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Negative binomial regression was chosen for the multivariate analyses due to two important characteristics of the dependent variable: (a) data were actual counts of the number of IUU fishing vessel visits to the countries, and (b) data were over-dispersed.
We combined gestational diabetes and diabetes mellitus for multivariate analyses due to small sample sizes in each category.
The following variables were excluded from multivariate analyses due to: 1. High number of missing responses: Income, Shame, Perceived benefits, and Fatalistic attitude.
However, a difficulty arises when they are used concurrently in multivariate analyses, due to multicollinearity that may result from the high functional correlation among them.
The measures of severity and extent of OHRQoL impacts were not used as outcomes in multivariate analyses due to their skewed distributions.
The classification tree (CT) [ 24] was chosen for the multivariate analyses due to a marked deviation from many of the parametric requirements of most of the variables (ordinal/nonlinearity, normality, colinearity, distribution of residuals etc).
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However, numerical differences have not been shown consistently in multivariate analyses, probably due to the small number of patients in the subgroups.
Statistical power was insufficient to perform multivariate statistical analyses due to the limited sample size in the first wave of data collection.
Principle components analysis (PCA) was applied to analyse multivariate data; however, most multivariate analyses were uninformative due to the large amount of missing data from the fossil specimens.
Reduce patient numbers in the multivariate linear regression analyses due to random missing in the outcome measure or selected predictors.
Comparison of survival rates was performed by use of the log rank test using median numbers as cut-off, no multivariate analyses were performed due to small number of events.
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