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PubMed2001

Statistical methods for the meta-analysis of cluster randomization trials.

Cluster randomization trials have become a very attractive research strategy, particularly for the evaluation of health service interventions. The need to conduct meta-analyses of such trials is also becoming more common. However, as with cluster randomization trials in general, such analyses raise special methodologic challenges. In this paper, we discuss and illustrate several statistical approaches that might be applied to a meta-analysis of cluster randomization trials, each of which has a binary endpoint. Statistical methods for constructing inferences for a summary intervention odds ratio include those based on Mantel-Haenszel procedures, the ratio estimator approach, Woolf procedures and generalized estimating equations using robust variance estimation. The advantages and disadvantages of each method are discussed in the context of an example.

Cluster Analysis↗

PubMed2001

Contextual clustering for analysis of functional MRI data.

We present a contextual clustering procedure for statistical parametric maps (SPM) calculated from time varying three-dimensional images. The algorithm can be used for the detection of neural activations from functional magnetic resonance images (fMRI). An important characteristic of SPM is that the intensity distribution of background (nonactive area) is known whereas the distributions of activation areas are not. The developed contextual clustering algorithm divides an SPM into background and activation areas so that the probability of detecting false activations by chance is controlled, i.e., hypothesis testing is performed. Unlike the much used voxel-by-voxel testing, neighborhood information is utilized, an important difference. This is achieved by using a Markov random field prior and iterated conditional modes (ICM) algorithm. However, unlike in the conventional use of ICM algorithm, the classification is based only on the distribution of background. The results from our simulations and human fMRI experiments using visual stimulation demonstrate that a better sensitivity is achieved with a given specificity in comparison to the voxel-by-voxel thresholding technique. The algorithm is computationally efficient and can be used to detect and delineate objects from a noisy background in other applications.

Algorithms↗

PubMed2004

Energy density analysis of cluster size dependence of surface-molecule interactions: H2, C2H2, C2H4, and CO adsorption onto Si(100)-(2x1) surface.

Adsorption of H2, C2H2, C2H4, and CO onto a Si(100)-(2x1) surface has been treated theoretically using Si(12n - 3)H(8n + 4) (n = 1-4) clusters. The energy density analysis (EDA) proposed by Nakai has been adopted to examine surface-molecule interactions for different cluster sizes. EDA results for the largest model cluster Si45H36 have shown that the adsorption-induced energy density variation in Si atoms decays with distance from the adsorption site. Analysis of this decay, which can be carried out using the EDA technique, is important because it enables verification of the reliability of the model cluster used. In the cases of H2, C2H2, C2H4, and CO adsorption onto the Si(100)-(2x1) surface, it is found that at least a Si21H20 cluster is necessary to treat the surface-molecule interaction with chemical accuracy.

Journal Article↗

PubMed2005

Comparison of statistical methods for analysis of clustered binary observations.

When correlated observations are obtained in a randomized controlled trial, the assumption of independence among observations within cluster likely will not hold because the observations share the same cluster (e.g. clinic, physician, or subject). Further, the outcome measurements of interest are often binary. The objective of this paper is to compare the performance of four statistical methods for analysis of clustered binary observations: namely (1) full likelihood method; (2) penalized quasi-likelihood method; (3) generalized estimating equation method; (4) fixed-effects logistic regression method. The first three methods take correlations into account in inferential processes whereas the last method does not. Type I error rate, power, bias, and standard error are compared across the four statistical methods through computer simulations under varying effect sizes, intraclass correlation coefficients, number of clusters, and number of observations per cluster, including large numbers 20 and 100 of observations per cluster. The results show that the performance of the full likelihood and the penalized quasi-likelihood methods is superior for analysis of clustered binary observations, and is not necessarily inferior to that of the fixed-effects logistic regression fit even when within-cluster correlations are zero.

Cluster Analysis↗

PubMed2004

A new approach for evaluating flexible working hours.

