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At least 1,027 records · Page 57Linked to original sources
PubMed2006

Joint spatial analysis of gastrointestinal infectious diseases.

A major obstacle in the spatial analysis of infectious disease surveillance data is the problem of under-reporting. This article investigates the possibility of inferring reporting rates through joint statistical modelling of several infectious diseases with different aetiologies. Once variation in under-reporting can be estimated, geographic risk patterns for infections associated with specific food vehicles may be discerned. We adopt the shared component model, proposed by Knorr-Held and Best for two chronic diseases and further extended by (Held L, Natario I, Fenton S, Rue H, Becker N. Towards joint disease mapping. Statistical Methods in Medical Research 2005b; 14: 61-82) for more than two chronic diseases to the infectious disease setting. Our goal is to estimate a shared component, common to all diseases, which may be interpreted as representing the spatial variation in reporting rates. Additional components are introduced to describe the real spatial variation of the different diseases. Of course, this interpretation is only allowed under specific assumptions, in particular, the geographical variation in under-reporting should be similar for the diseases considered. In addition, it is vital that the data do not contain large local outbreaks, so adjustment based on a time series method recently proposed by (Held L, Höhle M, Hofmann M. A statistical framework for the analysis of multivariate infectious disease surveillance data. Statistical Modelling 2005a; 5: 187-99) is made at a preliminary stage. We will illustrate our approach through the analysis of gastrointestinal diseases notification data obtained from the German infectious disease surveillance system, administered by the Robert Koch Institute in Berlin.

Adolescent↗
PubMed2006

Implant surface roughness and bone healing: a systematic review.

A systematic review was performed on studies investigating the effects of implant surface roughness on bone response and implant fixation. We searched the literature using MEDLINE from 1953 to 2003. Inclusion criteria were: (1) abstracts of animal studies investigating implant surface roughness and bone healing; (2) observations of three-month bone healing, surface topography measurements, and biomechanical tests; (3) provision of data on surface roughness, bone-to-implant contact, and biomechanical test values. The literature search revealed 5966 abstracts. There were 470, 23, and 14 articles included in the first, second, and third selection steps, respectively. Almost all papers showed an enhanced bone-to-implant contact with increasing surface roughness. Six comparisons were significantly positive for the relationship of bone-to-implant contact and surface roughness. Also, a significant relation was found between push-out strength and surface roughness. Unfortunately, the eventually selected studies were too heterogeneous for inference of data. Nevertheless, the statistical analysis on the available data provided supportive evidence for a positive relationship between bone-to-implant contact and surface roughness.

Animals↗
PubMed2003

Expression profiling using cDNA microarrays.

Microarray technology has become increasingly useful in measuring expression levels of a large number of genes and part of a repertoire of functional genomic tools. We describe the methods of cDNA microarray preparation, the use, data collection, and initial data processing. The cDNA fragments are first prepared by polymerase chain reaction (PCR), and then attached to a solid substrate, such as a chemically treated glass slide. Robotic machines spot the prepared cloned cDNA samples in a miniaturized gridded pattern, so that nanoliter amounts of tens of thousands cDNA samples are bound to a single 7.5 x 2.5 cm glass slide. Probes are generated from RNA samples of test and control tissues by incorporating Cyanine dyes (Cy3 or Cy5) in reverse-transcribed products. Probes from a test sample are labeled with one of two Cy dyes and mixed in equal amounts with probes from a control sample labeled with the second dye. The glass slides containing the cDNA microarray are hybridized with the mixed Cy-labeled probes, washed, dried, and scanned using laser scanners with an optimized wavelength to excite each Cy dye. The emission image patterns for each dye are captured by a digital camera using micro-optics and processed into numerical values that positively correlate with quantitative levels of mRNA for each cDNA spot on the slide. The collected data is then further processed, normalized across experiments, and examined via numerous statistical and mathematical approaches to infer changes in expression levels of particular genes due to the treatment tested.

DNA, Complementary↗
PubMed2005

Comparing bacterial DNA microarray fingerprints.

