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Two-way interactions were studied between significant variables in all multivariate analyses.
Associations between significant variables in the Chi-square test were then further examined using crude (unadjusted) odds ratios.
Two-way interactions were studied between significant variables in multivariate analysis, and there was no significant interaction at the P = 0.01 level in the final multivariate model.
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All possible two-way interactions between statistically significant variables in the model were tested by adding interaction terms to the model.
ORs and HRs were used to describe the strength of the associations between significant variables and adherence and discontinuation in the final model.
Two-way interactions between biological plausible and significant variables in the multivariate model were investigated.
Statistically significant variables in univariate analyses between these categories were entered into multivariate analysis using a logistic regression model.
Pearson correlations between significant independent variables in the most predictive models were also examined.
In this paper, the causal relationships between the statistically significant variables and outcomes in terms of vulnerability levels are explained using existing literature.
There was no effect modification between any of the significant variables and co-morbidity in any model.
There were no significant associations between these variables in any of the separate groups.
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