applying these techniques, thresholds have been determined for a large variety of parameter settings (smoothness, voxel size, brain shape, type of statistics). By means of interpolation techniques, thresholds for additional arbitrary settings can be quickly derived without the need to run individual simulations. Compared to the parametric approach of Worsley et al. (1996) (Worsley, K.J., Marrett, S., Neelin P., Vandal, A.C., Friston, K.J., Evans, A.C., 1996. A unified statistical approach for determining significant signals in images of cerebral activation. Hum. Brain Mapp. 4, 58-73) and Friston et al. (1991) (Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, R.S. 1991. Comparing functional (PET) images: the assessment of significant change. J. Cereb. Blood Flow Metab. 11(4), 690-699), and to the Bonferroni approach, these optimized thresholds lead to higher levels of significance (i.e., lower p values) with a specific amount of activation especially with fields of moderate smoothness (i.e., with a relative full width half maximum between 2 and 6). Alternatively, the threshold for a specified level of significance can be lowered. This improved statistical sensitivity is illustrated by the analysis of an actual event related functional magnetic resonance data set, and its limitations are tested by determining the false positive rate with experimental MR noise data. The grid of estimated threshold values as well as the interpolation algorithm to derive thresholds for arbitrary parameter settings are made available over the internet (http://neuro2.med.uni-magdeburg.de/quantile_estimation).

Algorithms↗