Epidemiologic and forensic investigations often require assays to detect subtle genetic differences between closely related microorganisms. Typically, gel electrophoresis is used to compare randomly amplified DNA fragments between microbial samples, where the patterns of DNA fragment sizes are viewed as genotype 'fingerprints'. The limited genomic sample captured on a gel, however, is not always sufficient to discriminate closely related strains. This paper examines the application of microarray technology to DNA fingerprinting as a high-resolution alternative to gel-based methods. The so-called universal microarray, which uses short oligonucleotide probes that do not target specific genes or species, is intended to be applicable to all microorganisms because it does not require prior knowledge of genomic sequence. In principle, closely related strains can be distinguished if enough independent oligonucleotide probes are used on the microarray, i.e., if the genome is sufficiently sampled. In practice, we confront noisy data, imperfectly matched hybridizations, and a high-dimensional inference problem. We describe the statistical problems of microarray fingerprinting, outline similarities with and differences from more conventional microarray applications, and illustrate a statistical measurement error model to fingerprint 10 closely related strains from three Bacillus species, and 3 strains from non-Bacillus species.

Journal Article↗
PubMed1998

[Evaluation of selected rehabilitation procedures for pain syndrome caused by cervical spine instability].

In the examined group of 71 patients with pain syndrome caused by instability of the cervical spine, an attempt was made to evaluate the impact of rehabilitation on the alleviation of complaints and symptoms, and to indicate the effectiveness of selected physicotherapeutical procedures used in this rehabilitation. From the examinations performed and their statistical analysis it can be inferred that there is significant improvement in the alleviation of complaints and symptoms when rehabilitation is used. The need also becomes apparent to provide a set of recommendations to be observed in daily and professional life. The best results in the alleviation of complaints and symptoms were achieved by patients in whom laser biostimulation was used as the basic physicotherapeutical procedure in rehabilitation.

Adult↗
PubMed2002

Genomic and expression analysis of the 12p11-p12 amplicon using EST arrays identifies two novel amplified and overexpressed genes.

We performed parallel array comparative genomic hybridization and array expression analysis of the 12p11-p12 amplicon in human testicular seminomas and an ovarian carcinoma cell line using an expressed sequence tags (ESTs) array spotted with 8254 ESTs. The data were normalized using a robust statistical modeling and the significance inferred from the local SD. We identified two ESTs within the chromosomal amplicon that were amplified and overexpressed in > or =75-100 percent of analyzed tumors with the 12p11-p12 amplicon. These sequences, belonging to coding regions of two novel genes designated here as GCT1 and GCT2, were broadly expressed in a panel of human tissues, including testis and ovary. GCT1 and GCT2 were overexpressed in 92 and 71 percent, respectively, of a panel of seminomas tested. Combined array comparative genomic hybridization and array expression analysis is a valid approach for gene discovery in large chromosomal amplicons.

Aged↗
PubMed1992

Comparison of 5.8S ribosomal DNA sequences among the basidiomycetous yeast genera Cystofilobasidium, Filobasidium and Filobasidiella.

Nucleotide sequences obtained from regions of the ribosomal DNA repeat were compared by phylogenetic methods and combined with a statistical evaluation to clarify the relationships among the genera Cystofilobasidium, Filobasidium (F.) and Filobasidiella (Fl.), to assess the affinity of Filobasidiella neoformans and Filobasidiella depauperata, and to compare the varieties of Fl. neoformans. With appropriate primers, the nuclear 18S, 5.8S and internal transcribed spacer (ITS) regions of the ribosomal RNA genes (rDNA) of 10 strains were amplified with the polymerase chain reaction. The resulting DNA products were compared by digestion with endonucleases and analysis of restriction fragments. Single strands of the 5.8S rDNA and ITS regions were subsequently sequenced by the dideoxy method. Statistical support for the phylogeny inferred from parsimony analysis of aligned 5.8S rDNA sequences was determined by bootstrapping. There were no nucleotide substitutions in this region, nor in the ITS, among strains of Fl. neoformans that differ in variety and serotype. There was strong support for retaining Fl. depauperata and Fl. neoformans in the same genus, but nucleotide substitutions can be used to distinguish the two species. There was no support for combining the genera Filobasidium, Filobasidiella or Cystofilobasidium.

