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PubMed2005

The statistical analysis of multivariate serological frequency data.

Data occurring in the form of frequencies are common in genetics-for example, in serology. Examples are provided by the AB0 group, the Rhesus group, and also DNA data. The statistical analysis of tables of frequencies is carried out using the available methods of multivariate analysis with usually three principal aims. One of these is to seek meaningful relationships between the components of a data set, the second is to examine relationships between populations from which the data have been obtained, the third is to bring about a reduction in dimensionality. This latter aim is usually realized by means of bivariate scatter diagrams using scores computed from a multivariate analysis. The multivariate statistical analysis of tables of frequencies cannot safely be carried out by standard multivariate procedures because they represent compositions and are therefore embedded in simplex space, a subspace of full space. Appropriate procedures for simplex space are compared and contrasted with simple standard methods of multivariate analysis ("raw" principal component analysis). The study shows that the differences between a log-ratio model and a simple logarithmic transformation of proportions may not be very great, particularly as regards graphical ordinations, but important discrepancies do occur. The divergencies between logarithmically based analyses and raw data are, however, great. Published data on Rhesus alleles observed for Italian populations are used to exemplify the subject.

Data Interpretation, Statistical↗
PubMed1991

Multivariate probit analysis: a neglected procedure in medical statistics.

The multivariate probit model is designed to regress a vector of correlated quantal variables on a mixture of continuous and discrete predictors. Various applications can be found in the biological, economical and psychosociological literature, but the method is not yet widely used in medical applications. We reintroduce this model thereby showing its usefulness in medical problems. Software for this model is, however, not widely available. We have written a PC program to select predictors and estimate parameters in the multivariate probit framework. The performance and characteristics of the program are briefly illustrated.

Mathematical Computing↗
PubMed2006

PYCHEM: a multivariate analysis package for python.

UNLABELLED: We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. AVAILABILITY: http://sourceforge.net/projects/pychem

Algorithms↗
PubMed2001

The analysis of repeated measures designs: a review.

Repeated measures ANOVA can refer to many different types of analysis. Specifically, this vague term can refer to conventional tests of significance, one of three univariate solutions with adjusted degrees of freedom, two different types of multivariate statistic, or approaches that combine univariate and multivariate tests. Accordingly, it is argued that, by only reporting probability values and referring to statistical analyses as repeated measures ANOVA, authors convey neither the type of analysis that was used nor the validity of the reported probability value, since each of these approaches has its own strengths and weaknesses. The various approaches are presented with a discussion of their strengths and weaknesses, and recommendations are made regarding the 'best' choice of analysis. Additional topics discussed include analyses for missing data and tests of linear contrasts.

Analysis of Variance↗
PubMed2006

Local feature analysis: a statistical theory for reproducible essential dynamics of large macromolecules.

Multivariate statistical methods are widely used to extract functional collective motions from macromolecular molecular dynamics (MD) simulations. In principal component analysis (PCA), a covariance matrix of positional fluctuations is diagonalized to obtain orthogonal eigenvectors and corresponding eigenvalues. The first few eigenvectors usually correspond to collective modes that approximate the functional motions in the protein. However, PCA representations are globally coherent by definition and, for a large biomolecular system, do not converge on the time scales accessible to MD. Also, the forced orthogonalization of modes leads to complex dependencies that are not necessarily consistent with the symmetry of biological macromolecules and assemblies. Here, we describe for the first time the application of local feature analysis (LFA) to construct a topographic representation of functional dynamics in terms of local features. The LFA representations are low dimensional, and like PCA provide a reduced basis set for collective motions, but they are sparsely distributed and spatially localized. This yields a more reliable assignment of essential dynamics modes across different MD time windows. Also, the intrinsic dynamics of local domains is more extensively sampled than that of globally coherent PCA modes.

Bacteriophage T4↗
PubMed1983

On some useful statistical techniques in the analysis of hormone data.

Three multivariate statistical techniques, which have been found very useful in the analysis of hormone data, are demonstrated. The value of these techniques as a means of compressing the information contained in a complex array of experimental units and variates into a few "dimensions" is emphasised.

Analysis of Variance↗
PubMed1991

Computer graphics representation of a statistical model used with computer-aided diagnosis.

A description of computer graphics of a multidimensional model that is used with computer-aided diagnosis or prognosis is presented. The model is discussed and computer graphics of the model are developed. The computer graphics are suitable as visual supplements for presenting the computer-aided diagnostic model to individuals who may be inexperienced in multivariate statistics.

Animals↗
PubMed1993

Steroidogenic assessment using ovary culture in cycling rats: effects of bis(2-diethylhexyl)phthalate on ovarian steroid production.

