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Multivariate analysis used a lasso regression (Tibshirani 1996; Musoro 2014), including multiple imputation for missing values and bootstrap validations, adapted for high numbers of variables (biomarkers).
Finally, multivariate analysis, used to create equations to predict impacts of the nutrients in the 11 feedstuffs on their proportional increase in 4 h gas production, suggests that the increase in 4 h gas production of any feed may be predicted from its organic nutrient profile, offering the potential to determine the optimal feeding level of sarsaponin in any TMR based on its nutrient profile.
The multivariate analysis used here thus demonstrated the capacity of identifying differential aspects of the profile of ionic species from relaxed or stressed animals.
Virological status was ascertained after five years of follow up and multivariate analysis used to model associations of baseline variables and medication adherence with time to viral suppression or failure.
Multivariate analysis used Cox's proportional hazard model.
Multivariate analysis used forward likelihood binomial logistic regression model.
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Multivariate analysis using logistic regression.
Various prognostic factors were analyzed by multivariate analysis using logistic regression.
Variables associated with outcome were identified by multivariate analysis using logistic regression.
A univariate and multivariate analysis using the Fischer and Mann–Whitney tests was performed.
Multivariate analysis using variables significant at univariate analysis and pre-eclampsia confirmed this difference.
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