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The Cartesian coordinates obtained after this superimposition constitute the shape coordinates that can be analyzed using multivariate statistics.
Data will be analyzed using multivariate statistical methods which allow analyses of time-dependent outcomes and covariates.
Binary outcomes that are not measured repeatedly, like treatment initiation or trial completion, will be analyzed using multivariate logistic regressions.
This relationship indicates that various aspects of the patients' capabilities (cognitive, global, IADL) should be analyzed using multivariate methods, and these aspects should be interpreted simultaneously because a change in one ability might affect a change in another.
The differences in the mean scores of the continuous variables will be analyzed using multivariate regression and the difference in the proportion of the dichotomous outcomes (e.g. the proportion 'ready to return to work') will be analyzed using logistic regression.
Many of the developments discussed in this review can be combined, the results can then be analyzed using multivariate techniques or combined with information from other imaging techniques using cross correlation analysis (Sinha et al. 2008).
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Data were analyzed using multivariate logistic regression models.
Many of the data from these investigations are analyzed using multivariate statistical techniques such as parallel independent component analysis.
Data was analyzed using multivariate techniques (TWINSPAN, Detrended Correspondence Analysis, Cluster Analysis, and Kruskal Wallis test).
Data collected from children aged between 10 and 12 years were analyzed using multivariate analysis of variance (MANOVA).
Data from 282 incident oral cancer cases and 1410 matched controls were analyzed using multivariate conditional logistic regression models.
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