Base Sequence↗
PubMedDate not supplied

Can't miss: conquer any number task by making important statistics simple. Part 4. Confidence intervals with t distributions, standard error, and confidence intervals for proportions.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies, all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. The statistical principles are equally applicable to quality improvement and healthcare research studies. This article, Part 4, starts with a review of the information contained in Parts 1, 2, and 3, which appeared in the July/August 2003 issue of the Journal for Healthcare Quality. This article describes t distributions and how these are used to calculate confidence intervals for estimating a population mean based on a sample mean of a continuous variable. Part 4 concludes with a discussion of standard error, margin of error, and confidence intervals for estimating a population proportion based on a sample proportion from a binomial variable.

Confidence Intervals↗
PubMedDate not supplied

Can't miss: conquer any number task by making important statistics simple. Part 5. Comparing two confidence intervals, standard error of the difference between two means and between two proportions.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. This article, Part 5, demonstrates the comparison of two confidence intervals as a method for estimating the difference between two population means. The concept of the standard error of the difference between two sample means is presented along with the confidence interval for estimating the difference between two population means. The article concludes with the standard error and confidence interval for estimating the difference between two population proportions from a binomial variable.

Confidence Intervals↗
PubMedDate not supplied

Can't miss: conquer any number task by making important statistics simple. Part 6. Tests of statistical significance (z test statistic, rejecting the null hypothesis, p value), t test, z test for proportions, statistical significance versus meaningful difference.

Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. This article, Part 6, merges the four concepts of the (1) standard error of the difference between sample means, (2) the z test statistic, (3) rejecting the null hypothesis, and (4) the p value to provide a comprehensive view of tests of statistical significance. This is followed by a description of t tests, statistical tests for comparing two sample proportions, and Type I and Type II errors. The series of articles concludes with a description of statistical significance versus meaningful difference.

Confidence Intervals↗
PubMed1996

Modeling for intergroup comparisons of imaging data.

Intergroup comparisons pose unique challenges in the analysis of functional imaging data. Imperfections in intersubject stereotaxis can give rise to artifactual results and make it particularly important to allow for intersubject differences in task-related changes when formulating statistical models. Because intergroup comparisons generally involve inferences about the populations from which the subjects were drawn rather than inferences about the particular subjects themselves, subjects must be treated as random rather than fixed effects in the statistical model. These requirements, when combined with the need to adjust for multiple spatial comparisons, result in low statistical power when the number of subjects in each group is small. Functional imaging studies to identify differences between groups generally require many more subjects than other types of functional imaging studies and require careful advance planning to maximize the likelihood of reaching meaningful conclusions.

Brain Mapping↗
PubMed2003

Global analysis of cellular transcription following infection with an HIV-based vector.

We have examined the changes in cellular transcription resulting from infection with HIV-based vectors. Previous work suggested that the incoming viral genome may under some circumstances be detected as DNA damage, so to explore this possibility, we compared the transcriptional response to infection with an HIV-based vector to the response to treatment with the DNA-damaging agent etoposide. Expression levels of about 12,000 cellular RNA transcripts were determined in a human B-cell line at different times after either treatment. Statistical analysis revealed that the infection with the lentivirus vector resulted in quite modest changes in gene expression. Treatment with etoposide, in contrast, caused drastic changes in expression of genes known or inferred to be involved in apoptosis. Statistically significant though subtle parallels in the cellular transcriptional responses to etoposide treatment and HIV-vector infection could be detected. Several further data sets analyzing infections with HIV-based vectors or wild-type HIV-1 showed similar modest effects on cellular transcription and very modest parallels among different data sets. These findings establish that HIV-vector or HIV-1 infection has remarkably little effect on cellular transcription. The statistical methods described here may be of wide use in mining microarray data sets. Our observations support the idea that gene therapy with HIV-based vectors should not be particularly toxic to cells due to disruption of cellular transcription.

B-Lymphocytes↗
PubMed2026

TL-HDMR: a transfer learning framework for advancing equitable causal inference reveals metabolic signatures of stroke across multiple ancestries.