Recent studies on flexible working hours show at least some of these working time arrangements seem to be associated with impairing effects of health and well-being. According to available evidence, variability of working hours seems to play an important role. The question, however, is how this variability can be assessed and used to explain or predict impairments. Based on earlier methods used to assess shift-work effects, a time series analysis approach was applied to the matter of flexible working hours. Data on the working hours of 4 week's length of 137 respondents derived from a survey on flexible work hours involving 15 companies of different production and service sectors in Germany were converted to time series and analyzed by spectral analysis. A cluster analysis of the resulting power spectra yielded 5 clusters of flexible work hours. Analyzing these clusters for differences in reported impairments showed that workers who showed suppression of circadian and weekly rhythms experienced severest impairments, especially in circadian controlled functions like sleep and digestion. The results thus indicate that analyzing the periodicity of flexible working hours seems to be a promising approach for predicting impairments which should be investigated further in the future.

Circadian Rhythm↗

PubMed2006

Energy density analysis of cluster size dependence of surface-molecule interactions (II): formate adsorption onto a Cu(111) surface.

Adsorption of formate (HCOO) onto a Cu(111) surface has been treated theoretically using 18 kinds of Cu(n) (6 < or = n < or = 56) clusters. The energy density analysis (EDA) proposed by Nakai has been adopted to examine surface-molecule interactions for different cluster sizes. EDA results for the largest model cluster Cu(56) have shown that the adsorption-induced energy density variation in Cu atoms decays with distance from the adsorption site. Analysis of this decay, which can be carried out using the EDA technique, is important because it enables verification of the reliability of the model cluster used. In the case of formate adsorption onto the Cu(111) surface, it is found that at least a four-layer model cluster is necessary to treat the surface-molecule interaction with chemical accuracy.

Journal Article↗

PubMed2002

Inability to find consistent bacterial biocontrol agents of Pythium aphanidermatum in cucumber using screens based on ecophysiological traits.

A collection of 821 rhizobacteria from cucumber, originating from different root locations and stages of plant development, was screened for potential biocontrol agents of Pythium aphanidermatum (Edson) Fitzp. The screening procedure exploited carbon source utilization profiles and growth rates of bacteria as indicators of a partial niche overlap with the pathogen. The bacteria were tested for growth on nine carbon sources (glucose, fucose, sucrose, maltose, asparagine, alanine, galacturonic acid, succinic acid, and linoleic acid), most of which are reported to be used by the zoospores of P. aphanidermatum in the infection process. The isolates were classified as fast- or slow-growing, depending on their growth rate in 1/10 strength TSB. By nonhierarchical cluster analysis, 20 clusters were generated of bacteria with similar profiles of carbon source utilization. Redundancy analysis showed that the type of root sample explained 47% of the variance found in the relative abundance of bacteria from the clusters. Bacteria from clusters using none or few of the carbon sources, e.g., maltose and linoleic acid, with many slow-growing isolates, showed a preference for plants in the vegetative or generative stage, or for old root regions (root base). Bacteria from clusters with fast-growing isolates, using many carbon sources, were relatively abundant in the seedling stage. A selection of 127 bacteria from the different clusters was tested for disease suppressive capabilities in bioassays on young cucumber plants in nutrient solution, inoculated with zoospores of P. aphanidermatum. Nine of these bacteria produced biosurfactants, and 27 showed antibiosis against mycelial growth in plate assays. For 31 isolates, significant positive effects on plant biomass were shown, as analyzed with a general linear regression model. For most isolates, these effects occurred only in one of two replicate assays and no reductions in the degree of root and crown rot were found. Of the isolates that used many of the tested carbon sources, only four had positive effects on plant biomass. The majority of the isolates that positively affected plant biomass used few to moderate numbers of carbon sources and did not produce antibiotics or biosurfactants. In conclusion, competition for the tested carbon sources with the zoospores did not play a decisive role in disease suppression, and no clear relation was found between ecophysiological traits and disease suppression. Only isolate 3.1T8, isolated from root tips in the generative stage of plant growth, significantly increased plant biomass and suppressed root and crown rot symptoms in five out of six bioassays. The isolate produced an antifungal substance in plate assays and showed biosurfactant production in several (cucumber-derived) media.

Bacteria↗

PubMed1989

Is major depressive disorder in adolescence a distinct diagnostic entity?

