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PubMedDate not supplied

[Use of microcomputers in biomedical research. Project and implementation of an analysis of variance program. Computer notes].

Clinical and experimental numerical data evaluation requires microcomputer programs which greatly facilitate both the ease and speed of handling statistical data processing tasks. In present study designing and construction of variance analysis program is approached for application of computers to surgery. It is an approach which allows a physician who has some familiarity with programming techniques to rapidly construct and use a family of programs for statistical manipulation of numeric information.

Analysis of Variance↗
PubMedDate not supplied

[Changes in lymphocyte subsets in chronic inflammatory diseases and neoplasms in the elderly. I. Multivariate analysis of variance and factoral analysis].

The variations of lymphocyte subsets have been studied both in neoplastic and chronic inflammatory elderly patients compared to a control group. The interpretation of the Multivariate Analysis of Variance (MANOVA) and of the Factorial Analysis has demonstrated the opposite role of TCD4 subset, mainly involved in the inflammatory process, and of TCD8 subset in regard of the neoplastic ones. We report the slight and yet significant increase of NK related to age. Independently from the disease pattern, factorial analysis permitted a comparison between the variations of lymphocyte subsets and the different grades of immunoresponse.

Aged↗
PubMed2004

Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis.

This article presents confidence interval methods for improving on the standard F tests in the balanced, completely between-subjects, fixed-effects analysis of variance. Exact confidence intervals for omnibus effect size measures, such as or and the root-mean-square standardized effect, provide all the information in the traditional hypothesis test and more. They allow one to test simultaneously whether overall effects are (a) zero (the traditional test), (b) trivial (do not exceed some small value), or (c) nontrivial (definitely exceed some minimal level). For situations in which single-degree-of-freedom contrasts are of primary interest, exact confidence interval methods for contrast effect size measures such as the contrast correlation are also provided.

Analysis of Variance↗
PubMed2004

Adjusting for covariates in variance components QTL linkage analysis.

Variance components modeling has emerged as a powerful method for quantitative trait loci (QTL) linkage analysis. However, the power to detect a gene of minor effect is low. Many complex traits are affected by environmental as well as genetic factors, and one strategy to increase power is to reduce nongenetic phenotypic variance by adjusting for environmental covariates. In this paper, we investigate the power of three approaches to covariate adjustment in variance components linkage analysis: (i) incorporate covariates in the means model, (ii) incorporate covariates in the covariance matrix, and (iii) perform analysis on residual statistics. These approaches are compared to an analysis without adjustment. The results show that in the absence of correlation between the covariate and the QTL effect, adjusting for covariates indeed increases power to detect an underlying QTL. As this correlation increases, however, the power decreases. In the presence of a causal association between QTL and covariates, not adjusting for covariates appeared to be more powerful. The three approaches for adjusting for covariate: residual statistics, the means model, and the covariance model, had equal power to detect a QTL.

Analysis of Variance↗
PubMed2004

Detecting assumption violations in mixed-model analysis of variance.

Parametric analysis of variance (ANOVA) is frequently used to analyse experimental data, yet for the results to be considered as accurate, certain assumptions must be respected: the normality of the distribution of the sampled data, the homogeneity of variance among the groups being compared (i.e., homoscedasticity), and, in certain cases, sphericity. The present work focuses on the methods for detecting violations of these assumptions and provides an example of the application of these methods.

Analysis of Variance↗
PubMed2006

Linkage analysis for complex diseases using variance component analysis: SOLAR.

Variance component linkage analysis has become one of the most popular tools for the analysis of poly genic phenotypes. In particular for cardiovascular disease, such as coronary artery disease and myocardial infarction, variance component analysis holds some unique advantages. This analysis approach is versatile, affording the user the ability to incorporate the interplay between risk factors, genetic susceptibility, the effect of environmental factors, or the joint analysis of multiple phenotypes in the analysis. In this chapter, we present as an introduction the statistical background of variance component analysis as implemented in the genetic analysis package SOLAR.

Analysis of Variance↗
PubMed1985

Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models.

Analysis of variance (ANOVA) interactions involving electrode location are often used to assess the statistical significance of differences between event-related potential (ERP) scalp distributions for different experimental conditions, subject groups, or ERP components. However, there is a fundamental incompatibility between the additive model upon which ANOVAs are based and the multiplicative effect on ERP voltages produced by differences in source strength. Using potential distributions generated by dipole sources in spherical volume conductor models, we demonstrate that highly significant interactions involving electrode location can be obtained between scalp distributions with identical shapes generated by the same source. Therefore, such interactions cannot be used as unambiguous indications of shape differences between distributions and hence of differences in source configuration. This ambiguity can be circumvented by scaling the data to eliminate overall amplitude differences between experimental conditions before an ANOVA is performed. Such analyses retain sensitivity to genuine differences in distributional shape, but do not confuse amplitude and shape differences.

Analysis of Variance↗
PubMed1998

Variance components analysis of carotid and femoral intima-media thickness measurements. REGRESS Study Group, Interuniversity Cardiology Institute of The Netherlands, Utrecht, The Netherlands. Regression Growth Evaluation Statin Study.

B-mode ultrasound intima-media thickness (IMT) measurements of carotid and femoral arterial walls are used in atherosclerosis studies. In this study, the components contributing to IMT measurement variability in males with coronary artery disease were investigated by means of repeated B-mode ultrasound scans and repeated off-line video image analyses. For statistical analysis, a mixed-model analysis of variance was used. From sonographer data, it was shown that human subjects and their arterial wall segments contributed 75% of the total IMT measurement variability in this population. Inter-sonographer variance contributed 25%. The intra-sonographer variance was negligible (<1%). In off-line image analysis, variance components due to subjects and segments, inter-analyst variance, and residual fluctuation were 88%, < 1% and 11%, respectively. Intra-analyst variance was negligible (<1%). The major source of B-mode ultrasound IMT measurement variability finds its origin in subjects and their arterial walls. Although sonographers proved a lesser source of variability, in comparative studies they should enter a study well trained and should be randomly assigned to subjects. Follow-up examinations should preferably be done by the same sonographer. Off-line image analysis contributed little to IMT measurement variability.

Adult↗
PubMed2006

Analysis of codon usage pattern in the radioresistant bacterium Deinococcus radiodurans.

The main factors shaping codon usage bias in the Deinococcus radiodurans genome were reported. Correspondence analysis (COA) was carried out to analyze synonymous codon usage bias. The results showed that the main trend was strongly correlated with gene expression level assessed by the "Codon Adaptation Index" (CAI) values, a result that was confirmed by the distribution of genes along the first axis. The results of correlation analysis, variance analysis and neutrality plot indicated that gene nucleotide composition was clearly contributed to codon bias. CDS length was also key factor in dictating codon usage variation. A general tendency of more biased codon usage of genes with longer CDS length to higher expression level was found. Further, the hydrophobicity of each protein also played a role in shaping codon usage in this organism, which could be confirmed by the significant correlation between the positions of genes placed on the first axis and the hydrophobicity values (r=-0.100, P<0.01). In summary, gene expression level played a crucial role, nucleotide mutational bias, CDS length and the hydrophobicity of each protein just in a minor way in shaping the codon usage pattern of D. radiodurans. Notably, 19 codons firstly defined as "optimal codons" may provide useful clues for molecular genetic engineering and evolutionary studying.

Codon↗