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    Tables S1 - 5 from Mitochondrial BAX Determines the Predisposition to Apoptosis in Human AML
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    Abstract:
    <p>S1 BAX/BAK localization for the test cohort S2 clinical characterization of patient cohorts S3 GISTIC analysis of the test cohort S4 BAX/BAK localization for the validation cohort S5 Multivariate analysis of variance for human AML survival probablity</p>
    Three multivariate prognostic models based on Cox's regression were tested in terms of how they predicted prognosis in a material of 134 patients with breast cancer. The multivariate models all incorporated tumor size, mitotic activity index (MAI), and axillary lymph node status in their formulas, and were originally produced through studies on different patient materials. The predictive behavior of MAI was also tested separately in the same material. The multivariate models gave roughly parallel predicted percentages of survival at two years (CV=5.2%), but showed clearly greater variation later (12.3% and 24.4% at 5 and 9 years, respectively). The results were more uniform between the multivariate models, than between the prediction by MAI and the multivariate models. The variation between repeated estimates was smaller within multivariate models than within the estimation of one of their components (MAI). We found the use of the multivariate models easy. However, traditional hospital practice does not necessarily favor the use of multivariate models, although they seem to group patients more reliably than single prognostic features.
    Univariate
    Citations (3)
    Univariate methods are very helpful when utilized appropriately within the research analysis. However, there are many occasions in which only multivariate methods will satisfy an optimal assessment. In this case, multivariate methods will permit the researcher to incorporate many variables within a single research analysis. This work reviews the use of multivariate methods and how to apply them in clinical medicine.
    Univariate
    Multivariate measurement systems analysis is usually performed by designing suitable gauge R&R experiments ignoring available data generated by the measurement system while used for inspection or process control. This article proposes an approach that, using the data that are routinely available from the regular activity of the instrument, offers the possibility of assessing multivariate measurement systems without the necessity of performing a multivariate gauge study. It can be carried out more frequently than a multivariate gauge R&R experiment, since can be implemented at almost no additional cost. Therefore the synergic use of the proposed approach and the traditional multivariate gauge R&R studies can be a useful strategy for improving the overall quality of multivariate measurement systems and is effective for reducing the costs of a multivariate MSA performed with a certain frequency.
    Multivariate statistical analyses are appropriate whenever a study involves two or more outcome variables. Because multiple-outcome models reflect social reality more accurately than do conventional single-outcome or univariate models, multivariate analysis should be studied and practiced more extensively than it is. In this article, several reasons for doing multivariate analysis are presented, and two common errors in statistical analysis are discussed. Examples are presented to show how a single multivariate analysis can produce different results than do separate univariate analyses, and to illustrate the relationship between ANOVA and canonical correlation analysis.
    Univariate
    Canonical correlation
    Statistical Analysis
    Current approaches to the issue of interpreting the output of multivariate analyses are examined and two broad classes of approach distinguished: univariate and multivariate. Most researchers opt for interpretations that deal with variables one at a time rather than in combination. This approach is appropriate for univariate but not for multivariate techniques which are essentially tools for investigating combinations of psychological attributes which may determine outcome scores or category membership. Assessing the relative importance of individual variables is also inappropriate. A number of multivariate approaches that deal with combinations of variables are then examined. These include the use of simultaneous test procedures (STPs), rotation, and the simplification of canonical variates.
    Univariate
    Canonical correlation
    Abstract Hydrological events are described through a number of dependent features. To be correctly treated, the latter should be considered jointly in a multivariate framework. The aims of the multivariate hydrological frequency analysis are given and the justifications of adopting the multivariate setting in hydrology are discussed. The main steps of the analysis are described. Some extensions and perspectives are presented.