For example, a multivariate analysis approach has been applied to Alzheimer's disease using a combination of MRS and MRI volumetric metrics to increase diagnostic accuracy and resulted in a sensitivity of 0.97 and a specificity of 0.94.
For example, a multivariate global test across all the components could be used to look for simultaneous demonstrated benefit; but readers may find it difficult to interpret such a result [ 16, 17].
For example, a multivariate Cox proportional hazards regression model including established confounding factors (e.g., age, sex, smoking, BMI, HbA1c, duration of diabetes, serum lipids, blood pressure, diabetes treatment, hypertension treatment, dyslipidemia treatment, or plaque thickness) as covariates simultaneously may be a good candidate.
PLS-DA is an example of a multivariate model that has been applied in systems biology a case study being our previous work on Indonesian herbal medicines, popularly known as Jamu.
Also, risk based stratification may still obscure important modification of relative treatment effect that can be discovered or excluded (as in the Justification for the Use of Statins in Prevention trial example) by a multivariate model for predicting treatment effect based on trial data.
For example, in a multivariate analysis of associations, Comings and his coauthors[ 3] examined 42 dopamine, serotonin, and norepinephrine genes on attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and CD phenotypes.
However, this is not necessarily true in other fields; for example, in a multivariate meta-analysis of longitudinal data the within-study correlations varied between 0.48 and 0.97 (Jones et al., personal communication).
For example, in the case of a multivariate meta-analysis (no covariates), where all studies provide all outcomes, and where all studies' within-study covariance matrices are identity matrices,.
The second example to be examined is the case of a multivariate ANOVA.
Therefore, the probability that a HW was an MD for example was expressed in a multivariate logistic regression model as: p ^ = e b 0 + b 1 X 1 + b 2 X 2 + … + b k X k 1 + e b 0 + b 1 X 1 + b 2 X 2 + … + b k X k where p ^ is the expected probability that a HW is an MD; X1 through Xk are k distinct independent variables; and b0 through bk are the regression coefficients.
Real-world simulations of various hydroclimatic processes with different correlation structures and marginals, such as precipitation, river discharge, wind speed, humidity, extreme events per year, etc., as well as a multivariate example, highlight the flexibility, advantages, and complete generality of the method.