The limited genetic diversity in genome-wide association studies (GWAS) poses a significant challenge to the generalizability and equity of biomedical discoveries. Most causal inferences, particularly from high-dimensional phenomes (e.g. metabolomics), are primarily based on European populations, and their applicability to other ancestries remains uncertain. Traditional multivariable Mendelian randomization (MVMR) methods further struggle in high-dimensional and correlated settings due to collinearity and model instability. To bridge this gap, we present a two-step transfer learning framework for high-dimensional MR (TL-HDMR), designed to enhance causal exposure detection in understudied populations. Our approach leverages the Minimax Concave Penalty for asymptotically unbiased estimation amidst exposure correlations. Crucially, we introduce two novel pre-transfer procedures-HDMR.TSD for sourcing beneficial data and HDMR.PRESSO for filtering pleiotropic instruments-to ensure robust knowledge transfer. Extensive simulations demonstrated TL-HDMR's superior performance in ROC curves and mean absolute error over alternative methods. When applied to identify causal metabolites for stroke across multi-ancestry cohorts (European, East Asian, South Asian, and African), TL-HDMR successfully pinpointed both shared and ethnic-specific causal biomarkers, showcasing its unique capability for equitable causal inference. This work provides a powerful statistical tool that not only addresses critical methodological challenges but also promotes inclusivity and fairness in human health research.

Humans↗
PubMed2005

Genetical genomics analysis of a yeast segregant population for transcription network inference.

Genetic analysis of gene expression in a segregating population, which is expression profiled and genotyped at DNA markers throughout the genome, can reveal regulatory networks of polymorphic genes. We propose an analysis strategy with several steps: (1) genome-wide QTL analysis of all expression profiles to identify eQTL confidence regions, followed by fine mapping of identified eQTL; (2) identification of regulatory candidate genes in each eQTL region; (3) correlation analysis of the expression profiles of the candidates in any eQTL region with the gene affected by the eQTL to reduce the number of candidates; (4) drawing directional links from retained regulatory candidate genes to genes affected by the eQTL and joining links to form networks; and (5) statistical validation and refinement of the inferred network structure. Here, we apply an initial implementation of this strategy to a segregating yeast population. In 65, 7, and 28% of the identified eQTL regions, a single candidate regulatory gene, no gene, or more than one gene was retained in step 3, respectively. Overall, 768 putative regulatory links were retained, 331 of which are the strongest candidate links, as they were retained in the expression correlation analysis and were located within or near an eQTL subregion identified by a multimarker analysis separating multiple linked QTL. One or several biological processes were statistically significantly overrepresented in independent network structures or in highly interconnected subnetworks. Most of the transcription factors found in the inferred network had a putative regulatory link to only one other gene or exhibited cis-regulation.

DNA↗
PubMed1999

Application of Bayesian inference to fMRI data analysis.

The methods of Bayesian statistics are applied to the analysis of fMRI data. Three specific models are examined. The first is the familiar linear model with white Gaussian noise. In this section, the Jeffreys' Rule for noninformative prior distributions is stated and it is shown how the posterior distribution may be used to infer activation in individual pixels. Next, linear time-invariant (LTI) systems are introduced as an example of statistical models with nonlinear parameters. It is shown that the Bayesian approach can lead to quite complex bimodal distributions of the parameters when the specific case of a delta function response with a spatially varying delay is analyzed. Finally, a linear model with auto-regressive noise is discussed as an alternative to that with uncorrelated white Gaussian noise. The analysis isolates those pixels that have significant temporal correlation under the model. It is shown that the number of pixels that have a significantly large auto-regression parameter is dependent on the terms used to account for confounding effects.

Artifacts↗
PubMed2025

Inferring the sensitivity of wastewater metagenomic sequencing for early detection of viruses: a statistical modelling study.