In vitro ovary culture in rats was used to characterize ovarian steroidogenesis and to evaluate changes produced by in vivo exposure to bis(2-diethylhexyl)phthalate (DEHP). Steroid profiles [progesterone (P4), estradiol (E2), and testosterone (T)] from cultures of minced ovary were obtained in untreated immature and mature rats, and from mature rats treated with DEHP. A 1-h incubation without human chorionic gonadotropin (hCG) was used to produce an initial steroidogenic profile. Three 1-h incubations with hCG were used to produce a stimulated steroid profile. A combination of initial and stimulated ovarian steroid profiles was shown to correctly identify the stage of the cycle in all untreated rats, using multivariate statistical analysis. Separately, initial or stimulated ovarian steroid profiles correctly identified the stage of the cycle in more than 90% of the rats. The statistical analysis using a combination of variables (multivariate) indicated that DEHP-treated rats were significantly different (P < 0.001) from sham-treated rats. In fact, the alteration caused by DEHP in the in vitro ovarian steroidogenic profile was most apparent in rats during diestrus and estrus. In DEHP-treated rats in diestrus, ovarian steroidogenesis appeared to shift to the production of more T and more E2 than in untreated rats in diestrus. The change seen in steroid profiles in DEHP-treated rats in estrus is to decreased E2 production. The steroid profile from ovary culture in conjunction with vaginal cytology was very useful in correctly identifying in vivo DEHP-treated rats, and will be a useful in vitro technique in the evaluation of ovarian toxicants in cycling females.

Analysis of Variance↗
PubMed1998

Predictors of occult pneumococcal bacteremia in young febrile children.

STUDY OBJECTIVE: Occult pneumococcal bacteremia (OPB) occurs in 2.5% to 3% of highly febrile children 3 to 36 months of age, and 10% to 25% of untreated patients with OPB experience complications, including 3% to 6% in whom meningitis develops. The purpose of this study was to identify predictors of OPB among a large cohort of young, febrile children treated as outpatients using multivariable statistical methods. METHODS: We derived and validated a logistic regression model for the prediction of OPB. We evaluated 6,579 outpatients 3 to 36 months of age with temperatures of 39 degrees C or higher who previously had been enrolled in a study of young febrile patients at risk of OPB in the emergency departments of 10 hospitals in the United States between 1987 and 1991; 164 patients (2.5%) had OPB. We randomly selected two thirds of this population for the derivation of the model and one third for validation. In the derivation set, we analyzed the univariate relationships of six variables with OPB: age, temperature, clinical score, WBC count, absolute neutrophil count (ANC), and absolute band count (ABC). All six variables were then entered into a logistic regression equation and those retaining statistical significance were considered to have an independent association with OPB. RESULTS: Patients with OPB were younger, more frequently ill-appearing, and had higher temperatures, WBC, ANC, and ABC than patients without bacteremia. Only three variables, however, retained statistically significant associations with OPB in the multivariate analysis: ANC (Adjusted odds ratio [OR] 1.15 for each 1,000 cells/mm3 increase, 95% confidence interval [CI] 1.06, 1.25), temperature (adjusted OR 1.77 for each 1 degree C increase, 95% CI 1.21, 2.58), and age younger than 2 years (adjusted OR 2.43 versus patients 2 to 3 years old, 95% CI interval 1.11, 5.34). In the derivation set, 8.1% of patients with ANCs greater than or equal to 10,000 cell/mm3 had OPB (95% CI 6.3, 10.1%) versus .8% of patients with ANCs less than 10,000 cells/mm3 (95% CI .5, 1.2%). When tested on the validation set, the model performed similarly. CONCLUSION: Independent predictors of OPB in children 3 to 36 months of age with temperatures of 39 degrees C or higher treated as outpatients include ANC, temperature, and age younger than 2 years. These predictors may be used to develop clinical strategies to limit laboratory testing and antibiotic administration to those children at greatest risk of OPB.

Age Factors↗
PubMed1996

[Value of immunohistochemical determination of receptors, tissue proteases, tumor suppressor proteins and proliferation markers as prognostic indicators in primary breast carcinoma].

