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Another study using multivariate statistical modelling, The Translating Research into Action for Diabetes (TRIAD) study of approximately 9000 patients, examined the association between intensity of disease management by physicians and diabetes process and outcomes measures [ 25].
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Omics-based biomarkers are generated by measuring many more variables and using multivariate statistical models.
The methodology is based on the identification of data-based models using multivariate statistical methods such as Partial Least Squares (PLS).
Using multivariate statistical analysis we could generate powerful models for diagnosis and prognosis of septic shock.
Using multivariate statistical methods, four types were identified.
Exploration of human encephalitis data using advanced multivariate statistical modelling approaches that can capture the inherent complexity in the data is, therefore, crucial in understanding the causes of human encephalitis.
Choices are decomposed using discrete multivariate statistical models.
Associations were sought using univariate and multivariate statistical models.
The logistic regression approach [ 15] is perhaps the most common technique used to develop multivariate statistical models to predict morbidity and mortality risk after coronary artery bypass grafting.
Akaike's information criteria (AIC= χ−2df) was used to derive suitable multivariate statistical models for each disease (Akaike, 1987).
Multivariate statistical modeling using principal component analysis (PCA) was performed on the GC-MS spectra of four independently grown replicates.
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