The concept of major depressive disorder in childhood and adolescence is reviewed and it is suggested that contemporary enthusiasm for this diagnosis may have outrun the evidence that it is a distinct categorical entity. To test the hypothesis that major depression is not a qualitatively distinct disorder in adolescence, but rather a continuously distributed, noncategorical syndrome, the behavioral rating scales (CBCL-P) of 216 hospitalized adolescent patients were analyzed first by principal components analysis and then by cluster analysis. Three behavioral syndromes were isolated by principal components analysis. Of three groups of patients identified by a subsequent cluster analysis, one was consistent with the concept of a categorically distinct "nuclear" depression. However, a noncategorical continuously distributed depressive syndrome appears to affect a larger number of patients in this age group, and the "nuclear" disorder may be less prevalent than is currently assumed. One explanation of these findings would combine a categorical model of nuclear depression with a dimensional model of dysthymia.

Adjustment Disorders↗

PubMed2005

Regression-based association analysis with clustered haplotypes through use of genotypes.

Haplotype-based association analysis has been recognized as a tool with high resolution and potentially great power for identifying modest etiological effects of genes. However, in practice, its efficacy has not been as successfully reproduced as expected in theory. One primary cause is that such analysis tends to require a large number of parameters to capture the abundant haplotype varieties, and many of those are expended on rare haplotypes for which studies would have insufficient power to detect association even if it existed. To concentrate statistical power on more-relevant inferences, in this study, we developed a regression-based approach using clustered haplotypes to assess haplotype-phenotype association. Specifically, we generalized the probabilistic clustering methods of Tzeng to the generalized linear model (GLM) framework established by Schaid et al. The proposed method uses unphased genotypes and incorporates both phase uncertainty and clustering uncertainty. Its GLM framework allows adjustment of covariates and can model qualitative and quantitative traits. It can also evaluate the overall haplotype association or the individual haplotype effects. We applied the proposed approach to study the association between hypertriglyceridemia and the apolipoprotein A5 gene. Through simulation studies, we assessed the performance of the proposed approach and demonstrate its validity and power in testing for haplotype-trait association.

Apolipoprotein A-V↗

PubMed2001

Adverse events associated with methicillin-resistant Staphylococcus aureus in a nursing home.

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) generates concern in nursing homes. Restrictive isolation precautions may be applied for indefinite periods. Adverse events driving these concerns include transmission and infection. METHODS: The 721-bed Wisconsin Veterans Home in King performs approximately 645 cultures annually. The site, severity, and number of MRSA infections were determined for 69 months. Pulsed-field gel electrophoresis was performed on all initial isolates, followed by a statistical cluster analysis looking for evidence of transmission. RESULTS: Sixty-seven MRSA infections were identified (1.6 per 100 residents per year); many were polymicrobial, and it was difficult to determine the proportionate role of MRSA in morbidity or mortality. There was an episode of rapidly fatal MRSA septicemia in which empiric antibiotic therapy was ineffective. Twenty-one genetic strains were encountered. Statistical analysis identified 13 clusters of genetically identical strains clustered in time and space (P<.05). CONCLUSIONS: Infections with MRSA were identified at relatively low rates; however, the etiology of many serious nursing home infections is not determined, especially pneumonia. Statistical analysis revealed clustering and evidence of transmission. Nursing home practitioners should consider MRSA when applying empiric treatment to serious infections. We recommend a program including (1) judicious use of antibiotics, including topical agents, to reduce selection of resistant organisms; (2) obtaining and tracking cultures of infectious secretions to diagnose MRSA infections and focus antibiotic therapy; (3) universal standard secretion precautions because any resident could be a carrier; and (4) a detailed assessment and care plan for the carrier that maximizes containment of secretions and independence in activities. However, basic hygiene cannot be maintained in communal areas by some residents without restriction of activities of daily living.

Aged↗

PubMed2003

Benign and malignant breast lesions: diagnosis with multiparametric MR imaging.