OBJECTIVE: We tested whether immunohistochemical detection of oestrogen and progesterone receptor (ER, PR), the oestrogen-dependent protein pS2, the growth hormone receptors p 185neu and EGF-R, the tumour suppressor protein p53, the tissue proteases Cathepsin D and Urokinase, and the proliferation marker PCNA are of prognostic relevance in breast cancer patients. METHODS: Expression of the proteins listed above was evaluated in formalin-fixed and paraffin-embedded sections of 311 primary breast cancer specimens using modified Avidin-Biotin-Complex methods. Results were correlated to clinical and morphological parameters (age, menopausal status, nodal status, tumour size, tumour grade), and clinical course of disease (complete follow-up in 301 women, median observation time 62 months) utilising univariate and multivariate statistical analyses. RESULTS: If univariate analyses and multivariate regression analyses according to the Cox-model were applied, only Cathepsin D correlated to an elevated risk for recurrence in nodally negative patients (n = 135). In nodally positive women (n = 161), increasing tumour size, tumour grade, lack of ER and PR, expression of p185neu, p53, and PCNA indicated a significantly increased relative risk. CONCLUSIONS: Immunohistochemistry allows the detection of parameters which may indicate prognosis in subgroup of breast cancer patients.

Adult↗
PubMed1986

[The application of multivariate analysis to the radioallergosorbent test (RAST)].

Multivariate statistical analyses using principal component analyses (PCA) and Mahalanobis' method were applied to the quality control of radioallergosorbent test (RAST). Mites and foods allergen were analyzed by PCA method, and foods allergen were analyzed by Mahalanobis' method. The vast differences in the distribution on titers among the mites and the foods were observed. To evaluate the results of a test and the quality of kits for test statistically, it should be better to use multivariate analyses such as PCA or Mahalanobis' method.

Allergens↗
PubMed2005

Dermatoglyphs and larynx cancer.

Cancer of the larynx is the seventh most common malignant disease among middle-aged men in Croatia. Morbidity ratio between men and women is 17:1. Etiology of disease is directly connected with the tobacco use while genetic influences have not yet been studied enough. Digito-palmar dermatoglyphs analysis has already been used in studying the genetic etiology of certain malignant diseases (lung, breast, cervical, colorectal, melanoma and gastric cancer). We have analyzed correlation of quantitative and qualitative traits between two groups (group of 40 men with larynx cancer versus control group of 100 phenotypically healthy men). Quantitative statistical analysis (descriptive statistics, multivariate and univariate analysis) has not shown statistically significant difference except for the latent structure using factor analysis. Qualitative analysis has shown statistically significant difference among two investigated groups thus suggesting the faster changes of the qualitative features under the influence of the ecological factors.

Adult↗
PubMed1985

Relationships between clinical data and histology of the large bowel in Crohn's disease and ulcerative colitis.

Histologic changes of rectal biopsies were compared with clinical data in 83 patients suffering from Crohn's disease, 78 patients with ulcerative colitis, and 87 normal controls. Additionally, colonic biopsies were studied in 82 Crohn's disease patients. The biopsies were cut in serial sections and examined by quantitative and semiquantitative methods, determining changes of superficial epithelium, crypts, stroma, and submucosa. The statistical evaluation was performed by univariate and multivariate analyses. In normal controls, 2.6 percent of the correlations existing between histologic and clinical data were significant; in rectal biopsies of Crohn's disease 8.9 percent, in colonic biopsies of Crohn's disease 6.7 percent, and in ulcerative colitis 10.4 percent. Multiple stepwise regression analyses revealed a distinct predictive value of histology of rectal biopsies for the clinical activity index according to Best et al. in Crohn's disease. Most effective histologic changes were content of goblet cells and acute inflammatory lesions. In colonic biopsies, significant predictive values were found for diarrhea, anal fissures, and meteorism. Most effective variables in prediction of diarrhea were granuloma and eosinophilic and histiocytic infiltration; in prediction of anal fissures increased basophilia of epithelium and leukocytic infiltration of crypts; in prediction of meteorism increased basophilia of epithelium and hyperemia. In ulcerative colitis, significant predictive values were present for activity of disease on colonoscopy and the blood content of thrombocytes. Most effective variables in the prediction of colonoscopically determined activity were histiocytic and neutrophilic infiltration, height of the cryptal epithelium, and cryptal distance; in the prediction of thrombocytic values cryptal length, cryptal distance, and plasmacellular infiltration. In normal controls, no consistent predictive value of histology was found. Though each multivariate statistical method depends on the underlying sample, at least the first variables entering the multiple regression analyses are of high value in the estimation of clinical parameters by morphologic methods. Thus, the study elucidates the high value of rectal biopsies in estimating the activity of illness, both in Crohn's disease and in ulcerative colitis.

Adolescent↗
PubMed2002

Prognostic impact of metallothionein on oral squamous cell carcinoma.

