A slightly more sophisticated category of multivariate analysis is supervised methods, whereby a calibration dataset of known identity is required to build a classification model.
This argument is supported by baseline findings [ 5] whereby multivariate analysis showed that P. falciparum and S. haematobium infections were the most important predictors of anaemia in the studied population.
A multivariate analysis was also employed in this study whereby nausea duration and vomiting/retching frequency were independent variables and NVP severity was the dependent variable.
The multivariate analysis provides some insight into the pathways whereby housing conditions may increase risk of skin infections in the remote Indigenous community environment, and is generally supportive of the simple conceptual framework used in the study.
Interestingly, multivariate analysis showed that baseline PSA levels provided a strong prognostic factor, whereby the overall survival hazard ratio in the lowest baseline PSA quartile (<22.1 ng/mL) was 0.51 (0.31–0.85) compared with the highest PSA quartile (<134 ng/mL) of 0.84 (0.55–1.29) [ 46].
First, all statistically significant covariates (P<0.05) in univariate analyses were included in a multivariate model, and backward stepwise regression was performed, whereby those factors losing their significance (P>0.05) in the multivariate analysis were dropped.
In a similar vein, both the B/P and M/D metagenes were strong and independent predictors of DMFS in Nagalla et al., whereby the B/P metagene emerged as the most significant immune covariate by multivariate analysis.
The multivariate analysis was based on a logistic regression model with a conventional backward stepwise procedure validated by a forward stepwise procedure whereby variables were optimized by the Akaike information criteria, with P < 0.05.
The multivariate analysis... indicates that heads of poor families should average $2,204 in earnings.