PURPOSE: To both develop and use a tissue signature method for the identification and classification of breast lesions and healthy breast tissue with magnetic resonance (MR) imaging. MATERIALS AND METHODS: Thirty-six patients underwent breast MR imaging (T1- and T2-weighted imaging and three-dimensional T1-weighted imaging with and without contrast material enhancement), followed by biopsy or mastectomy and histopathologic analysis. Tissue cluster analysis was performed by using the iterative self-organizing data technique to identify glandular, adipose, and lesion tissue signature vectors. Glandular and lesion tissue vectors were characterized by angular separation from the reference adipose tissue vector. Differences in angular separation of histologically proved benign and malignant lesion groups were evaluated with an independent t test. The usefulness of the angular separation model for distinguishing benign from malignant lesions was evaluated with nonparametric receiver operating characteristic curve analysis. RESULTS: The model enabled successful identification and characterization of breast lesion tissue clusters in all patients; 18 lesions were benign, and 18 were malignant. Angular separation +/- SD was 17.8 degrees +/- 6.1 degrees between adipose tissue and malignant lesions and 29.0 degrees +/- 11.2 degrees between adipose tissue and benign lesions. Angular separations of benign lesions and malignant lesions were significantly different (P =.002), with a specificity of 78% and sensitivity of 89% at a cutoff value of 21 degrees. Significant differences in angular separation from adipose tissue also were found between glandular tissue and lesion tissue (P <.001) and, in glandular tissue, between patients with benign lesions and those with malignant lesions (P =.04). The area under the receiver operating characteristic curve was 0.84. CONCLUSION: Multispectral analysis of conventional breast MR images based on the iterative self-organizing data model and on measurement of angular separation between tissue signature vectors may enable automated lesion identification and classification.

Adolescent↗

PubMed2003

Patterns and impact of comorbidity and multimorbidity among community-resident American Indian elders.

PURPOSE: The purpose of this study is to suggest a new approach to identifying patterns of comorbidity and multimorbidity. DESIGN AND METHODS: A random sample of 1,039 rural community-resident American Indian elders aged 60 years and older was surveyed. Comorbidity was investigated with four standard approaches, and with cluster analysis. RESULTS: Most respondents (57%) reported 3 or more of 11 chronic conditions. Cluster analysis revealed a four-cluster comorbidity structure: cardiopulmonary, sensory-motor, depression, and arthritis. When the impact of comorbidity on four health-related quality of life outcomes was tested, the use of the clusters offered more explanatory power than the other approaches. IMPLICATIONS: Our study improves understanding of comorbidity within an understudied and underserved population by characterizing comorbidity in conventional and novel ways. The cluster approach has four advantages over previous approaches. In particular, cluster analysis identifies specific health problems that have to be addressed to alter American Indian elders' health-related quality of life.

Activities of Daily Living↗

PubMed2006

[Study of Yiqi Huayu Bushen recipe and its decomposed formulas in regulating gene expressions in degenerated cervical intervertebral discs of rats].

OBJECTIVE: To investigate the gene expression changes in the degenerated cervical intervertebral discs of rats, and to study the function of Yiqi Huayu Bushen Recipe, a compound Chinese herbal medicine, and its discomposed formulas in regulating gene expressions in the degenerated cervical intervertebral discs. METHODS: The rat model with degenerated cervical intervertebral discs caused by imbalance between the dynamic and static forces was established. The mRNA was extracted from the cervical intervertebral discs of rats in the normal control and experiment groups, and the cDNA probes were obtained by inverse transcript. The cDNA probes were hybridized with the gene chips. The gene expression pattern was gained with a laser scanner. Image analysis, standardized ratio value and cluster analysis were used to investigate the differential of gene expressions between the control and experiment groups. RESULTS: Cluster analysis showed that the gene chips of No.1 (Yiqi Huayu group), No.2 (Yiqi Bushen group) and No.3 (Huayu Bushen group) were in one class, while the gene chips of No.4 (untreated group) and No.5 (Yiqi Huayu Bushen group) were in the other. The gene expression of No.4 was different from the others mostly, and the gene expressions of No.2 and No.3 were similar. There were 96 genes expressed differently in three cases and among them 77 genes were already known and the expression of 48 genes were up-regulated (ratio>1.0), and 29 down-regulated (ratio<0.5). There were 25 genes expressed differently between the untreated group and the herb-treated groups. CONCLUSIONS: The gene expressions of the degenerated rat intervertebral discs are changed. Yiqi Huayu Bushen Recipe and its discomposed formulas have the effect of regulating the expressions of related genes, such as PI(3)K, PTK, ERK3, and PH1B1.

Animals↗

PubMed1995

Classification and regression trees for bone marrow immunophenotyping.