Metallothionein (MT), a low-molecular-weight protein with high cysteine content, seems to be related to neoplastic resistance to oncologic treatment and therefore has been studied as a prognostic factor for a variety of human malignant tumors. MT overexpression in neoplasms of ectodermal origin is usually associated with a poor prognosis. MT expression was evaluated in 60 samples of oral squamous cell carcinoma by immunohistochemistry to study its prognostic influence on oral cancer. Possible associations of MT immunoexpression were also investigated with respect to clinical stage (TNM), histological grading, and proliferation index (Ki-67) of the lesions. No significant statistical correlation was observed among these variables. The impact on overall survival was assessed by uni and multivariate statistical tests. Mean MT labeling index was 60%. High MT labeling indexes (over 76%) predicted shorter survival in univariate statistical analysis. In multivariate analysis, MT labeling index and clinical stage were independent prognostic factors. MT overexpression in oral squamous cell carcinoma seems to be related to a worse prognosis for patients.

Adult↗
PubMed2006

Comparison of wound management methods after removal of maxillofacial osseous lesions.

PURPOSE: To evaluate outcomes associated with choice of wound management, ie, primary closure or healing by secondary intention, of osseous defects after excision of maxillofacial bone lesions as a guide to clinical practice. PATIENTS AND METHODS: Using a retrospective cohort study design, we enrolled a sample composed of subjects treated for jaw lesions between 1995 and 2003. The primary predictor variable was the wound management choice of the residual jaw defect, classified as primary closure or healing by secondary intention. The primary outcome variable was postoperative inflammatory complications. Other study variables were grouped as demographic, medical/dental history, lesion information, preoperative complications, operative treatment, and follow-up information. Appropriate uni-, bi-, and multivariate statistics were computed. RESULTS: The sample was composed of 93 subjects with 126 jaw lesions, of which 90 (71.4%) were managed by primary closure. In the bivariate analyses, tobacco use was statistically associated (P < .05) with wound management and near statistically associated (P = .06) with complications. In the multivariate model, after adjusting for the presence of multiple cysts and tobacco use, there was not a statistically significant difference between the 2 wound management choices in terms of postoperative complications. CONCLUSIONS: Our results suggest that the choice of managing the osseous wound, ie, primary closure versus secondary intention, was not associated with increased risk of postoperative inflammatory complications. The implications of these findings are discussed below.

Adult↗
PubMed1989

Clinical patch test data evaluated by multivariate analysis. Danish Contact Dermatitis Group.

The aim of the present study was to evaluate the influence of individual explanatory factors, such as sex, age, atopy, test time and presence of diseased skin, on clinical patch test results, by application of multivariate statistical analysis. The study population was 2166 consecutive patients patch tested with the standard series of the International Contact Dermatitis Research Group (ICDRG) by members of the Danish Contact Dermatitis Group (DCDG) over a period of 6 months. For the 8 test allergens most often found positive (nickel, fragrance-mix, cobalt, chromate, balsam of Peru, carba-mix, colophony, and formaldehyde), one or more individual factors were of significance for the risk of being sensitized, except for chromate and formaldehyde. It is concluded that patch test results can be compared only after stratification of the material or by multivariate analysis.

Adolescent↗
PubMed1981

Statistical basis for exploring schizophrenia.

The authors illustrate the use of multivariate statistics as a tool for exploring diagnostic factors in schizophrenia in two areas: the derivation and replication of the 12-point flexible diagnostic system and the definition of schizophrenic subtypes. They suggest an interactive approach between clinician and statistician to ensure the optimal combination of clinical judgment and systematic data analysis. Statistical concepts are presented with a minimum of statistical terminology.

Diagnosis, Differential↗
PubMed2003

Integration of distributed multi-analyzer monitoring and control in bioprocessing based on a real-time expert system.

A computer system solution for integration of a distributed bioreactor monitoring and control instrumentation on the laboratory scale is described. Bioreactors equipped with on-line analyzers for mass spectrometry, near-infrared spectroscopy, electrochemical probes and multi-array gas sensors and their respective software were networked through a real-time expert systems platform. The system allowed data transmission of more than 1800 different signals from the instrumentation, including signals from gas sensors, electrodes, spectrometer detectors, balances, flowmeters, etc., and were used for processing and carrying out a number of computational tasks such as partial least-square regression, principal component analysis, artificial neural network modelling, heuristic decision-making and adaptive control. The system was demonstrated on different cultivations/fermentations which illustrated sensor fusion control, multivariate statistical process monitoring, adaptive glucose control and adaptive multivariate control. The performance of these examples showed high operational stability and reliable function and meet typical requirements for production safety and quality.

Algorithms↗