BACKGROUND: Metagenomic sequencing of wastewater (W-MGS) can in principle detect any known or novel pathogen in a population. We aimed to quantify the sensitivity and cost of W-MGS for viral pathogen detection by jointly analysing W-MGS and epidemiological data for a range of human-infecting viruses. METHODS: In this statistical modelling study, we analysed sequencing data from four studies of untargeted W-MGS to estimate the relative abundance of 11 human-infecting viruses. Corresponding prevalence and incidence estimates were obtained or calculated from academic and public health reports. We combined these estimates using a hierarchical Bayesian model to predict relative abundance at set prevalence or incidence values, allowing comparison across studies and viruses. These predictions were then used to estimate the sequencing depth and concomitant cost required for pathogen detection using W-MGS with or without use of a hybridisation capture enrichment panel. FINDINGS: After controlling for variation in local infection rates, relative abundance varied by orders of magnitude across studies for a given virus. For instance, a local SARS-CoV-2 weekly incidence of 1% corresponded to a predicted SARS-CoV-2 relative abundance ranging from 3·8 × 10-10 to 2·4 × 10-7 across studies, translating to orders-of-magnitude variation in the cost of operating a system able to detect a SARS-CoV-2-like pathogen at a given sensitivity. Use of a respiratory virus enrichment panel in two studies greatly increased predicted relative abundance of SARS-CoV-2, lowering yearly costs by 27-fold (from US$7·87 million to $287 000) and 29-fold (from $1·98 million to $69 100) for a system able to detect a SARS-CoV-2-like pathogen before reaching 0·01% cumulative incidence. INTERPRETATION: The large variation in viral relative abundance after controlling for epidemiological factors indicates that other sources of inter-study variation, such as differences in sewershed hydrology and laboratory protocols, have a substantial impact on the sensitivity and cost of W-MGS. Well chosen hybridisation capture panels can greatly increase sensitivity and reduce cost for viruses in the panel, but might reduce sensitivity to unknown or unexpected pathogens. FUNDING: The Wellcome Trust, Open Philanthropy, and Musk Foundation.

Humans↗
PubMed1997

New method for comparing levels of microdifferentiation: application to migration matrices of two populations from the Basque Country (Spain).

Population microdifferentiation rates can be obtained by various coefficients. Despite differences in the formulations of these coefficients, the obtained values lead to convergent empirical interpretations. One of the most widely used coefficients is Wright's FST. Insofar as all these statistics depend on the number of subpopulations r and their effective size Ne, they are far from easy to compare and interpret. Here, we propose a new method for comparing microdifferentiation rates: a measurement referred to as -log(p), inferred from the relationship between the statistics FST and chi 2. The most interesting quality of -log(p) is that it is independent of r and Ne so that its use facilitates comparisons between populations with different characteristics. Using parent-offspring migration matrices, we estimated the values of -log(p) for two populations in the Basque Country: Lanciego and Orozco. The obtained values were compared with the -log(p) for other world populations whose microdifferentiation rates had already been calculated by other researchers using different data sources. Both Basque populations showed low -log(p) values, as did most of the considered continental populations, whereas island and nonmodern populations showed higher values. This implies microdifferentiation only in island and nonmodern populations, although the secular trend of this process in some populations, such as the Aland Islanders and the Papago, leads to isolation breakdowns.

Censuses↗
PubMed2000

Conditional and exact tests in crossover trials.

Generalized linear models are developed for crossover trials with no carryover effects and fixed subject effects. A general multinominal model for the distribution of data is considered. This subsumes both binary and categorical data. Conditional inferences eliminate subject effects by conditioning on their sufficient statistics. For normal data, least-squares analysis is exact with identical treatment inferences from unconditional and conditional analyses. For Poisson data, unconditional and conditional analyses are also identical, but for multinomial data this is not the case and the unconditional analysis is invalid. For multinomial data, asymptotic tests of both treatment effects and goodness of fit are unreliable with small samples. Procedures for exact tests are developed to overcome such problems, using enumeration, random sampling, and a hybrid of importance sampling and enumeration. A four-period binary crossover trial is used to illustrate an exact test of treatment effects by a two-stage sampling procedure based on a factorization of the conditional distribution of the sufficient statistics. An exact test of goodness of fit on the same data illustrates a two-stage scheme mixing importance sampling and enumeration.

Binomial Distribution↗