Methods are needed to assist with automating three-color flow cytometric immunophenotyping of bone marrow from leukemia patients. Described is a method in which a normal bone marrow data set is used as a template against which to compare leukemic bone marrow data sets. This template is obtained using techniques of cluster analysis and cluster editing. Leukemic cells often inappropriately express antigens and appear in a different part of the multivariate data space than normal cells. To recognize the cells exhibiting inappropriate antigen expression, an artificial cluster of "cells" is added to the normal template. The "cells" in this cluster fill the space not occupied by normal cells. Classification and regression tree (CART) analysis is used to train a classifier that can then be used to isolate the major cell types and the inappropriate expression cells in a leukemic bone marrow specimen.

Automation↗

PubMed1995

Enhancing the diversity of a corporate database using chemical database clustering and analysis.

The contribution that the Chemical Abstracts structural database (CAST-3D) and the Maybridge database (MAY) would make to diversifying the structural information and property space spanned by our corporate database (CBI) is assessed. A subset of the CAST-3D database has been selected to augment the structural diversity of various electronic databases used in computer-assisted drug design projects. The analysis of the MAY database directly offers the potential to expand the CBI compound library, but also provides a source for structural diversity in a format suitable for computer-assisted database searching and molecular design. The analysis performed is twofold. First, a nonhierarchical clustering technique available in the Daylight clustering package is applied to evaluate the structural differences between databases. The comparison is then extended to analyze various structure-derived property spaces calculated from molecular descriptors such as the logarithm of the octanol-water partition coefficient (CLOGP), the molar refractivity (CMR) and the electronic dipole moment (CDM). The diversity contribution of each database to these property spaces is quantified in relation to our corporate database.

Chemical Phenomena↗

PubMed2002

Evidence-based infection control in thoracic surgery.

OBJECTIVES: Methicillin-resistant Staphylococcus aureus (MRSA) continues to pose a major threat to the lung and cardiovascular surgery patients. We propose evidence-based infection control (EBIC) against MRSA. METHODS: We conducted a basic study comparing genotyping to cluster analysis using minimal inhibition concentration on 17 drugs for 21 MRSA strains. With or without EBIC using cluster analysis and global evidence, we compared the incidence of postoperative MRSA infection. Notably, we eliminated tweezers stands and emesis basins. RESULTS: Cluster analysis showed a typing sensitivity of 72%. The use of EBIC decreased MRSA cross-infection in the recovery room. A lung surgery series showed an MRSA incidence of 1/190 before and 0/200 after EBIC was introduced. For a cardiovascular surgery series, the MRSA incidence was 2/169 before and 0/84 after EBIC was introduced. Across wards, MRSA among Staphylococcus aureus in patient fell from 68% in 1999 to 57% in 2000. CONCLUSIONS: EBIC consisting of global guidelines and cluster analysis was useful in controlling MRSA in lung and cardiovascular surgery patients.

Cluster Analysis↗

PubMed2006

A generalized clustering problem, with application to DNA microarrays.

We think of cluster analysis as class discovery. That is, we assume that there is an unknown mapping called clustering structure that assigns a class label to each observation, and the goal of cluster analysis is to estimate this clustering structure, that is, to estimate the number of clusters and cluster assignments. In traditional cluster analysis, it is assumed that such unknown mapping is unique. However, since the observations may cluster in more than one way depending on the variables used, it is natural to permit the existence of more than one clustering structure. This generalized clustering problem of estimating multiple clustering structures is the focus of this paper. We propose an algorithm for finding multiple clustering structures of observations which involves clustering both variables and observations. The number of clustering structures is determined by the number of variable clusters. The dissimilarity measure for clustering variables is based on nearest-neighbor graphs. The observations are clustered using weighted distances with weights determined by the clusters of the variables. The motivating application is to gene expression data.

Algorithms↗

PubMedDate not supplied

Toward a representative set of "real-life" noises.

The focus of this study was to create a database containing a large number of background noises. These noises were analysed on the basis of their spectral and temporal behaviour. The most important dimensions have been determined by using a factor analysis, which reduced the total number of dimensions from 14 (7 octave bands in the spectral domain and 7 in the temporal domain) to 4. One dimension provides information about the amount of amplitude modulation present in the signal. The other dimensions provide spectral information. Based on the output of the factor analysis, a cluster analysis was carried out to reduce the total number of signals (144) to 15 anchor points (i.e., the noise forming the median of the cluster). A selection of these anchor points can be used for fitting of so-called comfort programs in multi-program hearing aids and for the evaluation of signal-processing schemes in specific background noise conditions.

Factor Analysis, Statistical↗